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Journal articles on the topic 'Histopathological slides'
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Dr., Indra Kanta Maitra, and Samir Kand Bandyopadhyay Prof. "Approach Towards Analysis of Biopsy Slide of Breast Cancer." International Journal of Trend in Scientific Research and Development 2, no. 6 (2018): 855–61. https://doi.org/10.31142/ijtsrd18734.
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Pathologists placed tissue slices on glass slides use appropriate stains and examine them through a microscope. The proposed method uses insignificant portions of the slide images by colour polarization as a pre processing step. The simplicity of algorithm leads to less computational time and thus suitable tool to assist experts for automated real time breast cancer diagnosis. Dr. Indra Kanta Maitra | Prof. Samir Kand Bandyopadhyay "Approach Towards Analysis of Biopsy Slide of Breast Cancer" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-6 , October 2018, URL: https://www.ijtsrd.com/papers/ijtsrd18734.pdf
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Hardi, Zon, Wiwin Wiryanti, Adang Durachim, and Mamat Rahmat. "The effect of reusing formaldehyde fixative solution on the quality of histopathological slides and the amount of waste produced." Current Biomedicine 2, no. 2 (2024): 71–83. http://dx.doi.org/10.29244/currbiomed.2.2.71-83.
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Background: Neutral buffered formalin (NBF) 10% fixative solution is widely used in histopathological slides. The fixation process generates liquid waste of NBF 10% and solid waste of tissue remnants. Objective: The research aimed to assess the reuse of NBF 10% fixative solution on the quality of histopathological slides and calculate the amount of waste produced. Methods: Treatments included single-use of fixative solution (control), reuse for 1, 2, and 3 times. Ten sample slides were prepared for each treatment, consisting of intestinal tissue, uterine fibroids, prostate, uterus, ovarian cyst, portio vaginalis cervicis, thyroid, rectum, breast fibroadenoma, and gallbladder tissues. Tissues were fixed with NBF 10% and processed histologically with hematoxylin-eosin staining. Liquid waste of NBF 10% and solid waste of tissue remnants were quantified. Histopathological slide quality was measured under a microscope for nuclear and cytoplasmic clarity, staining intensity, and color uniformity. Results: Control slides exhibited good quality with clearly blue-stained nuclei, pink cytoplasm, no color accumulation, and uniform staining across fields of view. Reused NBF 10% slides experienced a decrease in quality compared to the control but were still usable for diagnosis. Slides reused 2 and 3 times showed poor quality, making diagnosis difficult. Fixation resulted in 299.0 liters of liquid waste of NBF 10% and 64.9 kilograms of solid tissue remnants. Conclusion: Reusing NBF 10% decreases histological slide quality, though reuse once still allows for diagnosis. Reusing 10% NBF for tissue fixation can reduce the liquid waste of fixative solution and solid tissue waste.
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Ottoman, Oscar, Shaban Urassa, Edrick Elias, Jeffer Bhuko, and Aron O. Isay. "Histopathological Evaluation of the Microtomy Artifacts on Haematoxylin and Eosin Section; Hospital Based Cross-Sectional Study." East African Journal of Health and Science 5, no. 1 (2022): 311–18. http://dx.doi.org/10.37284/eajhs.5.1.848.
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Background information: Microtomy artifacts are abnormal structures or features in histological slides resulting from tissue sectioning by microtome. Objective: To determine the type and prevalence of microtomy artifacts found in histopathological tissue sections slides at Bugando Medical Centre (BMC). Methodology: This was a cross-sectional observational study that involved 547 consecutive hematoxylins and eosin (H&E) stained sections of histological archived tissue slides of January 2021. The slides were retrieved from the archives of the histopathology laboratory at BMC, Mwanza Tanzania and analysed for artifacts under a light microscope. Results: A total number of 547 histopathological slides were retrieved for the study and 412 (75.3%) slides had microtomy artifacts present while the remaining 135 (24.7%) histopathological slides had no microtomy artifacts. Of 412 slides with microtomy artifacts, 204(49.5%) slides had only one type of microtomy artifacts while the remaining 208 (50.5%) slides had more than one type of microtomy artifacts. There was a total of 672 microtomy artifacts, and the majority 576 (85.7%) were due to section cutting, followed by trimming artifacts in 92 (13.69%) of the slides. The least artifact was floatation which was seen in 4 (0.6%) of the slides. For the floatation artifact, the folding artifact was the most commonly seen in 300(54.8%) of the slides. Conclusion: Higher prevalence of microtomy artifacts at BMC reflects the problem of interpretation of histopathological slides in our setting. Section folding artifacts were the most prevalent pattern of artifact observed in this study.
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Poudel, Pratibha, and Bhoj Raj Adhikari. "Analysis of Histopathological Artifacts in Oral Biopsy Specimen: A Descriptive Cross Sectional Study." Journal of College of Medical Sciences-Nepal 19, no. 1 (2023): 44–49. http://dx.doi.org/10.3126/jcmsn.v19i1.44310.
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Introduction Arriving at the final diagnosis requires the histopathological examination of the biopsied lesion. Many a times, diagnosis of lesion may be hindered due to presence of artifacts in the slide. Having a thorough knowledge of these artifacts help to take the precautionary measures to avoid their occurrence. This study is an attempt to analyze histopathological slides from Department of Oral Pathology to identify the artifacts seen in oral biopsy specimens. Methods This cross sectional study was conducted in Department of Oral Pathology, Dhulikhel Hospital from July 2021 to February 2022. Slides of all the biopsies during the study period were included in the study. The artifacts were divided into three groups: Artifacts related to surgeons performance, artifacts related to technicians performance and artifacts caused during transfer of sample to the laboratory. Then, the frequency distribution for each type of artifact was calculated. Results A total of 280 slides were included in the present study. Artifacts related to technicians performance were seen in 89.3% slides whereas artifacts related to surgeons performance were seen in 76.4% slides. None of the slides showed artifacts related to transfer of sample to the laboratory. The most common artifact seen was eosin leaching (63.6%) followed by stain deposit (60%) and folds and wrinkles (40.7%). Conclusions The findings of our study showed that various types of artifacts may be incorporated in biopsy specimen that create difficulty in diagnosing the lesion properly. Proper biopsy protocol and careful handling of sample to prevent technical errors may be helpful to reduce the frequency of artifacts.
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Muller, B. G., A. Swaan, D. M. de Bruin, et al. "Customized Tool for the Validation of Optical Coherence Tomography in Differentiation of Prostate Cancer." Technology in Cancer Research & Treatment 16, no. 1 (2016): 57–65. http://dx.doi.org/10.1177/1533034615626614.
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Objective: To design and demonstrate a customized tool to generate histologic sections of the prostate that directly correlate with needle-based optical coherence tomography pullback measurements. Materials and Methods: A customized tool was created to hold the prostatectomy specimens during optical coherence tomography measurements and formalin fixation. Using the tool, the prostate could be sliced into slices of 4 mm thickness through the optical coherence tomography measurement trajectory. In this way, whole-mount pathology slides were produced in exactly the same location as the optical coherence tomography measurements were performed. Full 3-dimensional optical coherence tomography pullbacks were fused with the histopathology slides using the 3-dimensional imaging software AMIRA, and images were compared. Results: A radical prostatectomy was performed in a patient (age: 68 years, prostate-specific antigen: 6.0 ng/mL) with Gleason score 3 + 4 = 7 in 2/5 biopsy cores on the left side (15%) and Gleason score 3 + 4 = 7 in 1/5 biopsy cores on the right side (5%). Histopathology after radical prostatectomy showed an anterior located pT2cNx adenocarcinoma (Gleason score 3 + 4 = 7). Histopathological prostate slides were produced using the customized tool for optical coherence tomography measurements, fixation, and slicing of the prostate specimens. These slides correlated exactly with the optical coherence tomography images. Various structures, for example, Gleason 3 + 4 prostate cancer, stroma, healthy glands, and cystic atrophy with septae, could be identified both on optical coherence tomography and on the histopathological prostate slides. Conclusion: We successfully designed and applied a customized tool to process radical prostatectomy specimens to improve the coregistration of whole mount histology sections to fresh tissue optical coherence tomography pullback measurements. This technique will be crucial in validating the results of optical coherence tomography imaging studies with histology and can easily be applied in other solid tissues as well, for example, lung, kidney, breast, and liver. This will help improve the efficacy of optical coherence tomography in cancer detection and staging in solid organs.
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P, Sindu, and Arthanari A. "Efficacy of Aloe Vera gel and Egg albumin as slide coating adhesives- A Comparative Pilot study." International Journal of Clinicopathological Correlation 6, no. 2 (2022): 17–20. http://dx.doi.org/10.56501/intjclinicopatholcorrel.v6i2.673.
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Introduction
 The problem with the staining process is the tendency for tissue section to detach from glass slides during the heat treatment used in the further processing of the slides. This has been minimised by the use of slide adhesives. The most commonly used slide adhesives for daily use are gelatine and egg albumin.
 Aim
 To compare the efficacy of Aloe Vera gel as slide coating adhesives with egg albumin.
 Methods
 15 formalin fixed paraffin embedded tissue blocks of known histopathological diagnosis were sectioned from the archives of the Department of Oral and Maxillofacial Pathology for the study, with n= 15 for Aloe Vera gel and n= 15 for egg albumin coating. The Aloe Vera gel was prepared and the slides were coated by the same and the routine staining procedure was done. The slides were viewed under the microscope by two independent blinded observers and grading was done for ease of handling, viscosity, ability to withstand heat and chemicals and adhesion.
 Results
 Our study showed that Aloe Vera gel when used as a slide adhesive was found have more advantage than routine albumin slide coating with a p <0.05 value for ease of handling and absence of background staining.
 Conclusion
 Aloe Vera gel was found to be superior to routinely used egg albumin as a slide coating agent.
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Jaarsma, Thomas, Halszka Jarodzka, Marius Nap, Jeroen J. G. van Merrienboer, and Henny P. A. Boshuizen. "Expertise under the microscope: processing histopathological slides." Medical Education 48, no. 3 (2014): 292–300. http://dx.doi.org/10.1111/medu.12385.
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Kinar, Yaron, Gal Dinstag, Omer Tirosh, et al. "Abstract 729: Applying novel transcriptomics-based markers to predict response to durvalumab and olaparib in mTNBC from histopathological slides." Cancer Research 85, no. 8_Supplement_1 (2025): 729. https://doi.org/10.1158/1538-7445.am2025-729.
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Abstract Background: Transcriptomics-based predictors show great promise in identifying responders to immune checkpoint inhibitors (ICI) who are rejected by standard biomarkers like PD-L1. However, the reliance on gene-expression data makes these biomarkers less applicable in the clinic. On the other hand, H&E-stained slides are commonly available, so predicting response from slide scans could greatly improve accessibility. We have previously described DeepPT - a deep learning model to infer gene expression from H&E slides. Such a model could bridge that gap and allow clinicians to use transcriptomics-based biomarkers directly with H&E slides. Methods: We tested two transcriptomics-based biomarkers of response to PD-1/PD-L1 inhibitors on a dataset of 21 mTNBC patients treated with olaparib and durvalumab. The first biomarker was derived using our published ENLIGHT platform, and the second is an XGBoost model on a curated set of 839 genes associated with ICI response, trained on 24 datasets with measured gene-expression and response data. We blindly applied the two models to the pre-treated tumor RNAseq data of the patient tumors, as well as to expression profiles inferred from their H&E slides using DeepPT. We also examined whether combining the scores from multiple slides per sample improves prediction. We then unblinded the outcomes and analyzed the model's ability to predict objective response (RECIST 1.1). Results: Both models showed high predictivity in distinguishing responders (CR/PR) from non responders (SD/PD) when applied to RNAseq gene-expression profiles: AUC of 0.73 for the ENLIGHT model and 0.85 for the XGB model (Odds ratio for classification of 2.1 and 5.6, respectively). The performance on the gene-expression profiles that were inferred from a single H&E slide was also high - AUC of 0.82 for ENLIGHT and 0.83 for XGB (OR of 44 and 12). The performance of the XGB model improves when we average over all slides that correspond to a single patient (AUC of 0.85, OR of 16.7) while we observe no such improvement for ENLIGHT (AUC of 0.78, OR of 6.7). Interestingly, both models also distinguish stable from progressing disease within the nonresponders group. Conclusion: We introduce a method to tackle the challenge of applying gene-expression based biomarkers for response to treatment by PD1/PDL1 inhibitors in the clinic. We do this by combining transcriptomics-based models and a deep-learning model for predicting gene-expression from H&E slides. We demonstrate that this method correctly predicts response on an external dataset, favorably comparing with direct application of the models to RNAseq data. This approach may be extended to additional drugs where transcriptomics-based biomarkers exist, and can be further improved by taking into consideration the differential predictivity of genes from H&E slides. Citation Format: Yaron Kinar, Gal Dinstag, Omer Tirosh, Doreen S. Ben-Zvi, SMMART Clinical Trials Program, Tuvik Beker, Aharonov Ranit, Gordon B. Mills. Applying novel transcriptomics-based markers to predict response to durvalumab and olaparib in mTNBC from histopathological slides [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 729.
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Zakaria, Farhana, Taqiya Nuzhath, and Ayisha Begum. "HISTOPATHOLOGICAL STUDY OF ENDOMETRIALBIOPSIES IN ABNORMAL UTERINE BLEEDING." International Journal of Advanced Research 10, no. 02 (2022): 258–62. http://dx.doi.org/10.21474/ijar01/14202.
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Introduction: Excessive and painful bleeding from uterus is a common cause for distress among menstruating women, perimenopausal as well as post-menopausal. There are innumerable causes for abnormal bleeding from uterus. We carried out a retrospective study to enumerate the causes of abnormal uterine bleeding. Materials And Methods: Consecutive 200 slides of endometrial biopsies done for abnormal uterine bleeding were evaluated over a period of two years between June 2019 to May 2021. The slides were stained with haematoxylin and eosin stain. All the slides were evaluated, examined and reported by two experienced pathologists. The results were tabulated depending upon the age and diagnosis. Results: Commonest age group noted for abnormal uterine bleeding was between 41-50yrs. Disordered proliferative phase was the commonest (16%) functional cause of abnormal bleeding and diagnosis. The most common organic cause of abnormal bleeding was simple endometrial hyperplasia without atypia (28%). Endometrial carcinoma was diagnosed in 2 cases above 50yrs. Conclusion: Simple endometrial hyperplasiawas the most common cause of abnormal bleeding from uterus in our series. Atypical hyperplasia or endometrial carcinoma should be ruled out in post-menopausal age group.
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Kriegsmann, Mark, Katharina Kriegsmann, Georg Steinbuss, Christiane Zgorzelski, Anne Kraft, and Matthias M. Gaida. "Deep Learning in Pancreatic Tissue: Identification of Anatomical Structures, Pancreatic Intraepithelial Neoplasia, and Ductal Adenocarcinoma." International Journal of Molecular Sciences 22, no. 10 (2021): 5385. http://dx.doi.org/10.3390/ijms22105385.
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Identification of pancreatic ductal adenocarcinoma (PDAC) and precursor lesions in histological tissue slides can be challenging and elaborate, especially due to tumor heterogeneity. Thus, supportive tools for the identification of anatomical and pathological tissue structures are desired. Deep learning methods recently emerged, which classify histological structures into image categories with high accuracy. However, to date, only a limited number of classes and patients have been included in histopathological studies. In this study, scanned histopathological tissue slides from tissue microarrays of PDAC patients (n = 201, image patches n = 81.165) were extracted and assigned to a training, validation, and test set. With these patches, we implemented a convolutional neuronal network, established quality control measures and a method to interpret the model, and implemented a workflow for whole tissue slides. An optimized EfficientNet algorithm achieved high accuracies that allowed automatically localizing and quantifying tissue categories including pancreatic intraepithelial neoplasia and PDAC in whole tissue slides. SmoothGrad heatmaps allowed explaining image classification results. This is the first study that utilizes deep learning for automatic identification of different anatomical tissue structures and diseases on histopathological images of pancreatic tissue specimens. The proposed approach is a valuable tool to support routine diagnostic review and pancreatic cancer research.
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Murchan, Pierre, Cathal Ó’Brien, Shane O’Connell, et al. "Deep Learning of Histopathological Features for the Prediction of Tumour Molecular Genetics." Diagnostics 11, no. 8 (2021): 1406. http://dx.doi.org/10.3390/diagnostics11081406.
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Advanced diagnostics are enabling cancer treatments to become increasingly tailored to the individual through developments in immunotherapies and targeted therapies. However, long turnaround times and high costs of molecular testing hinder the widespread implementation of targeted cancer treatments. Meanwhile, gold-standard histopathological assessment carried out by a trained pathologist is widely regarded as routine and mandatory in most cancers. Recently, methods have been developed to mine hidden information from histopathological slides using deep learning applied to scanned and digitized slides; deep learning comprises a collection of computational methods which learn patterns in data in order to make predictions. Such methods have been reported to be successful in a variety of cancers for predicting the presence of biomarkers such as driver mutations, tumour mutational burden, and microsatellite instability. This information could prove valuable to pathologists and oncologists in clinical decision making for cancer treatment and triage for in-depth sequencing. In addition to identifying molecular features, deep learning has been applied to predict prognosis and treatment response in certain cancers. Despite reported successes, many challenges remain before the clinical implementation of such diagnostic strategies in the clinical setting is possible. This review aims to outline recent developments in the field of deep learning for predicting molecular genetics from histopathological slides, as well as to highlight limitations and pitfalls of working with histopathology slides in deep learning.
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Vishnu, Vardhan, and R. Martha. "R415 – Recurrence of Nasopharyngeal Angiofibroma (NPF) Study." Otolaryngology–Head and Neck Surgery 139, no. 2_suppl (2008): P182. http://dx.doi.org/10.1016/j.otohns.2008.05.569.
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Problem Study of correlation of recurrence of NPF with its histopathologic appearance. Methods A retrospective and prospective study of 184 NPF cases identified, treated in Government Ear Nose Throat Hospital, and followed over a period of 26 years (1981–2006) and their histopathological slides are preserved, their recurrence rate is documented. The histopathology of NPF is varied - composed of vascular and fibrous components and their proportion varied. In our study we will undertake the review of histopathology slides that are already preserved and would also like to study the new recurrent cases in the coming 6 months. Results All recurrences were observed with in one year of of treatment and the recurrence rate was 19.66%. Recurrence had no correlation with age of patient, duration of symptoms, or surgical approach but correlated with stage of tumor at presentation (p less than 0.05). Preoperative embolisation was done in 16.85% cases and did not show any statistical difference in recurrence rate between the embolised and non embolised. Conclusion The correlation between the histopathological appearance and the recurrence rate will help in choosing the right approach for surgery and also postoperative follow-up to detect the recurrence at the earliest. Significance Histopathological detail can help in knowing the prognosis in terms of chances of recurrence and also guide for follow up at close intervals and advocate the appropriate treatment at the earliest.
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Popa, Liliana. "A statistical framework for evaluating convolutional neural networks. Application to colon cancer." Annals of the University of Craiova - Mathematics and Computer Science Series 48, no. 1 (2021): 159–66. http://dx.doi.org/10.52846/ami.v48i1.1449.
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"Purpose: Explore the efficiency of two convolutional neural networks in helping physicians in establishing colon cancer diagnosis from histopathological image scans. Methods: The dataset used in this study contains 357 histopathological image slides that ranged from benign cases to colon cancer grade three. The slides were collected by doctors at the Emergency Hospital of Craiova, Romania. The study proposes a statistical framework that studies the performances of two convolutional neural networks AlexNet and GoogleNet. Results: AlexNet has revealed a competitive accuracy in comparison with GoogleNet. To prove the robustness of the AlexNet in fair terms, we have performed a thorough statistical analysis of its performance. Conclusions: On this particular dataset which contains histopathological image scans regarding colon cancer, the convolutional neural network AlexNet proved to be superior to GoogleNet. "
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Mehrotra, Saket. "Incidence of histopathological variants of oral squamous cell carcinoma: An institutional study." Journal of Oral Medicine, Oral Surgery, Oral Pathology and Oral Radiology 7, no. 4 (2022): 226–29. http://dx.doi.org/10.18231/j.jooo.2021.060.
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Squamous cell carcinoma is the most important and the most common malignant mucosal neoplasm of the head and neck accounting for over 90% of all malignancies. Conventional oral Squamous cell carcinoma is frequently present in general cancerous conditions. It is bundled up with six different variants. Histomorphologically every variant shows a unique appearance. This raises an opportunity for the different diagnostic consideration with the precise management decision.All cases of OSCC reported at our institution Dentopath Pathologies Amravati in past two months were scrutinized. Representative sections containing the full thickness of the tumor were used for histopathological grading. The structure and identification of carcinomas were done microscopically by two expert dentopathologist.In the present study, we screened 100 slides of a conventional epithelial cell carcinoma. Amonst which 30 Slides showed the verrucous carcinoma. On 5 slides adenoid squamous cell carcinoma were observed. Incidence of Papillary squamous cell carcinoma and basaloid squamous cell carcinoma was only 1 out of 100 slides each. Whereas, the spindle cell/sarcomatoid carcinoma was observed on 2 slides. Adenosquamous carcinoma is the rarest variant and hence no incidence of this carcinoma were observed in our study. The behavior of the OSCC varies amongst due to the presence of different morphological type of tumor. A few studies on OSCC malignancy grading with different clinical parameters were made. In the present study different types of variants are seen according to their histopathological appearances.Histopathological knowledge is very important for the precise diagnosis. Squamous cell carcinoma is the most common neoplasm of oral cavity. However, variants of the same show very less frequency. Hence, it became challenge for the appropriate diagnosis as many times a misdiagnosis affects the course of treatment of the patient
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Hansen, H. J., and P. O. Nilsson. "A German-Swedish Collection of Histopathological Slides from 1893." Acta Veterinaria Scandinavica 38, no. 1 (1997): 97–107. http://dx.doi.org/10.1186/bf03548512.
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Saravani, Shirin, Hamideh Kadeh, Maryam Shahsavari, Mahnaz Shahrakipour, and Bahare Mosafer. "Evaluation of Artifacts in Oral and Maxillofacial Histopathological Slides." Journal of Dentomaxillofacial Radiology, Pathology and Surgery 5, no. 3 (2016): 11–16. http://dx.doi.org/10.18869/acadpub.3dj.5.3.11.
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Khacheishvili, Mariam, Alexi Baidoshvili, and Christina Hulsbergen-Van De Kaa. "Improvement of the Quality of Prostate Cancer Diagnosis Using AI In Digital Pathology Short Heading: Quality Improvement by Using AI In Pathology." Journal of Clinical and Medical Images 08, no. 07 (2024): 01–06. https://doi.org/10.47829/jcmi.2024.8702.
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Automated AI-based tools are becoming increasingly important in modern medicine, including pathology, bysignificantly supporting pathologists’ diagnoses and reducing human biases. Histopathological evaluation of prostate biopsies plays a crucial role in diagnosing prostate cancer. Pathologists assess tumor type, grade (Gleason Grade), and tumor extension to determine the management plan. Diagnosis accuracy, particularly in tumor grading, can be affected by inter- and intraobserver variability among pathologists. Due to the increased incidence of prostate cancer and subsequent workload on pathologists, an AI-based tool like Ibex Prostate, can potentially reduce pathologists’ workflow and enhance diagnostic accuracy.3 This study aimed to retrospectively compare histologically diagnosed prostate cancer by pathologists to the AIbased algorithm, Ibex Prostate. The study evaluates the algorithm’simpact on laboratory workflowand diagnostic accuracy. Methods: The study was conducted at the Laboratory of Pathology East Netherlands (Lab PON, Hengelo, The Netherlands), using hematoxylin and eosin-stained (H&E) Whole Slide Images (WSI) from 2021. A total of 169 randomly selected and de-identified prostate biopsy cases, consisting of 809 slides and 701 parts, were used. Slides were digitized using a Philips Ultrafast Scanner (UFS). Of these, 674 parts from 168 cases were used for the study, while 33 slides were excluded: 16 slides lacked a definitive diagnosis from the original report, and 17 slides were out-of-focus. According to pathologists’ diagnoses, 391 parts (58%) were benign, and 283 (42%) contained carcinoma. Ibex Prostate, a validated and CE-marked AI tool developed using advanced machine learning techniques, particularly convolutional neural networks (CNNs), assessed slide-level scores for cancer probability, Gleason grading, and perineural invasion. The algorithm’s performance was evaluated using the area under the receiver operating characteristic curve (AUC).
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Fuster, Saul, Umay Kiraz, Trygve Eftestøl, Emiel A. M. Janssen, and Kjersti Engan. "NMGrad: Advancing Histopathological Bladder Cancer Grading with Weakly Supervised Deep Learning." Bioengineering 11, no. 9 (2024): 909. http://dx.doi.org/10.3390/bioengineering11090909.
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The most prevalent form of bladder cancer is urothelial carcinoma, characterized by a high recurrence rate and substantial lifetime treatment costs for patients. Grading is a prime factor for patient risk stratification, although it suffers from inconsistencies and variations among pathologists. Moreover, absence of annotations in medical imaging renders it difficult to train deep learning models. To address these challenges, we introduce a pipeline designed for bladder cancer grading using histological slides. First, it extracts urothelium tissue tiles at different magnification levels, employing a convolutional neural network for processing for feature extraction. Then, it engages in the slide-level prediction process. It employs a nested multiple-instance learning approach with attention to predict the grade. To distinguish different levels of malignancy within specific regions of the slide, we include the origins of the tiles in our analysis. The attention scores at region level are shown to correlate with verified high-grade regions, giving some explainability to the model. Clinical evaluations demonstrate that our model consistently outperforms previous state-of-the-art methods, achieving an F1 score of 0.85.
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Bhavya, Singh, Ranjan Amit, and Kumar Deepak. "A Correlational Study on FNAC and Histopathology for the Diagnosis of Breast Lump." International Journal of Pharmaceutical and Clinical Research 15, no. 1 (2023): 1218–23. https://doi.org/10.5281/zenodo.13162863.
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<strong>Objectives: </strong>This present study was to correlate the diagnostic accuracy of FNAC and histopathology for the diagnosis of breast lump. <strong>Methods: </strong>A total of 50 patients with palpable breast lumps attending OPD of surgery department were enrolled in this study. FNAC was performed using 23 gauge needle after history and clinical examination of the patient. Aspirated material was expressed to glass slide and slide was immersed in fixator of 95% methyl alcohol. Slides were stained with Hematoxylin-Eosin and Leishman`s stain. According to findings, FNAC study was categorized into benign and malignant lesions. <strong>Results: </strong>Majorities of patients 29(58%) were in age group of 31-40 years. Out of 50 cases of breast lump, benign lesion was seen in 35(70%) cases and malignant lesion was seen in 15(30%) cases. <strong>Conclusions: </strong>FNAC is a reliable, safe, inexpensive, little discomfort, fast and time saving diagnostic method for the assessment of breast lumps with high degree of accuracy as compared to histopathological diagnosis. It should be used as preliminary investigation for the diagnosis of breast lump in outdoor patient department.
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P, Sneha, and Kavitha Yevoor. "Study of Histopathological Changes in Fibroadenoma of the Breast." Annals of Pathology and Laboratory Medicine 10, no. 2 (2023): A13–18. http://dx.doi.org/10.21276/apalm.3194.
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Background: Fibroadenoma of the breast is a relatively frequently occurring tumor. Although often considered a benign tumour, several reports describe a higher risk of subsequent breast carcinoma in patients diagnosed with fibroadenoma. Increased risk depends on presence of complex changes within fibroadenoma, presence of hyperplasia and positive family history for breast cancer.
 Aims and Objectives: Our main aim was to study the histological variations within the fibroadenoma of the breast and also to identify those lesions with the possible risk of malignancy.
 Methods: Descriptive study of three years. A total of 250 cases of fibroadenoma were studied. Slides were stained with Hematoxylin and Eosin (H & E) and were thoroughly reviewed. Slides were screened for proliferative epithelial changes, fibrocystic epithelial changes, stromal changes and various other changes such as foci of tubular adenoma and phyllodes tumour. Slides with invasive malignancies were excluded from the study.
 Result: Apocrine change among fibrocystic changes was the commonest variation within the fibroadenoma. Complex fibroadenoma, moderate and atypical ductal hyperplasia was seen in older age groups.
 Conclusion: Increased risk of breast cancer is seen patients with presence of hyperplasia and complex fibroadenoma of the breast. So exclusive study of histopathological changes in epithelial and stromal elements of fibroadenoma is required and are essential to be reported so as to alert the clinician for follow up of the patient. This will help in timely management to reduce morbidity and mortality.
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Hao, Jie, Jose Agraz, Caleb Grenko, et al. "NIMG-09. PREDICTING OVERALL SURVIVAL OF GLIOBLASTOMA PATIENTS ON MULTI-INSTITUTIONAL HISTOPATHOLOGY STAINED SLIDES USING DEEP LEARNING AND POPULATION-BASED NORMALIZATION." Neuro-Oncology 22, Supplement_2 (2020): ii148. http://dx.doi.org/10.1093/neuonc/noaa215.622.
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Abstract MOTIVATION Glioblastoma is the most common and aggressive adult brain tumor. Clinical histopathologic evaluation is essential for tumor classification, which according to the World Health Organization is associated with prognostic information. Accurate prediction of patient overall survival (OS) from clinical routine baseline histopathology whole slide images (WSI) using advanced computational methods, while considering variations in the staining process, could contribute to clinical decision-making and patient management optimization. METHODS We utilize The Cancer Genome Atlas glioblastoma (TCGA-GBM) collection, comprising multi-institutional hematoxylin and eosin (H&E) stained frozen top-section WSI, genomic, and clinical data from 121 subjects. Data are randomly split into training (80%), validation (10%), and testing (10%) sets, while proportionally keeping the ratio of censored patients. We propose a novel deep learning algorithm to identify survival-discriminative histopathological patterns in a WSI, through feature maps, and quantitatively integrate them with gene expression and clinical data to predict patient OS. The concordance index (C-index) is used to quantify the predictive OS performance. Variations in slide staining are assessed through a novel population-based stain normalization approach, informed of glioblastoma distinct histologic sub-regions and their appearance from 509 H&E stained slides with corresponding anatomical annotations from the Ivy Glioblastoma Atlas Project (IvyGAP). RESULTS C-index was equal to 0.797, 0.713, and 0.703 for the training, validation, and testing data, respectively, prior to stain normalization. Following normalization, staining variations in H&E and ‘E’ gained significant improvements in IvyGAP (pWilcoxon&lt; 0.01) and TCGA-GBM (pWilcoxon&lt; 0.0001) data, respectively. These improvements contributed to further optimizing the C-index to 0.871, 0.777, and 0.780 for the training, validation, and testing data, respectively. CONCLUSIONS Appropriate normalization and integrative deep learning yield accurate OS prediction of glioblastoma patients through H&E slides, generalizable in multi-institutional data, potentially contributing to patient stratification in clinical trials. Our computationally-identified survival-discriminative histopathological patterns can contribute in further understanding glioblastoma.
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Maktabi, Marianne, Hannes Köhler, Claire Chalopin, et al. "Semi-automatic decision-making process in histopathological specimens from Barrett’s carcinoma patients using hyperspectral imaging (HSI)." Current Directions in Biomedical Engineering 6, no. 3 (2020): 261–63. http://dx.doi.org/10.1515/cdbme-2020-3066.
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AbstractDiscrimination of malignant and non-malignant cells of histopathologic specimens is a key step in cancer diagnostics. Hyperspectral Imaging (HSI) allows the acquisition of spectra in the visual and near-infrared range (500-1000nm). HSI can support the identification and classification of cancer cells using machine learning algorithms. In this work, we tested four classification methods on histopathological slides of esophageal adenocarcinoma. The best results were achieved with a Multi-Layer Perceptron. Sensitivity and F1-Score values of 90% were obtained.
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Nair, Vedha R., Palati Sinduja, and Priyadharshini R. "Innovative Brain Matrix Device-Hypomatrix for Histopathological Studies of Rat Hippocampus." UTTAR PRADESH JOURNAL OF ZOOLOGY 44, no. 22 (2023): 229–35. http://dx.doi.org/10.56557/upjoz/2023/v44i223737.
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Introduction: Rodents, such as rats, are frequently employed in neuroscience studies. Exact knowledge of the neuroanatomy and recognition of brain regions, as well as their interactions to nearby tissues, are required for precise technique and experiment success. In most studies, the hypothalamus of the rat brain is widely used to study many diseases. Brain sectioning is frequently the first stage in the dissection of certain brain structures. The aim of our study was to produce an innovative hypo matrix that would help compensate for the adversities that are caused by manual conventional grossing.
 Materials and Method: For the study 25 rat brain samples were obtained. 20 samples were immersed in formalin overnight and were grossed manually using conventional rat brain grossing method using blades and forceps. 5 rat brain samples were sliced using the hypomatrix which is a rectangular box with a lid containing blades that slice through the brain.
 Results: The slides viewed under the microscope showed that the slices of hypothalamus obtained from the samples grossed using the hypomatrix presented a clear picture with least distortion and tissue damages.
 Conclusion: The hypomatrix developed has shown better user handling, ease of use, and was better in terms of accuracy and efficacy. It will be helpful in reducing the time needed for the digestion and grossing of rat brain samples using conventional hand grossing hence showing improved time management.
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Kempula, Geetha Mala, Krishna Divyansh, Joshitha Velidandla, and M. Haravi Rekha. "Gamut of Histopathological Findings in Autopsy Specimens: Our Hospital Experience." International Journal of Current Pharmaceutical Review and Research 17, no. 4 (2025): 679–84. https://doi.org/10.5281/zenodo.15305046.
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correlating the cause and manner of death, and allows detailed study of a wide range of conditions, including infections, inflammations, tumors, and infrequent or unnoticed lesions. Recent advances in diagnostic medical technology have increased the use of non-invasive modalities, histopathological examination of autopsies remains the gold standard for obtaining direct morphological and histological diagnoses, and this study was therefore undertaken to investigate the gamut of histopathological findings in autopsy specimens at our institution Objectives: The primary objective was to determine the range of histopathological lesions and identify any incidental findings. Materials and Methods: This retrospective study included 1146 autopsy organs of which 760 organs showed significant changes archived over six years, from 2016 to 2021.Following ethical clearance, autopsy specimens sent for histopathological examination and diagnosis, irrespective of cause of death, were retrieved from the records. Hematoxylin and eosin-stained slides were retrieved and studied for histopathological details; inclusion criteria were all archived histopathological examination slides and records of autopsy specimens, and exclusion criteria were any records or slides that were not archived due to unavailability or loss; all data collected were analyzed using descriptive statistics. Results: The study reveals that the heart was the most frequently examined organ (42.14%), with atherosclerosis being the most common pathology (25.98%). Incidental findings such as myxoma of the heart, fungal abscess and SCC metastatic deposits of the lung were also noted. Conclusion: The findings underscore the continued importance of autopsy histopathology in elucidating disease processes and identifying unexpected pathologies, which have significant implications for understanding disease mechanisms and improving clinical practice.
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Lee, Joong, and Junghye Lee. "A Study of Mycobacterium tuberculosis Detection Using Different Neural Networks in Autopsy Specimens." Diagnostics 13, no. 13 (2023): 2230. http://dx.doi.org/10.3390/diagnostics13132230.
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Tuberculosis (TB) presents a substantial health risk to autopsy staff, given its three to five times higher incidence of TB compared to clinical staff. This risk is notably accentuated in South Korea, which reported the highest TB incidence rate and the third highest TB mortality rate among OECD member countries in 2020. The standard TB diagnostic method, histopathological examination of sputum or tissue for acid-fast bacilli (AFB) using Ziehl–Neelsen staining, demands microscopic examination of slides at 1000× magnification, which is labor-intensive and time-consuming. This article proposes a computer-aided diagnosis (CAD) system designed to enhance the efficiency of TB diagnosis at magnification less than 1000×. By training nine neural networks with images taken from 30 training slides and 10 evaluation slides at 400× magnification, we evaluated their ability to detect M. tuberculosis. The N model achieved the highest accuracy, with 99.77% per patch and 90% per slide. We discovered that the model could aid pathologists in preliminary TB screening, thereby reducing diagnostic time. We anticipate that this research will contribute to minimizing autopsy staff’s infection risk and rapidly determining the cause of death.
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van der Kamp, Ananda, Thomas de Bel, Ludo van Alst, et al. "Automated Deep Learning-Based Classification of Wilms Tumor Histopathology." Cancers 15, no. 9 (2023): 2656. http://dx.doi.org/10.3390/cancers15092656.
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(1) Background: Histopathological assessment of Wilms tumors (WT) is crucial for risk group classification to guide postoperative stratification in chemotherapy pre-treated WT cases. However, due to the heterogeneous nature of the tumor, significant interobserver variation between pathologists in WT diagnosis has been observed, potentially leading to misclassification and suboptimal treatment. We investigated whether artificial intelligence (AI) can contribute to accurate and reproducible histopathological assessment of WT through recognition of individual histopathological tumor components. (2) Methods: We assessed the performance of a deep learning-based AI system in quantifying WT components in hematoxylin and eosin-stained slides by calculating the Sørensen–Dice coefficient for fifteen predefined renal tissue components, including six tumor-related components. We trained the AI system using multiclass annotations from 72 whole-slide images of patients diagnosed with WT. (3) Results: The overall Dice coefficient for all fifteen tissue components was 0.85 and for the six tumor-related components was 0.79. Tumor segmentation worked best to reliably identify necrosis (Dice coefficient 0.98) and blastema (Dice coefficient 0.82). (4) Conclusions: Accurate histopathological classification of WT may be feasible using a digital pathology-based AI system in a national cohort of WT patients.
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Bhat, Salma, Ambreen Beigh, and Summyia Farooq. "Histopathological study of endometrial stromal sarcomas." International Journal of Reproduction, Contraception, Obstetrics and Gynecology 7, no. 12 (2018): 4891. http://dx.doi.org/10.18203/2320-1770.ijrcog20184935.
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Background: Endometrial stromal sarcomas (ESSs) are rare malignant uterine tumours comparatively affecting younger women and the mean age is 42 to 58 years. The World Health Organization (WHO) classification categorises endometrial stromal neoplasms and related tumors as: endometrial stromal nodule (ESN), low-grade endometrial stromal sarcoma (LG-ESS), high-grade endometrial stromal sarcoma (HG-ESS), and undifferentiated uterine sarcoma (UUS).Methods: Present study is a retrospective one and includes 6 patients with histologically proven endometrial stromal sarcoma for a period of 3 years. Authors examined every slide available from each case and new HE-stained slides generated from formaline-fixed, paraffin-embedded tissue were reviewed to confirm the diagnoses. Demographic information, pathologic, and treatment information were collected from the clinic and hospital charts. All had primary surgical management in the form of total abdominal hysterectomy and salpingo-oophorectomy.Results: The mean patient age was 41 years. All of the patients had presented with abnormal uterine bleeding. Diffuse growth of small cells closely resembling those of the normal proliferative endometrial stroma was the characteristic feature of these tumors. All of these patients had a low grade ESS on histopathology. They had regular follow-up visits until the end of study.Conclusions: Endometrial stromal sarcomas are rare malignant tumors of the uterus and a proper preoperative diagnosis is difficult. Their differential diagnosis from typical submucosal uterine myomas or benign endometrial polyps can be difficult. The histological examination of the specimen is necessary to exclude malignancy and establish the final diagnosis.
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Cheung, Eva Y. W., Ricky W. K. Wu, Albert S. M. Li, and Ellie S. M. Chu. "AI Deployment on GBM Diagnosis: A Novel Approach to Analyze Histopathological Images Using Image Feature-Based Analysis." Cancers 15, no. 20 (2023): 5063. http://dx.doi.org/10.3390/cancers15205063.
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Background: Glioblastoma (GBM) is one of the most common malignant primary brain tumors, which accounts for 60–70% of all gliomas. Conventional diagnosis and the decision of post-operation treatment plan for glioblastoma is mainly based on the feature-based qualitative analysis of hematoxylin and eosin-stained (H&E) histopathological slides by both an experienced medical technologist and a pathologist. The recent development of digital whole slide scanners makes AI-based histopathological image analysis feasible and helps to diagnose cancer by accurately counting cell types and/or quantitative analysis. However, the technology available for digital slide image analysis is still very limited. This study aimed to build an image feature-based computer model using histopathology whole slide images to differentiate patients with glioblastoma (GBM) from healthy control (HC). Method: Two independent cohorts of patients were used. The first cohort was composed of 262 GBM patients of the Cancer Genome Atlas Glioblastoma Multiform Collection (TCGA-GBM) dataset from the cancer imaging archive (TCIA) database. The second cohort was composed of 60 GBM patients collected from a local hospital. Also, a group of 60 participants with no known brain disease were collected. All the H&E slides were collected. Thirty-three image features (22 GLCM and 11 GLRLM) were retrieved from the tumor volume delineated by medical technologist on H&E slides. Five machine-learning algorithms including decision-tree (DT), extreme-boost (EB), support vector machine (SVM), random forest (RF), and linear model (LM) were used to build five models using the image features extracted from the first cohort of patients. Models built were deployed using the selected key image features for GBM diagnosis from the second cohort (local patients) as model testing, to identify and verify key image features for GBM diagnosis. Results: All five machine learning algorithms demonstrated excellent performance in GBM diagnosis and achieved an overall accuracy of 100% in the training and validation stage. A total of 12 GLCM and 3 GLRLM image features were identified and they showed a significant difference between the normal and the GBM image. However, only the SVM model maintained its excellent performance in the deployment of the models using the independent local cohort, with an accuracy of 93.5%, sensitivity of 86.95%, and specificity of 99.73%. Conclusion: In this study, we have identified 12 GLCM and 3 GLRLM image features which can aid the GBM diagnosis. Among the five models built, the SVM model proposed in this study demonstrated excellent accuracy with very good sensitivity and specificity. It could potentially be used for GBM diagnosis and future clinical application.
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de Bel, Thomas, John-Melle Bokhorst, Jeroen van der Laak, and Geert Litjens. "Residual cyclegan for robust domain transformation of histopathological tissue slides." Medical Image Analysis 70 (May 2021): 102004. http://dx.doi.org/10.1016/j.media.2021.102004.
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Mete, Mutlu, Xiaowei Xu, Chun-Yang Fan, and Gal Shafirstein. "Automatic delineation of malignancy in histopathological head and neck slides." BMC Bioinformatics 8, Suppl 7 (2007): S17. http://dx.doi.org/10.1186/1471-2105-8-s7-s17.
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Peter, Loïc, Diana Mateus, Pierre Chatelain, et al. "Assisting the examination of large histopathological slides with adaptive forests." Medical Image Analysis 35 (January 2017): 655–68. http://dx.doi.org/10.1016/j.media.2016.09.009.
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Houghton, Joseph P., Aaron J. Ervine, Sarah L. Kenny, et al. "Concordance between digital pathology and light microscopy in general surgical pathology: a pilot study of 100 cases." Journal of Clinical Pathology 67, no. 12 (2014): 1052–55. http://dx.doi.org/10.1136/jclinpath-2014-202491.
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Aim(1) A pilot study to determine the accuracy of interpretation of whole slide digital images in a broad range of general histopathology cases of graded complexity. (2) To survey the participating histopathologists with regard to acceptability of digital pathology.Materials and methodsGlass slides of 100 biopsies and minor resections were digitally scanned in their entirety, producing digital slides. These cases had been diagnosed by light microscopy at least 1 year previously and were subsequently reassessed by the original reporting pathologist (who was blinded to their original diagnosis) using digital pathology. The digital pathology-based diagnosis was compared with the original glass slide diagnosis and classified as concordant, slightly discordant (without clinical consequence) or discordant. The participants were surveyed at the end of the study.ResultsThere was concordance between the original light microscopy diagnosis and digital pathology-based diagnosis in 95 of the 100 cases while the remaining 5 cases showed only slight discordance (with no clinical consequence). None of the cases were categorised as discordant. Participants had mixed experiences using digital pathology technology.ConclusionsIn the broad range of cases we examined, digital pathology is a safe and viable method of making a primary histopathological diagnosis.
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Rai, Tina, Garjesh Singh Rai, Aditya Gargava, Vaishali Jain, and G. K. Sawke. "A Study on the Use of Digital Images in Learning Histopathology in PhaseII M.B.B.S Students." International Journal of Pharmaceutical and Clinical Research 16, no. 6 (2024): 2041–46. https://doi.org/10.5281/zenodo.12749738.
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<strong>Background:</strong> With the recent advancement of whole slide digital scanners, tissue histopathology slides can now be digitalized & stored in digital image form, and these digital slides can be used as an effective tool for teaching histopathology along with the conventional histopathology slides. Learning to recognize & appreciate the histopathological features remains a difficult and time-consuming task for many. To improve the identification skills of the students, we have introduced a module containing digital histopathology slides. These slides can be used as an adjunct with old learning method in practical classes for learning histopathology. <strong>Aim: </strong>To introduce study of histopathology slides with the help of digital images to develop a better clinico-pathological correlation. <strong>Objectives:</strong> (1). To compare the learning outcomes of conventional method with digital learning method in histopathology practical classes. (2). To assess the student’s perception regarding new method of learning histopathology. <strong>Methodology</strong><strong>:</strong> This study was conducted in Department of Pathology, ABV GMC, Vidisha. It was an educational intervention study that lasted for a period of five months. It was carried on 150 students which were divided in the batch of two each having 75 students. Total 75 students were involved at one time who were taught by conventional method, while remaining was taught the same topic through the new (hybrid) method. The same set of questionnaires was given to both set of students and their scores were compared. Perception to the teaching learning method was taken on the Likert scale. After each session the batches were flipped. <strong>Results: </strong>Learning outcome of the students by new teaching learning method was much better (P<0.001) than the old method that was used to teach histopathology slides. <strong>Conclusion: </strong>Newer method introduced for learning histopathology slides gave a better learning outcome if used as an adjunct with old learning method, without compromising the basic skills that were taught in the old learning method.
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Ajayi, S., Y. Raji, S. Aminu, et al. "Changing trends of native renal histopathologic diagnosis in a tertiary health centre in Nigeria." Research Journal of Health Sciences 13, no. 1 (2025): 10–15. https://doi.org/10.4314/rejhs.v13i1.2.
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Background: Renal biopsy is an essential tool in investigating renal disease. Over the past few years, several authors have described changes in renal histopathologic diagnosis. Several factors may have contributed to this: including improvements in histopathological techniques, patient's demographic data, presence or absence of underlying disease or malignancy. Methodology:We reviewed our database of native renal biopsies done between 1968 and 2022 to study the trend in the histological diagnosis of patients with the nephrotic syndrome. Results: There was a total of 251 biopsy reports for which we had the requisite data which were year of diagnosis and histopathological diagnosis. In the period around 1968, the main histological diagnosis was proliferative glomerulonephritis, followed by membranous glomerulonephritis and miscellaneous. By the period of 1985-2011, membranoproliferative glomerulonephritis (MPGN) predominated as histological diagnosis. From 2012-2022, the pattern changed to focal segmental glomerulonephritis. Conclusion: There is a changing trend of histopathological diagnosis made from renal biopsy slides. There is a transition to predominance of focal segmental glomerulosclerosis. Whether this reported trend is real or apparent is still unclear.
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Mishra, Pragnya P., K. Madan, Siddhartha Biswas, Anuradha Calicut Kini Rao, Rohan Shetty, and Premanand Panda. "Tumour budding in colorectal carcinoma: Association with other histopathological prognostic parameters." IP Archives of Cytology and Histopathology Research 7, no. 1 (2022): 26–31. http://dx.doi.org/10.18231/j.achr.2022.006.
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Colorectal carcinoma (CRC) is the third leading cancer in India. Pathologists play a crucial role in assessing stage, analyzing surgical margins and documenting the histopathologic prognostic parameters. Tumour budding is one such prognostic parameter, defined as single cells or small groups of tumour cells (up to 4 cell clusters) at the invasive front of the tumour. The aim of this study is to examine the association of tumour budding with other histopathological prognostic parameters in patients with colorectal carcinoma.TheHematoxylin & Eosin (H &E) stained slides of 52 histopathologically diagnosed CRC resection specimens were reviewed and tumour budding (BD) was assessed into four grades under 200x power. Other histopathological prognostic parameters like tumour size, site, grade, laterality, lymphovascular invasion, perineural invasion, T and N stage were analyzed using descriptive statistics and Chi-square test with Software SPSS version 23.A higher BD score is seen to be more often associated with grade 3 tumour morphology, presence of perineural invasion, tumour size of 5cm or more and tumours located in the sigmoid colon or rectum. No association of tumour budding is seen with TIL’s or tumour of size &#60; 5cm.Tumour budding is a practical and significant histological index for identification of high malignant potential and poor outcome in CRC patients with rectal or sigmoid colon location, size more than 5cm, perineural invasion and higher histological grade. Tumour budding may help identify patients who need a more intensive postoperative follow up and the possibility of adjuvant therapy.
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Furat, N. Tawfeeq, A.S. Alwan Nada, and M. Khashman Basim. "Optimization of Digital Histopathology Image Quality." International Journal of Artificial Intelligence (IJ-AI) 7, no. 2 (2018): 71–77. https://doi.org/10.11591/ijai.v7.i2.pp71-77.
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One of the biomedical image problems is the appearance of the bubbles in the slide that could occur when air passes through the slide during the preparation process. These bubbles may complicate the process of analysing the histopathological images. Aims: The objective of this study is to remove the bubble noise from the histopathology images, and then predict the tissues that underlie it using the fuzzy controller in cases of remote pathological diagnosis. Methods: Fuzzy logic uses the linguistic definition to recognize the relationship between the input and the activity, rather than using difficult numerical equation. Mainly there are five parts, starting with accepting the image, passing through removing the bubbles, and ending with predict the tissues. These were implemented by defining membership functions between colours range using MATLAB. Results: 50 histopathological images were tested on four types of membership functions (MF); the results show that (nine-triangular) MF get 75.4% correctly predicted pixels versus 69.1, 72.31 and 72% for (five-triangular), (five-Gaussian) and (nine-Gaussian) respectively. Conclusions: In line with the era of digitally driven epathology, this process is essentially recommended to ensure quality interpretation and analyses of the processed slides; thus overcoming relevant limitations.
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Lichtblau, Daniel, and Catalin Stoean. "Cancer diagnosis through a tandem of classifiers for digitized histopathological slides." PLOS ONE 14, no. 1 (2019): e0209274. http://dx.doi.org/10.1371/journal.pone.0209274.
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Ahmad, Marwa. "Histopathological Approach to Camelid Hepatitis in One-Humped Camel Slaughter in Aswan Governorate, Egypt." Alexandria Journal of Veterinary Sciences 82 (2024): 16. http://dx.doi.org/10.5455/ajvs.208493.
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Abstract Many liver illnesses in camels are not recognized ante-mortem and are frequently missed due to vague or nonspecific symptoms. So, determining the histological appearance is a very important first step in the study of camel hepatitis. This study documents the histological changes associated with acute hepatitis in liver samples from camels (Camelus dromedaries) in Egypt's Aswan district. Liver samples were kept in 10 mL of formalin. The paraffin liver slices were stained on slides with hematoxylin and eosin. This is a common histological method for defining various tissue types and morphological changes. Acute hepatitis was detected in three out of the 43 camels slaughtered at the Aswan Government's Draw slaughterhouse. Acute hepatitis is characterized by a combination of inflammation, hepatocellular apoptosis, necrosis, and a varied mononuclear infilammatory infiltration, mainly lymphocytic, accompanied by a low number of plasma cells. Kupffer-cell activation, bile-duct damage, and the ductular reaction are additional characteristics.
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Wang, Linyan, Longqian Ding, Zhifang Liu, et al. "Automated identification of malignancy in whole-slide pathological images: identification of eyelid malignant melanoma in gigapixel pathological slides using deep learning." British Journal of Ophthalmology 104, no. 3 (2019): 318–23. http://dx.doi.org/10.1136/bjophthalmol-2018-313706.
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Background/AimsTo develop a deep learning system (DLS) that can automatically detect malignant melanoma (MM) in the eyelid from histopathological sections with colossal information density.MethodsSetting: Double institutional study.Study population: We retrospectively reviewed 225 230 pathological patches (small section cut from pathologist-labelled area from an H&E image), cut from 155 H&E-stained whole-slide images (WSI).Observation procedures: Labelled gigapixel pathological WSIs were used to train and test a model designed to assign patch-level classification. Using malignant probability from a convolutional neural network, the patches were embedded back into each WSI to generate a visualisation heatmap and leveraged a random forest model to establish a WSI-level diagnosis.Main outcome measure(s): For classification, the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity and specificity were used to evaluate the efficacy of the DLS in detecting MM.ResultsFor patch diagnosis, the model achieved an AUC of 0.989 (95% CI 0.989 to 0.991), with an accuracy, sensitivity and specificity of 94.9%, 94.7% and 95.3%, respectively. We displayed the lesion area on the WSIs as graded by malignant potential. For WSI, the obtained sensitivity, specificity and accuracy were 100%, 96.5% and 98.2%, respectively, with an AUC of 0.998 (95% CI 0.994 to 1.000).ConclusionOur DLS, which uses artificial intelligence, can automatically detect MM in histopathological slides and highlight the lesion area on WSIs using a probabilistic heatmap. In addition, our approach has the potential to be applied to the histopathological sections of other tumour types.
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Gholami, Behnaz, Manouchehr Khoshbaten, Hamidreza Eftekhari, et al. "Histopathological Association of Non-alcoholic Fatty Liver Disease With Coronary Artery Atherosclerosis Grade." International Journal of Medical Toxicology and Forensic Medicine 14, no. 1 (2024): 42742. http://dx.doi.org/10.32598/ijmtfm.v14i1.42742.
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Background: The association between the severity of coronary atherosclerosis and histopathologic findings in patients with non-alcoholic fatty liver disease (NAFLD) is not entirely understood. Considering the gold standard method, this study evaluates the histopathologic association between the severity of NAFLD and the grades of coronary atherosclerosis. Methods: In this descriptive-analytical study, data from 205 cadavers who were referred to an Iranian (Tabriz) forensic medicine organization between 2015 and 2017 and underwent simultaneous liver and coronary artery biopsies were examined. Finally, 168 cases were entered based on the inclusion criteria. First, pathological slides of these cadavers were extracted from the forensic medicine archive and re-examined. Then, the selected cases’ blocks were extracted from the tissue block bank, and again, after preparing a new slide, they were stained with trichrome for accurate estimation of liver fibrosis. Results: The assessment of NAFLD histological status in the studied cases revealed that 75.6% of the cases were classified as severity I, 18.4% as severity II, and 6% as severity III. Most cases with coronary atherosclerosis were classified as American Heart Association staging (AHA), type V (19.6%), and normal (19.6%). There was no statistically significant relationship between the severity of simple steatosis, steatohepatitis, and NAFLD, with coronary atherosclerosis. In subjects with higher severity of coronary atherosclerosis, the liver fibrosis rate is also higher, but no statistically significant difference was observed. Conclusion: The present study revealed no significant histopathological association between NAFLD and coronary artery atherosclerosis grade.
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Bhatta, S., and S. Hirachan. "Prostatic lesions: Histopathological study in a tertiary care hospital." Journal of Manmohan Memorial Institute of Health Sciences 4, no. 1 (2018): 12–19. http://dx.doi.org/10.3126/jmmihs.v4i1.21133.
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Background: Prostatic lesions like Nodular hyperplasia of prostate, inflammation and carcinoma are common causes of morbidity and mortality in males. The incidence of these lesions increases with age. This study was conducted with the objective of evaluating histopathological pattern of prostatic lesions.Methods: This was a retrospective study conducted at KIST Medical College from Jan 2014 to Jan 2018. The study included ninety six prostatic specimens received in department of pathology. Hematoxylin and Eosin stained slides were retrieved and reviewed. The specimens and slides were analyzed according to type of specimen, age of patient, histopathological pattern and final diagnosis. Results were analyzed using Statistical Package for Social Science (SPSS, version 21) for Windows. Independent t test was used to correlate the mean age between patients with benign and malignant lesions. P value less than 0.05 was considered as statistically significant.Results: The most common benign lesion was nodular hyperplasia of prostate 86(89.58%). Malignant lesions comprised 8 (8.34%) cases of all prostatic lesions. All the cases of prostate carcinoma were adenocarcinoma. The most frequent Gleason score was 9. Mean age for benign and malignant lesions were 69.6 ± 8.1 years and 72.9 ± 5.2 years respectively. There was no significant difference in the mean age between patients with benign and malignant lesions (p value 0.27).Conclusion: Benign lesions of prostate are more common than malignant lesions. Histopathological examination of prostate specimens have important role in diagnosing various benign and malignant lesions, especially to rule out incidental carcinoma.JMMIHS.2018;4(1):12-19
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Shafi, Gowhar, Shivamurthy P.M., Anand Ulle, et al. "AI-enabled identification prediction of homologous recombination deficiency (HRD) from histopathology images." Journal of Clinical Oncology 40, no. 16_suppl (2022): 3019. http://dx.doi.org/10.1200/jco.2022.40.16_suppl.3019.
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3019 Background: Homologous recombination deficient (HRD) tumors are highly responsive to platinum-based chemotherapy and poly (ADP-ribose) polymerase inhibitor (PARPi) therapy. Pathogenic BRCA-1 and BRCA-2 (BRCA1/2) alterations are key members of the HR DNA repair pathway but genomic instability status, including loss of heterozygosity, telomeric allelic imbalance and large scale state transitions across the genome are also predictive of HRD. HRD testing is currently performed by next generation sequencing which can take 2-4 weeks for results, has a high failure rate, requires significant tissue and is costly. We developed and tested the ability of an AI enabled platform to predict HRD status from the analysis of whole slide imaging of the diagnostic H&E slide. This platform, iPREDICT-HRD is rapid, precise, and cost effective. Methods: The AI engine was trained on 120 H&E slides that were used to identify tumor prior to manual microdisseection for HRD assessment by NGS. Histopathological features were extracted, followed by feature mapping to predict HRD status based on the results of NGS testing. ResNet AI algorithm was trained to segment, annotate and predict HRD status. 10 lac tiles of 256x256 size at 40x magnification were generated per pathological class. 70% of the data set was used for training and 30% for validation of the AI model. Results: Using single blinded clinical samples, iPREDICT-HRD tool detected HRD + ve samples with 99.3% accuracy with 100% sensitivity and 99% specificity in the test set. Patch-level predictions of HRD status demonstrated intra-tumor heterogeneity within the H&E slides. Visual inspection of the heatmap suggested the presence of patches with high predictive ability of HRD status and this outperformed an average HRD score for slides with heterogeneity. Conclusions: AI-enabled prediction of HRD status can be accurately performed on diagnostic H&E slides potentially yielding results quickly and afforadably, even when limited tissue is available for testing.
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Thajudeen, Ayeesha, Sudha Srinivasan, GeethaPriya Govindarajan, and Akashavanan Shanmugam. "A comparative study of efficacy of coconut oil, lemon water and dishwashing liquid as surrogates to xylene." Environmental Analysis Health and Toxicology 37, no. 3 (2022): e2022026. http://dx.doi.org/10.5620/eaht.2022026.
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Histopathology is the field of science that helps in analyzing the architectural pattern of cells under the microscope. Hematoxylin and eosin-stained sections are used for routine histopathological examination. Xylene being a biohazardous hydrocarbon is used in many steps of tissue processing and laboratory personnel are exposed to this toxic substance. Maximum exposure to xylene occurs in the step of deparaffinization, for which alternate safer methods should be introduced. This study compares the efficacy of natural products like coconut oil, lemon water, less chemical substance like dish wash liquid with xylene as deparaffinizing agent. 50 paraffin embedded sections were used in each of the groups using xylene, coconut oil, diluted lemon water and dish wash liquid as deparaffinizing agents. 80% of slides using dishwashing liquid, 64% using lemon water and 42% of slides using coconut oil showed excellent cellular features. 96% of slides using xylene showed good quality staining, 54% of slides using dishwashing liquid and 40% slides using lemon water showed good quality staining. Only 4% of slides prepared using coconut oil showed good quality staining. Dishwashing liquid is the best surrogate and among the natural products, diluted lime water yields a better result and coconut oil, the least productive as deparaffinizing agent in this study.
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Luhano, Mahesh Kumar, Fareeha Naseer Syed, Afia Sarwar, Urwa Sarwar, Sabah Kaleem Baloch, and Fnu Anjali. "Malignancies in the Parotid Glands: A Histopathological Analysis." Pakistan Journal of Medical and Health Sciences 17, no. 2 (2023): 692–94. http://dx.doi.org/10.53350/pjmhs2023172692.
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Objective: The purpose of this study was to examine the incidence of benign and malignant parotid tumors, as well as their age and gender distributions, and histomorphological characteristics. Study Design: Retrospective study Place and Duration: Liaquat University of Medical and Health Sciences, Jamshoro in the duration from July, 2022 to December, 2022. Methods: The department of pathology examined a total of 47 Parotid tumor specimens. Sections were obtained, processed, and paraffin-embedded after specimens were formalin-fixed. Slides were prepared, hematoxylin and eosin was used to stain them, and then the paraffin blocks were sliced. (WHO) histological type was used to categorize the cancers. The gathered data were statistically examined. Results: There were majority 26 (55.3%) males and 21 (44.7%) females in this study. Among all, 34 (72.3%) cases had benign tumor and 13 (27.7%) cases were had malignant tumor. Among 34 cases of benign tumor, pleomorphic adenoma was the most common found in 17 cases and in malignant tumor mucoepidermoid carcinoma was the most common found in 6 cases. Majority of the cases of benign tumor were aged between 30-50 years. Conclusion: Parotid tumors are significant even though they are uncommon because they exhibit a great variety of morphologic variability between various tumor types and occasionally even within a single tumor mass. Hence, a precise diagnosis is crucial and may be made by a histological study. Keywords: Histopathological Examination, Parotid Tumors, Mucoepidermoid Carcinoma, Pleomorphic Adenoma
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Tawfeeq, Furat Nidhal, Nada A. S. Alwan, and Basim M. Khashman. "Optimization of Digital Histopathology Image Quality." IAES International Journal of Artificial Intelligence (IJ-AI) 7, no. 2 (2018): 71. http://dx.doi.org/10.11591/ijai.v7.i2.pp71-77.
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<span lang="EN-US">One of the biomedical image problems is the appearance of the bubbles in the slide that could occur when air passes through the slide during the preparation process. These bubbles may complicate the process of analysing the histopathological images. The objective of this study is to remove the bubble noise from the histopathology images, and then predict the tissues that underlie it using the fuzzy controller in cases of remote pathological diagnosis. Fuzzy logic uses the linguistic definition to recognize the relationship between the input and the activity, rather than using difficult numerical equation. Mainly there are five parts, starting with accepting the image, passing through removing the bubbles, and ending with predict the tissues. These were implemented by defining membership functions between colours range using MATLAB. Results: 50 histopathological images were tested on four types of membership functions (MF); the results show that (nine-triangular) MF get 75.4% correctly predicted pixels versus 69.1, 72.31 and 72% for (five- triangular), (five-Gaussian) and (nine-Gaussian) respectively. Conclusions: In line with the era of digitally driven e-pathology, this process is essentially recommended to ensure quality interpretation and analyses of the processed slides; thus overcoming relevant limitations.</span>
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Deyhimi, Parviz, Forouz Keshani, Fatemeh Mohaghegh, and Taha Mohagheghi. "Comparative study of histopathological diagnostic criteria in cutaneous lesions of lichen planus and discoid lupus erythematosus." Journal of Shahrekord University of Medical Sciences 24, no. 2 (2022): 84–92. http://dx.doi.org/10.34172/jsums.2022.14.
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Background and aims: Lichen planus (LP) and discoid lupus erythematosus (DLE) are two relatively common mucocutaneous lesions whose clinical and histopathological features overlap in some cases. The present study aimed to distinguish between these two lesions histopathologically in order to treat them more accurately. Methods: In a cross-sectional descriptive-analytical study, 29 and 48 microscopic slides of skin samples of DLE and LP, respectively, were examined in the pathology archive of Al-Zahra hospital of Isfahan from 2008 to 2018. The slides prepared by hematoxylin-eosin staining were examined simultaneously and blindly by three pathologists with a light microscope and compared according to certain histopathological criteria. Then obtained data were analyzed by SPSS version 24 using chi-square, Fisher’s exact, Mann-Whitney, and t tests (P<0.05). Results: Based on the findings, the presence of hyperparakeratosis with superficial hyperorthokeratosis, epithelial atrophy, deep perivascular infiltration, presence of edema in the papillary dermis, presence of plasma cells with lymphohistiocytes in inflammatory infiltration, and presence of mucin in the dermis were significantly higher in DLE than in LP (P<0.05). On the other hand, the intensity of lichenoid infiltration, presence of saw tooth hyperplasia of rete ridges, presence of cleft between the epithelium and connective tissue, spongiosis, hyperorthokeratosis alone, and wedge-shaped hypergranulosis were significantly higher in LP than in DLE (P<0.05). Conclusion: Perieccrine and perifollicular inflammation, presence of Civatte bodies (CBs), abundance of fibrosis, presence of pale keratinocytes, and presence of pseudoepitheliomatous hyperplasia were not the criteria for differential diagnosis of LP and DLE.
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Ahsan, Farah, Muhammad Asif, Ishaque Chauhan, Ahmed Ahson Khan, and Maria Aslam. "Frequency of Histological Patterns of Renal Allograft Biopsies– One Year of Renal Allograft Experience." Pakistan Armed Forces Medical Journal 72, no. 4 (2022): 1411–14. http://dx.doi.org/10.51253/pafmj.v72i4.8129.
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Objective: To ascertain the frequency and spectrum of histopathological findings in renal allograft rejection cases received in one year.
 Study Design: Cross-sectional study.
 Place and Duration of Study: Histopathology Department of Armed Forces Institute of Pathology, Rawalpindi Pakistan from Jun 2020 to May 2021.
 Methodology: Renal allograft rejection biopsy cases of 62 male and female patients between the ages of 15-60 years having undergone renal transplant with prior end-stage renal disease over one year were collected. Frequency and histopathological findings were studied after classifying them according to the Banff Classification.
 Results: Cellular (T-cell mediated) rejection accounted for more than half of the cases under study, making it the most common cause of transplant rejection in our demographical area. It accounted for 28 (45.2%) slides of all the biopsies studied. Antibody-mediated rejection followed next with 17 (27.4%) slides, with seven slides (11.3%) of the cases borderline for changes accounting for a T-Cell mediated rejection. About 10 (16.2%) were non-specific changes negative for transplant rejection criteria.
 Conclusion: Our study was instrumental in establishing rejection patterns and major rejection sub-types while classified under the Banff Classification in our demographical area. The cataloguing of the cases and the major underlying cause would help minimize rejection rates resulting in better clinical outcomes and increased patient survival.
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Tirosh, Omer, Leon Gugel, Gal Dinstag, et al. "A blind retrospective analysis of a novel predictive marker to ICB response in NSCLC, calculated directly from histopathological slides." Journal of Clinical Oncology 42, no. 16_suppl (2024): 2665. http://dx.doi.org/10.1200/jco.2024.42.16_suppl.2665.
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2665 Background: Immune checkpoint blockers (ICB), and primarily PD-1/PD-L1 inhibitors, are in the forefront of contemporary clinical oncology and have become an integral part of treatment of many malignancies, including non-small cell lung cancer (NSCLC). Nevertheless, tumor response to ICB varies widely. Predictive markers commonly used to distinguish patients likely to respond to ICB, such as PD-L1 expression and tumor mutational burden (TMB) have limited predictive value, which calls for the development of practical and more accurate tests. We present results of a blind retrospective analysis of a novel predictive marker of ICB response in NSCLC, relying solely on histopathological slides. Methods: We obtained high resolution Hematoxylin and Eosin (H&E) slides from tumor-tissue samples of 50 cases of metastatic NSCLC patients treated with first-line PD-1 inhibitors. We retrospectively applied our ENLIGHT-DeepPT (ENLIGHT-DP for short) pipeline to generate, in a blinded manner, an individual response score to PD-1 inhibition for each slide. ENLIGHT-DP is composed of two main steps: (i) predict mRNA expression directly from an H&E slide using DeepPT, our digital-pathology based algorithm; and (ii) use these values as input to ENLIGHT, our transcriptome-based precision oncology platform, which generates a score that predicts response to targeted therapies and ICB (based on a 10-gene signature in this case). We then unblinded the clinical outcome (RECIST1.1), and evaluated ENLIGHT-DP’s performance vs. standard markers. Results: ENLIGHT-DP’s score is predictive of response in this cohort, which had an overall response rate of 68% (34 of 50), with ROC AUC = 0.69 (p = 0.01, one-sided permutation test). Using a predefined threshold for binary classification of response derived from independent data, all 15 patients that were predicted to respond by ENLIGHT-DP indeed responded (100% PPV, 44% sensitivity). In comparison, predicting response according to PD-L1 > 1% achieves 68% PPV and 62% sensitivity, while PD-L1 > 50% achieves 65% PPV and 38% sensitivity, i.e, both thresholds exhibit no predictive power (PPV <= baseline response rate). Patients with high TMB (>10) had 82% PPV and 26% sensitivity, showing lower predictive benefit than ENLIGHT-DP. ENLIGHT-DP was particularly good at stratifying patients with PD-L1 < 1% (18 patients, ROC AUC = 0.8, p = 0.03). Conclusions: ENLIGHT-DP demonstrates high predictive power for response to ICB in NSCLC relying solely on accessible H&E slides, outperforming the commonly used PD-L1 and TMB markers. ENLIGHT-DP is also able to identify responders within patients with PD-L1 < 1%, for whom ICB is usually considered ineffective. Importantly, our approach does not require training on prior treatment outcomes, and can therefore be generalized to drugs for which such data is unavailable or scarce.
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Paudyal, P., S. Shrestha, M. Agarwal, et al. "Histoid leprosy on scrotal skin: an unusual case." International Journal of Infection and Microbiology 2, no. 2 (2013): 64–67. http://dx.doi.org/10.3126/ijim.v2i2.8325.
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INTRODUCTION: Although no part of the skin is immune from invasion by mycobacterium leprae it is commonly seen over cooler parts of the body and very rarely found over external genitalia because of their warm temperature. Histoid leprosy is rare but a well-defined entity with specific clinical, histopathological, and bacteriological features. CASE REPORT: A Punch biopsy measuring 3x3 cm of a 73 year old male having an erythematous plaque over scrotum since 2 months was received for histopathologic examination with the clinical differentials of pilomatrixoma and steatocystoma multiplex. Histopathologic and special stain slides revealed a feature of histoid leprosy. CONCLUSION: Although rare, leprosy lesions may occur on the male genitalia and therefore in all male patients, history taking and examination of external genitalia should not be neglected. DOI: http://dx.doi.org/10.3126/ijim.v2i2.8325 Int J Infect Microbiol 2013;2(2):64-67
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Ojo, B. A., A. A. Akanbi, M. S. Odimayo, and A. K. Jimoh. "Endometrial tuberculosis in the Nigerian middle belt: an eight-year review." Tropical Doctor 38, no. 1 (2008): 3–4. http://dx.doi.org/10.1258/td.2007.052090.
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Endometrial tuberculosis (TB) is a known cause of infertility in women which, because of the global increase in the spread of TB, should always be considered when investigating the cause of infertility. We undertook this review in order to determine its incidence among infertile women in the Nigerian middle belt. This is a retrospective study of all histopathological slides of infertile women seen at the University of Ilorin Teaching Hospital, Ilorin, Nigeria, between 1997 and 2004 who were evaluated for infertility by endometrial biopsy. The slides were reviewed and, where necessary, new sections were cut from tissue blocks and stained with haematoxylin and eosin. Ziehl–Neelsen stain used to demonstrate acid-fast bacilli. Clinical reports were extracted from histopathological request form. A total of 661 patients were included in the study. Primary infertility constituted 30%, secondary 69% and unspecified cases 1%. TB endometritis was seen in 0.45%. Endometrial TB is not a frequent cause of infertility in our set-up. However, with the worldwide resurgence of TB, its possible presence should always be taken into consideration.