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Journal articles on the topic 'Histopathological tumor segmentation'

Author: Grafiati

Published: 25 May 2024

Last updated: 31 July 2025

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1

Elidrissi, Sofyan, Ikram Ben Abdel Ouahab, Mohammed Bouhorma, and Fatiha Elouaai. "Unveiling the Clinical Significance of Microsatellite Instability in Colorectal Cancer: Deep Learning and the Segment Anything Model for Accurate Segmentation and Classification." International Journal of Online and Biomedical Engineering (iJOE) 21, no. 06 (2025): 97–110. https://doi.org/10.3991/ijoe.v21i06.54491.

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Microsatellite instability (MSI) is crucial for colorectal cancer (CRC) diagnosis and prognosis. Accurate differentiation between MSI and microsatellite stability (MSS) tumors is essential for personalized treatment. This paper introduces a novel approach combining the segment anything model (SAM), Yolov8, and convolutional neural networks (CNNs) for precise segmentation and classification of histopathological images. SAM employs a prompt-based mechanism for segmenting tumor regions like invasive margins, tumor-infiltrating lymphocytes (TILs), and necrotic areas. Integrating SAM’s segmentation

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Liu, Yiqing, Qiming He, Hufei Duan, Huijuan Shi, Anjia Han, and Yonghong He. "Using Sparse Patch Annotation for Tumor Segmentation in Histopathological Images." Sensors 22, no. 16 (2022): 6053. http://dx.doi.org/10.3390/s22166053.

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Tumor segmentation is a fundamental task in histopathological image analysis. Creating accurate pixel-wise annotations for such segmentation tasks in a fully-supervised training framework requires significant effort. To reduce the burden of manual annotation, we propose a novel weakly supervised segmentation framework based on sparse patch annotation, i.e., only small portions of patches in an image are labeled as ‘tumor’ or ‘normal’. The framework consists of a patch-wise segmentation model called PSeger, and an innovative semi-supervised algorithm. PSeger has two branches for patch classific

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Zadeh Shirazi, Amin, Eric Fornaciari, Mark D. McDonnell, et al. "The Application of Deep Convolutional Neural Networks to Brain Cancer Images: A Survey." Journal of Personalized Medicine 10, no. 4 (2020): 224. http://dx.doi.org/10.3390/jpm10040224.

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In recent years, improved deep learning techniques have been applied to biomedical image processing for the classification and segmentation of different tumors based on magnetic resonance imaging (MRI) and histopathological imaging (H&E) clinical information. Deep Convolutional Neural Networks (DCNNs) architectures include tens to hundreds of processing layers that can extract multiple levels of features in image-based data, which would be otherwise very difficult and time-consuming to be recognized and extracted by experts for classification of tumors into different tumor types, as well a

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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. (

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Park, Youngjae, Jinhee Park, and Gil-Jin Jang. "Efficient Perineural Invasion Detection of Histopathological Images Using U-Net." Electronics 11, no. 10 (2022): 1649. http://dx.doi.org/10.3390/electronics11101649.

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Perineural invasion (PNI), a sign of poor diagnosis and tumor metastasis, is common in a variety of malignant tumors. The infiltrating patterns and morphologies of tumors vary by organ and histological diversity, making PNI detection difficult in biopsy, which must be performed manually by pathologists. As the diameters of PNI nerves are measured on a millimeter scale, the PNI region is extremely small compared to the whole pathological image. In this study, an efficient deep learning-based method is proposed for detecting PNI regions in multiple types of cancers using only PNI annotations wit

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Sun, Yibao, Zhaoyang Xu, Yihao Guo, et al. "Scale-Adaptive viable tumor burden estimation via histopathological microscopy image segmentation." Computers in Biology and Medicine 189 (May 2025): 109915. https://doi.org/10.1016/j.compbiomed.2025.109915.

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Mutkule, Prasad R., Nilesh P. Sable, Parikshit N. Mahalle, and Gitanjali R. Shinde. "Histopathological parameter and brain tumor mapping using distributed optimizer tuned explainable AI classifier." Journal of Autonomous Intelligence 7, no. 5 (2024): 1617. http://dx.doi.org/10.32629/jai.v7i5.1617.

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<p>Brain tumors represent a critical and severe challenge worldwide early and accurate diagnosis is necessary to increase the predictions for individuals with brain tumors. Several studies on brain tumor mapping have been conducted recently; however, the methods have some drawbacks, including poor image quality, a lack of data, and a limited capacity for generalization ability. To tackle these drawbacks this research presents a distributed optimizer tuned explainable AI classifier model for brain tumor mapping from histopathological images. The foraging gyps africanus optimization enable

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Althubaity, DaifAllah D., Faisal Fahad Alotaibi, Abdalla Mohamed Ahmed Osman, et al. "Automated Lung Cancer Segmentation in Tissue Micro Array Analysis Histopathological Images Using a Prototype of Computer-Assisted Diagnosis." Journal of Personalized Medicine 13, no. 3 (2023): 388. http://dx.doi.org/10.3390/jpm13030388.

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Background: Lung cancer is a fatal disease that kills approximately 85% of those diagnosed with it. In recent years, advances in medical imaging have greatly improved the acquisition, storage, and visualization of various pathologies, making it a necessary component in medicine today. Objective: Develop a computer-aided diagnostic system to detect lung cancer early by segmenting tumor and non-tumor tissue on Tissue Micro Array Analysis (TMA) histopathological images. Method: The prototype computer-aided diagnostic system was developed to segment tumor areas, non-tumor areas, and fundus on TMA

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Altini, Nicola, Emilia Puro, Maria Giovanna Taccogna, et al. "Tumor Cellularity Assessment of Breast Histopathological Slides via Instance Segmentation and Pathomic Features Explainability." Bioengineering 10, no. 4 (2023): 396. http://dx.doi.org/10.3390/bioengineering10040396.

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The segmentation and classification of cell nuclei are pivotal steps in the pipelines for the analysis of bioimages. Deep learning (DL) approaches are leading the digital pathology field in the context of nuclei detection and classification. Nevertheless, the features that are exploited by DL models to make their predictions are difficult to interpret, hindering the deployment of such methods in clinical practice. On the other hand, pathomic features can be linked to an easier description of the characteristics exploited by the classifiers for making the final predictions. Thus, in this work,

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Wu, Rujuan, Jiayi Yang, Qi Chen, et al. "Distinguishing of Histopathological Staging Features of H-E Stained Human cSCC by Microscopical Multispectral Imaging." Biosensors 14, no. 10 (2024): 467. http://dx.doi.org/10.3390/bios14100467.

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Cutaneous squamous cell carcinoma (cSCC) is the second most common malignant skin tumor. Early and precise diagnosis of tumor staging is crucial for long-term outcomes. While pathological diagnosis has traditionally served as the gold standard, the assessment of differentiation levels heavily depends on subjective judgments. Therefore, how to improve the diagnosis accuracy and objectivity of pathologists has become an urgent problem to be solved. We used multispectral imaging (MSI) to enhance tumor classification. The hematoxylin and eosin (H&E) stained cSCC slides were from Shanghai Ruiji

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Ramakrishnan, Vignesh, Annalena Artinger, Laura Alexandra Daza Barragan, et al. "Nuclei Detection and Segmentation of Histopathological Images Using a Feature Pyramidal Network Variant of a Mask R-CNN." Bioengineering 11, no. 10 (2024): 994. http://dx.doi.org/10.3390/bioengineering11100994.

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Cell nuclei interpretation is crucial in pathological diagnostics, especially in tumor specimens. A critical step in computational pathology is to detect and analyze individual nuclear properties using segmentation algorithms. Conventionally, a semantic segmentation network is used, where individual nuclear properties are derived after post-processing a segmentation mask. In this study, we focus on showing that an object-detection-based instance segmentation network, the Mask R-CNN, after integrating it with a Feature Pyramidal Network (FPN), gives mature and reliable results for nuclei detect

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Musulin, Jelena, Daniel Štifanić, Ana Zulijani, Tomislav Ćabov, Andrea Dekanić, and Zlatan Car. "An Enhanced Histopathology Analysis: An AI-Based System for Multiclass Grading of Oral Squamous Cell Carcinoma and Segmenting of Epithelial and Stromal Tissue." Cancers 13, no. 8 (2021): 1784. http://dx.doi.org/10.3390/cancers13081784.

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Oral squamous cell carcinoma is most frequent histological neoplasm of head and neck cancers, and although it is localized in a region that is accessible to see and can be detected very early, this usually does not occur. The standard procedure for the diagnosis of oral cancer is based on histopathological examination, however, the main problem in this kind of procedure is tumor heterogeneity where a subjective component of the examination could directly impact patient-specific treatment intervention. For this reason, artificial intelligence (AI) algorithms are widely used as computational aid

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Nicolás-Sáenz, Laura, Sara Guerrero-Aspizua, Javier Pascau, and Arrate Muñoz-Barrutia. "Nonlinear Image Registration and Pixel Classification Pipeline for the Study of Tumor Heterogeneity Maps." Entropy 22, no. 9 (2020): 946. http://dx.doi.org/10.3390/e22090946.

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We present a novel method to assess the variations in protein expression and spatial heterogeneity of tumor biopsies with application in computational pathology. This was done using different antigen stains for each tissue section and proceeding with a complex image registration followed by a final step of color segmentation to detect the exact location of the proteins of interest. For proper assessment, the registration needs to be highly accurate for the careful study of the antigen patterns. However, accurate registration of histopathological images comes with three main problems: the high

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Huang, Zhi, Anil V. Parwani, Kun Huang, and Zaibo Li. "Abstract 5436: Developing artificial intelligence algorithms to predict response to neoadjuvant chemotherapy in HER2-positive breast cancer." Cancer Research 83, no. 7_Supplement (2023): 5436. http://dx.doi.org/10.1158/1538-7445.am2023-5436.

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Abstract Increasing implementation of whole slide image (WSI) and advances in computing capacity enable the use of artificial intelligence (AI) in pathology, such as quantification of biomarkers, aids in diagnosis and detection of lymph node metastasis. However, predicting therapy response in cancer patients from pre-treatment histopathologic images remains a challenging task, limited by poor understanding of tumor immune microenvironment. In this study, we aimed to develop AI models using multi-source histopathologic images to predict neoadjuvant chemotherapy (NAC) response in HER2-positive (

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de Oliveira, Lays Assolini Pinheiro, Diana Lorena Garcia Lopes, João Pedro Perez Gomes, et al. "Enhanced Diagnostic Precision: Assessing Tumor Differentiation in Head and Neck Squamous Cell Carcinoma Using Multi-Slice Spiral CT Texture Analysis." Journal of Clinical Medicine 13, no. 14 (2024): 4038. http://dx.doi.org/10.3390/jcm13144038.

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This study explores the efficacy of texture analysis by using preoperative multi-slice spiral computed tomography (MSCT) to non-invasively determine the grade of cellular differentiation in head and neck squamous cell carcinoma (HNSCC). In a retrospective study, MSCT scans of patients with HNSCC were analyzed and classified based on its histological grade as moderately differentiated, well-differentiated, or poorly differentiated. The location of the tumor was categorized as either in the bone or in soft tissues. Segmentation of the lesion areas was conducted, followed by texture analysis. Ele

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Fagundes, Theara C., Arnoldo Mafra, Rodrigo G. Silva, et al. "Individualized threshold for tumor segmentation in 18F-FDG PET/CT imaging: The key for response evaluation of neoadjuvant chemoradiation therapy in patients with rectal cancer?" Revista da Associação Médica Brasileira 64, no. 2 (2018): 119–26. http://dx.doi.org/10.1590/1806-9282.64.02.119.

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Summary Introduction: The standard treatment for locally advanced rectal cancer (RC) consists of neoadjuvant chemoradiation followed by radical surgery. Regardless the extensive use of SUVmax in 18F-FDG PET tumor uptake as representation of tumor glycolytic consumption, there is a trend to apply metabolic volume instead. Thus, the aim of the present study was to evaluate a noninvasive method for tumor segmentation using the 18F-FDG PET imaging in order to predict response to neoadjuvant chemoradiation therapy in patients with rectal cancer. Method: The sample consisted of stage II and III rect

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Anghel, Cristian, Mugur Cristian Grasu, Denisa Andreea Anghel, Gina-Ionela Rusu-Munteanu, Radu Lucian Dumitru, and Ioana Gabriela Lupescu. "Pancreatic Adenocarcinoma: Imaging Modalities and the Role of Artificial Intelligence in Analyzing CT and MRI Images." Diagnostics 14, no. 4 (2024): 438. http://dx.doi.org/10.3390/diagnostics14040438.

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Pancreatic ductal adenocarcinoma (PDAC) stands out as the predominant malignant neoplasm affecting the pancreas, characterized by a poor prognosis, in most cases patients being diagnosed in a nonresectable stage. Image-based artificial intelligence (AI) models implemented in tumor detection, segmentation, and classification could improve diagnosis with better treatment options and increased survival. This review included papers published in the last five years and describes the current trends in AI algorithms used in PDAC. We analyzed the applications of AI in the detection of PDAC, segmentati

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Cancian, Pierandrea, Nina Cortese, Matteo Donadon, et al. "Development of a Deep-Learning Pipeline to Recognize and Characterize Macrophages in Colo-Rectal Liver Metastasis." Cancers 13, no. 13 (2021): 3313. http://dx.doi.org/10.3390/cancers13133313.

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Quantitative analysis of Tumor Microenvironment (TME) provides prognostic and predictive information in several human cancers but, with few exceptions, it is not performed in daily clinical practice since it is extremely time-consuming. We recently showed that the morphology of Tumor Associated Macrophages (TAMs) correlates with outcome in patients with Colo-Rectal Liver Metastases (CLM). However, as for other TME components, recognizing and characterizing hundreds of TAMs in a single histopathological slide is unfeasible. To fasten this process, we explored a deep-learning based solution. We

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Bundschuh, Lena, Jens Buermann, Marieta Toma, et al. "A Tumor Volume Segmentation Algorithm Based on Radiomics Features in FDG-PET in Lung Cancer Patients, Validated Using Surgical Specimens." Diagnostics 14, no. 23 (2024): 2654. http://dx.doi.org/10.3390/diagnostics14232654.

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Background: Although the integration of positron emission tomography into radiation therapy treatment planning has become part of clinical routine, the best method for tumor delineation is still a matter of debate. In this study, therefore, we analyzed a novel, radiomics-feature-based algorithm in combination with histopathological workup for patients with non-small-cell lung cancer. Methods: A total of 20 patients with biopsy-proven lung cancer who underwent [18F]fluorodeoxyglucose positron emission/computed tomography (FDG-PET/CT) examination before tumor resection were included. Tumors were

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Mahmoudi, Keon, Daniel H. Kim, Elham Tavakkol, et al. "Multiparametric Radiogenomic Model to Predict Survival in Patients with Glioblastoma." Cancers 16, no. 3 (2024): 589. http://dx.doi.org/10.3390/cancers16030589.

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Background: Clinical, histopathological, and imaging variables have been associated with prognosis in patients with glioblastoma (GBM). We aimed to develop a multiparametric radiogenomic model incorporating MRI texture features, demographic data, and histopathological tumor biomarkers to predict prognosis in patients with GBM. Methods: In this retrospective study, patients were included if they had confirmed diagnosis of GBM with histopathological biomarkers and pre-operative MRI. Tumor segmentation was performed, and texture features were extracted to develop a predictive radiomic model of su

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Broggi, Giuseppe, Antonino Maniaci, Mario Lentini, et al. "Artificial Intelligence in Head and Neck Cancer Diagnosis: A Comprehensive Review with Emphasis on Radiomics, Histopathological, and Molecular Applications." Cancers 16, no. 21 (2024): 3623. http://dx.doi.org/10.3390/cancers16213623.

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The present review discusses the transformative role of AI in the diagnosis and management of head and neck cancers (HNCs). Methods: It explores how AI technologies, including ML, DL, and CNNs, are applied in various diagnostic tasks, such as medical imaging, molecular profiling, and predictive modeling. Results: This review highlights AI’s ability to improve diagnostic accuracy and efficiency, particularly in analyzing medical images like CT, MRI, and PET scans, where AI sometimes outperforms human radiologists. This paper also emphasizes AI’s application in histopathology, where algorithms a

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Zováthi, Bendegúz H., Réka Mohácsi, Attila Marcell Szász, and György Cserey. "Breast Tumor Tissue Segmentation with Area-Based Annotation Using Convolutional Neural Network." Diagnostics 12, no. 9 (2022): 2161. http://dx.doi.org/10.3390/diagnostics12092161.

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In this paper, we propose a novel approach to segment tumor and normal regions in human breast tissues. Cancer is the second most common cause of death in our society; every eighth woman will be diagnosed with breast cancer in her life. Histological diagnosis is key in the process where oncotherapy is administered. Due to the time-consuming analysis and the lack of specialists alike, obtaining a timely diagnosis is often a difficult process in healthcare institutions, so there is an urgent need for improvement in diagnostics. To reduce costs and speed up the process, an automated algorithm cou

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Hosainey, Sayied Abdol Mohieb, David Bouget, Ingerid Reinertsen, et al. "Are there predilection sites for intracranial meningioma? A population-based atlas." Neurosurgical Review 45, no. 2 (2021): 1543–52. http://dx.doi.org/10.1007/s10143-021-01652-9.

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Abstract Meningioma is the most common benign intracranial tumor and is believed to arise from arachnoid cap cells of arachnoid granulations. We sought to develop a population-based atlas from pre-treatment MRIs to explore the distribution of intracranial meningiomas and to explore risk factors for development of intracranial meningiomas in different locations. All adults (≥ 18 years old) diagnosed with intracranial meningiomas and referred to the department of neurosurgery from a defined catchment region between 2006 and 2015 were eligible for inclusion. Pre-treatment T1 contrast-enhanced MRI

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Capar, Abdulkerim, Dursun Ali Ekinci, Mucahit Ertano, et al. "An interpretable framework for inter-observer agreement measurements in TILs scoring on histopathological breast images: A proof-of-principle study." PLOS ONE 19, no. 12 (2024): e0314450. https://doi.org/10.1371/journal.pone.0314450.

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Breast cancer, a widespread and life-threatening disease, necessitates precise diagnostic tools for improved patient outcomes. Tumor-Infiltrating Lymphocytes (TILs), reflective of the immune response against cancer cells, are pivotal in understanding breast cancer behavior. However, inter-observer variability in TILs scoring methods poses challenges to reliable assessments. This study introduces a novel and interpretable proof-of-principle framework comprising two innovative inter-observer agreement measures. The first method, Boundary-Weighted Fleiss’ Kappa (BWFK), addresses tissue segmentati

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Zhang, Xiaoxuan, Xiongfeng Zhu, Kai Tang, Yinghua Zhao, Zixiao Lu, and Qianjin Feng. "DDTNet: A dense dual-task network for tumor-infiltrating lymphocyte detection and segmentation in histopathological images of breast cancer." Medical Image Analysis 78 (May 2022): 102415. http://dx.doi.org/10.1016/j.media.2022.102415.

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Bali, Ayman, Saskia Wolter, Daniela Pelzel, et al. "Real-Time Intraoperative Decision-Making in Head and Neck Tumor Surgery: A Histopathologically Grounded Hyperspectral Imaging and Deep Learning Approach." Cancers 17, no. 10 (2025): 1617. https://doi.org/10.3390/cancers17101617.

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Background: Accurate and rapid intraoperative tumor margin assessment remains a major challenge in surgical oncology. Current gold-standard methods, such as frozen section histology, are time-consuming, operator-dependent, and prone to misclassification, which limits their clinical utility. Objective: To develop and evaluate a novel hyperspectral imaging (HSI) workflow that integrates deep learning with three-dimensional (3D) tumor modeling for real-time, label-free tumor margin delineation in head and neck squamous cell carcinoma (HNSCC). Methods: Freshly resected HNSCC samples were snap-froz

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Youbi, Mohammed Ridha, Feroui Amel, Mourad Kholkhal, and Nabil Dib. "A non-invasive approach to prostate cancer diagnosis with MRI-based feature extraction and machine learning." STUDIES IN ENGINEERING AND EXACT SCIENCES 5, no. 2 (2024): e12034. https://doi.org/10.54021/seesv5n2-761.

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Prostate cancer, a significant global health concern, requires accurate and timely diagnosis for optimal treatment planning. Gleason grading, a key prognostic factor, often relies on subjective histopathological interpretation. This research proposes a non-invasive approach to accurately predict prostate cancer Gleason grade using Magnetic Resonance Imaging (MRI) and advanced machine learning techniques. Our method involves three key steps: image segmentation, feature extraction, and classification. First, we utilize Fuzzy C-Means segmentation to accurately identify tumor regions within MRI sc

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Bundschuh, Lena, Vesna Prokic, Matthias Guckenberger, Stephanie Tanadini-Lang, Markus Essler, and Ralph A. Bundschuh. "A Novel Radiomics-Based Tumor Volume Segmentation Algorithm for Lung Tumors in FDG-PET/CT after 3D Motion Correction—A Technical Feasibility and Stability Study." Diagnostics 12, no. 3 (2022): 576. http://dx.doi.org/10.3390/diagnostics12030576.

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Positron emission tomography (PET) provides important additional information when applied in radiation therapy treatment planning. However, the optimal way to define tumors in PET images is still undetermined. As radiomics features are gaining more and more importance in PET image interpretation as well, we aimed to use textural features for an optimal differentiation between tumoral tissue and surrounding tissue to segment-target lesions based on three textural parameters found to be suitable in previous analysis (Kurtosis, Local Entropy and Long Zone Emphasis). Intended for use in radiation

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Pan, Xiaoxi, Maria E. Salvatierra, Caner Ercan, et al. "Abstract 2426: TMEseg: Connecting histopathology with spatial transcriptomics through tumor microenvironment segmentation for lung cancer." Cancer Research 85, no. 8_Supplement_1 (2025): 2426. https://doi.org/10.1158/1538-7445.am2025-2426.

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Abstract The tumor microenvironment (TME) plays a key role in lung cancer progression. Spatial transcriptomics offers insights into tumor heterogeneity but relies on integration with histological features to decode tumor transcriptomic programs, which is challenging due to TME complexity. To overcome this, we developed AI models that accurately identify 10 tissue types from histology images, streamlining TME analysis. We used public annotations to train AI models, DeepLabV3+ and Segformer, for TME segmentation. The training set included hematoxylin&eosin-stained slides from breast and lung

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Danaher, Patrick, Michael Patrick, Shanshan He, et al. "Abstract 751: High-resolution and AI-enabled single-cell spatial transcriptomics and histopathology integrated to reveal tumor differentiation and immune exclusion in skin squamous cell carcinoma." Cancer Research 85, no. 8_Supplement_1 (2025): 751. https://doi.org/10.1158/1538-7445.am2025-751.

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Abstract Skin squamous cell carcinoma (SCC) is characterized by heterogeneity in differentiation states and immune exclusion within the tumor microenvironment (TME). Using the Bruker Spatial Biology CosMx® Whole Transcriptome (WTX) panel, which profiles approximately 19, 000 genes at single-cell resolution, we examined spatial gene expression in FFPE SCC sections. Individual single cell boundaries were defined utilizing a trained AI cell segmentation model. H&E staining on the same tissue provided histopathological context, enabling the integration of molecular and morphological findings.

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Zhou, Wentong, Ziheng Deng, Yong Liu, Hui Shen, Hongwen Deng, and Hongmei Xiao. "Global Research Trends of Artificial Intelligence on Histopathological Images: A 20-Year Bibliometric Analysis." International Journal of Environmental Research and Public Health 19, no. 18 (2022): 11597. http://dx.doi.org/10.3390/ijerph191811597.

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Cancer has become a major threat to global health care. With the development of computer science, artificial intelligence (AI) has been widely applied in histopathological images (HI) analysis. This study analyzed the publications of AI in HI from 2001 to 2021 by bibliometrics, exploring the research status and the potential popular directions in the future. A total of 2844 publications from the Web of Science Core Collection were included in the bibliometric analysis. The country/region, institution, author, journal, keyword, and references were analyzed by using VOSviewer and CiteSpace. The

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Jaber, Mustafa I., Christopher W. Szeto, Bing Song, et al. "Pathology image-based lung cancer subtyping using deeplearning features and cell-density maps." Electronic Imaging 2020, no. 10 (2020): 64–1. http://dx.doi.org/10.2352/issn.2470-1173.2020.10.ipas-064.

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In this paper, we propose a patch-based system to classify non-small cell lung cancer (NSCLC) diagnostic whole slide images (WSIs) into two major histopathological subtypes: adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC). Classifying patients accurately is important for prognosis and therapy decisions. The proposed system was trained and tested on 876 subtyped NSCLC gigapixel-resolution diagnostic WSIs from 805 patients – 664 in the training set and 141 in the test set. The algorithm has modules for: 1) auto-generated tumor/non-tumor masking using a trained residual neural network (R

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Kurczyk, Agata, Marta Gawin, Piotr Paul, et al. "Prognostic Value of Molecular Intratumor Heterogeneity in Primary Oral Cancer and Its Lymph Node Metastases Assessed by Mass Spectrometry Imaging." Molecules 27, no. 17 (2022): 5458. http://dx.doi.org/10.3390/molecules27175458.

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Different aspects of intra-tumor heterogeneity (ITH), which are associated with the development of cancer and its response to treatment, have postulated prognostic value. Here we searched for potential association between phenotypic ITH analyzed by mass spectrometry imaging (MSI) and prognosis of head and neck cancer. The study involved tissue specimens resected from 77 patients with locally advanced oral squamous cell carcinoma, including 37 patients where matched samples of primary tumor and synchronous lymph node metastases were analyzed. A 3-year follow-up was available for all patients wh

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Eminaga, Okyaz, Mahmoud Abbas, Axel Semjonow, James D. Brooks, and Daniel Rubin. "Determination of biologic and prognostic feature scores from whole slide histology images using deep learning." Journal of Clinical Oncology 38, no. 15_suppl (2020): e17527-e17527. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.e17527.

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e17527 Background: In cancer, histopathology is a reflection of the underlying molecular changes in the cancer cells and provides prognostic information on the risk of disease progression. Therefore, whole slide images may harbor histopathological features that have a biological association and are prognostic. Methods: This study has extracted histopathological feature scores generated from hematoxylin and eosin (HE) histology images based on deep learning models developed for the detection of pathological findings related to prostate cancer (PCa). Correlation analyses between the histopatholo

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Milić, Marko, Šćepan Sinanović, and Tanja Prodović. "Digital pathology and bioinformatics analysis of PIT1 expression in pituitary macroadenomas." Medicinski glasnik Specijalne bolnice za bolesti štitaste žlezde i bolesti metabolizma 30, no. 97 (2025): 7–16. https://doi.org/10.5937/mgiszm2597007m.

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Introduction: Pituitary macroadenomas pose a challenge in clinical endocrinology due to their impact on hormonal balance and subsequent clinical complications. Traditional diagnostic methods often suffer from subjectivity, highlighting the need for a more objective approach. Materials and Methods: This study was conducted as a retrospective secondary analysis of publicly available, de-identified data. Digital histopathological images were obtained from a digital pathology repository, while RNA-seq data, including PIT1 gene expression, were retrieved from the NCBI GEO database. Convolutional ne

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Jung, Jiyoon, Eunsu Kim, Hyeseong Lee, Sung Hak Lee, and Sangjeong Ahn. "Automated Hybrid Model for Detecting Perineural Invasion in the Histology of Colorectal Cancer." Applied Sciences 12, no. 18 (2022): 9159. http://dx.doi.org/10.3390/app12189159.

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Perineural invasion (PNI) is a well-established independent prognostic factor for poor outcomes in colorectal cancer (CRC). However, PNI detection in CRC is a cumbersome and time-consuming process, with low inter-and intra-rater agreement. In this study, a deep-learning-based approach was proposed for detecting PNI using histopathological images. We collected 530 regions of histology from 77 whole-slide images (PNI, 100 regions; non-PNI, 430 regions) for training. The proposed hybrid model consists of two components: a segmentation network for tumor and nerve tissues, and a PNI classifier. Unl

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Liu, Yan, Fadila Zerka, Sylvain Bodard, et al. "CT based radiomics signature for phenotyping histopathological subtype in patients with non-small cell lung cancer." Journal of Clinical Oncology 41, no. 16_suppl (2023): e20599-e20599. http://dx.doi.org/10.1200/jco.2023.41.16_suppl.e20599.

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e20599 Background: The determination of the histopathology of non-small cell lung cancer (NSCLC) is crucial for guiding the appropriate therapeutic strategy, affecting prognosis and recurrence rates. In addition, targetable oncologic mutations are highly correlated to histological subtypes. Conventional methods such as biopsy or surgical excision are the primary methods for histology determination but are invasive, costly, and have limitations such as sampling error. Computed tomography (CT) scans, widely used for NSCLC diagnosis and follow-up, offer a non-invasive alternative through radiomic

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Talwar, Vineet, Kundan Singh Chufal, and Srujana Joga. "Artificial Intelligence: A New Tool in Oncologist's Armamentarium." Indian Journal of Medical and Paediatric Oncology 42, no. 06 (2021): 511–17. http://dx.doi.org/10.1055/s-0041-1735577.

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AbstractArtificial intelligence (AI) has become an essential tool in human life because of its pivotal role in communications, transportation, media, and social networking. Inspired by the complex neuronal network and its functions in human beings, AI, using computer-based algorithms and training, had been explored since the 1950s. To tackle the enormous amount of patients' clinical data, imaging, histopathological data, and the increasing pace of research on new treatments and clinical trials, and ever-changing guidelines for treatment with the advent of novel drugs and evidence, AI is the ne

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Khalil, Muhammad-Adil, Yu-Ching Lee, Huang-Chun Lien, Yung-Ming Jeng, and Ching-Wei Wang. "Fast Segmentation of Metastatic Foci in H&E Whole-Slide Images for Breast Cancer Diagnosis." Diagnostics 12, no. 4 (2022): 990. http://dx.doi.org/10.3390/diagnostics12040990.

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Breast cancer is the leading cause of death for women globally. In clinical practice, pathologists visually scan over enormous amounts of gigapixel microscopic tissue slide images, which is a tedious and challenging task. In breast cancer diagnosis, micro-metastases and especially isolated tumor cells are extremely difficult to detect and are easily neglected because tiny metastatic foci might be missed in visual examinations by medical doctors. However, the literature poorly explores the detection of isolated tumor cells, which could be recognized as a viable marker to determine the prognosis

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Grewal, Mahip, Taha Ahmed, and Ammar Asrar Javed. "Current state of radiomics in hepatobiliary and pancreatic malignancies." Artificial Intelligence Surgery 3, no. 4 (2023): 217–32. http://dx.doi.org/10.20517/ais.2023.28.

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Rising in incidence, hepatobiliary and pancreatic (HPB) cancers continue to exhibit dismal long-term survival. The overall poor prognosis of HPB cancers is reflective of the advanced stage at which most patients are diagnosed. Late diagnosis is driven by the often-asymptomatic nature of these diseases, as well as a dearth of screening modalities. Additionally, standard imaging modalities fall short of providing accurate and detailed information regarding specific tumor characteristics, which can better inform surgical planning and sequencing of systemic therapy. Therefore, precise therapeutic

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Kerkour, Thamila, Loes Hollestein, Alex Nigg, et al. "Abstract 2007: Prognostic value of immune infiltrating lymphocytes in primary cutaneous melanoma: Insights from the D-ESMEL study." Cancer Research 85, no. 8_Supplement_1 (2025): 2007. https://doi.org/10.1158/1538-7445.am2025-2007.

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Abstract Introduction: Most stage I/II cutaneous melanoma patients have favorable outcomes, while a subset experiences distant metastasis, highlighting limitations in current staging systems, which rely primarily on Breslow thickness and ulceration status. In recent years, the involvement of tumor-infiltrating lymphocytes (TILs) in the tumor microenvironment (TME) has gained attention for their prognostic significance in melanoma, while their role and reproducibility remain debated. Here, we developed and validated an artificial intelligence (AI) based model to segment histological features in

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Theocharopoulos, Charalampos, Spyridon Davakis, Dimitrios C. Ziogas, et al. "Deep Learning for Image Analysis in the Diagnosis and Management of Esophageal Cancer." Cancers 16, no. 19 (2024): 3285. http://dx.doi.org/10.3390/cancers16193285.

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Esophageal cancer has a dismal prognosis and necessitates a multimodal and multidisciplinary approach from diagnosis to treatment. High-definition white-light endoscopy and histopathological confirmation remain the gold standard for the definitive diagnosis of premalignant and malignant lesions. Artificial intelligence using deep learning (DL) methods for image analysis constitutes a promising adjunct for the clinical endoscopist that could effectively decrease BE overdiagnosis and unnecessary surveillance, while also assisting in the timely detection of dysplastic BE and esophageal cancer. A

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Lu, Di, Yupeng Cai, Liuyin Chen, et al. "Artificial intelligence-based prediction model of malignant lung nodules for preoperative planning." Journal of Clinical Oncology 42, no. 16_suppl (2024): 8034. http://dx.doi.org/10.1200/jco.2024.42.16_suppl.8034.

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8034 Background: The histopathological prediction of malignant lung nodules is crucial for preoperative planning, but it always remains not precise until the detailed pathological evaluation is performed after the surgery. Thus, to define the histology types (in situ adenocarcinoma (AIS), microinvasive adenocarcinoma (MIA), and invasive adenocarcinoma (IA)) of pulmonary adenocarcinoma appearing as lung nodules before the operation and reduce unnecessary invasive diagnosis and treatment operations, we developed a classification model of based on CT images. Methods: Patients who were diagnosed w

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Piñeiro Fiel, Manuel, Carlos Pérez Míguez, Jose Antonio Taibo Salorio, et al. "Automated Image Analysis Pipeline for Standardized Processing, Segmentation, and Feature Extraction in Diffuse Large B-Cell Lymphoma Histological Slides: Towards Enhanced Prediction of Immunotherapy Response and Risk Stratification." Blood 144, Supplement 1 (2024): 3593. https://doi.org/10.1182/blood-2024-199008.

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Introduction: Diffuse Large B-Cell Lymphoma (DLBCL), the most prevalent form of non-Hodgkin lymphoma, presents significant treatment challenges due to heterogeneous responses and the need for rapid biomarkers to anticipate risk and treatment response, particularly to immunotherapy. Traditional histopathological methods often lack the precision required to understand tumor microenvironment interactions. Recent advancements in imaging techniques combined with artificial intelligence (AI) offer promising solutions for detailed analysis. These technologies could predict risk and identify predictiv

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Rigamonti, Alessandra, Marika Viatore, Rebecca Polidori, et al. "Abstract 5783: Integration of AI-powered digital pathology and imaging mass cytometry to identify relevant features of the tumor microenvironment." Cancer Research 83, no. 7_Supplement (2023): 5783. http://dx.doi.org/10.1158/1538-7445.am2023-5783.

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Abstract Digital pathology coupled to artificial intelligence (AI)-powered approaches are receiving great attention in the oncoimmunology field, as their adoption holds promise to improve current diagnostic workflows and potentiate the analytic outputs. In this work, we aimed at combining different histopathological approaches and AI-aided analytic tools to analyze the ecosystem of tumor tissues. By deploying AI-powered standard H&E and high-dimensional imaging-mass cytometry (IMC) to FFPE tissue samples, we could extract quantitative and standardized features that couldn’t have been easil

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Kong, Qianqian, Ruilei Li, Jiaran Zhang, et al. "Annotations-free survival prediction with WSIs using graph convolutional neural networks." Journal of Clinical Oncology 42, no. 16_suppl (2024): e16501-e16501. http://dx.doi.org/10.1200/jco.2024.42.16_suppl.e16501.

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e16501 Background: Survival prediction of cancer patients has always been an challenging problem.Tumor microenvironment (TME) Analyzation based on whole-slide-images (WSIs) has provide an effective perspective for survival prediction. However, most existing TME analyzation based on cell segmentation or classification relies heavily on labor-intensive cell-level annotations of pathologists. Furthermore, except for each individual cell or local pathological feature, survival prediction also involves local-level pathological features interactions in tumor microenvironments. This requires context-

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Wu, Wei, Lauren Cech, Victor Olivas, Aubhishek Zaman, Daniel Lucas Kerr, and Trever G. Bivona. "Deep learning-based characterization of the drug tolerant persister cell state in lung cancer." JCO Global Oncology 9, Supplement_1 (2023): 141. http://dx.doi.org/10.1200/go.2023.9.supplement_1.141.

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141 Background: Lung cancer is the leading cause of cancer-related lethality globally. Targeted therapies improve the clinical outcome of cancer treatment; however, a subpopulation of cancer cells survive during initial therapy and evolve into drug tolerant persister cells (DTPs) that maintain a residual disease reservoir. Residual disease preludes acquired resistance and tumor progression; therefore, identifying and eliminating DTPs could benefit future treatment paradigms. We have shown that Hippo pathway effector YAP1 (Yes Associated Protein-1) is activated in oncogene-driven lung cancers w

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Liu, Yunhe, Ansam Sinjab, Jimin Min, et al. "Abstract 166: Spatial subtypes and cellular interactions of cancer-associated fibroblasts revealed by single-cell spatial omics." Cancer Research 85, no. 8_Supplement_1 (2025): 166. https://doi.org/10.1158/1538-7445.am2025-166.

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Abstract Background: Cancer-associated fibroblasts (CAFs) are a highly diverse cell population that plays a crucial role in shaping the tumor microenvironment (TME). Their spatial interactions within the TME remain to be systematically characterized, and the factors driving their phenotypic heterogeneity are not yet fully understood. Recent advancements in single-cell spatial transcriptomics technologies and cell segmentation approaches enable the precise measurement of thousands of transcripts at subcellular resolution. Combined with innovative bioinformatics, these advancements have unlocked

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Di Dio, Michele, Simona Barbuto, Claudio Bisegna, et al. "Artificial Intelligence-Based Hyper Accuracy Three-Dimensional (HA3D®) Models in Surgical Planning of Challenging Robotic Nephron-Sparing Surgery: A Case Report and Snapshot of the State-of-the-Art with Possible Future Implications." Diagnostics 13, no. 14 (2023): 2320. http://dx.doi.org/10.3390/diagnostics13142320.

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Recently, 3D models (3DM) gained popularity in urology, especially in nephron-sparing interventions (NSI). Up to now, the application of artificial intelligence (AI) techniques alone does not allow us to obtain a 3DM adequate to plan a robot-assisted partial nephrectomy (RAPN). Integration of AI with computer vision algorithms seems promising as it allows to speed up the process. Herein, we present a 3DM realized with the integration of AI and a computer vision approach (CVA), displaying the utility of AI-based Hyper Accuracy Three-dimensional (HA3D®) models in preoperative planning and intrao

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Zhu, Zede, Yiran Sun, and Barmak Honarvar Shakibaei Asli. "Early Breast Cancer Detection Using Artificial Intelligence Techniques Based on Advanced Image Processing Tools." Electronics 13, no. 17 (2024): 3575. http://dx.doi.org/10.3390/electronics13173575.

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The early detection of breast cancer is essential for improving treatment outcomes, and recent advancements in artificial intelligence (AI), combined with image processing techniques, have shown great potential in enhancing diagnostic accuracy. This study explores the effects of various image processing methods and AI models on the performance of early breast cancer diagnostic systems. By focusing on techniques such as Wiener filtering and total variation filtering, we aim to improve image quality and diagnostic precision. The novelty of this study lies in the comprehensive evaluation of these

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