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Articoli di riviste sul tema "Radiomics analysis"

Autore: Grafiati

Pubblicato: 14 dicembre 2024

Ultima modifica: 31 luglio 2025

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1

Hu, Shuyi, Xiajie Lyu, Weifeng Li, et al. "Radiomics Analysis on Noncontrast CT for Distinguishing Hepatic Hemangioma (HH) and Hepatocellular Carcinoma (HCC)." Contrast Media & Molecular Imaging 2022 (June 25, 2022): 1–8. http://dx.doi.org/10.1155/2022/7693631.

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Background. To form a radiomic model on the basis of noncontrast computed tomography (CT) to distinguish hepatic hemangioma (HH) and hepatocellular carcinoma (HCC). Methods. In this retrospective study, a total of 110 patients were reviewed, including 72 HCC and 38 HH. We accomplished feature selection with the least absolute shrinkage and operator (LASSO) and built a radiomics signature. Another improved model (radiomics index) was established using forward conditional multivariate logistic regression. Both models were tested in an internal validation group (38 HCC and 21 HH). Results. The ra

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Yin, Yunchao, Derya Yakar, Rudi A. J. O. Dierckx, Kim B. Mouridsen, Thomas C. Kwee, and Robbert J. de Haas. "Combining Hepatic and Splenic CT Radiomic Features Improves Radiomic Analysis Performance for Liver Fibrosis Staging." Diagnostics 12, no. 2 (2022): 550. http://dx.doi.org/10.3390/diagnostics12020550.

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Background: The exact focus of computed tomography (CT)-based artificial intelligence techniques when staging liver fibrosis is still not exactly known. This study aimed to determine both the added value of splenic information to hepatic information, and the correlation between important radiomic features and information exploited by deep learning models for liver fibrosis staging by CT-based radiomics. Methods: The study design is retrospective. Radiomic features were extracted from both liver and spleen on portal venous phase CT images of 252 consecutive patients with histologically proven l

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Xia, Zhen, Xiao-Chen Huang, Xin-Yu Xu, et al. "Ultrasound-Based Deep Learning Radiomics Models for Predicting Primary and Secondary Salivary Gland Malignancies: A Multicenter Retrospective Study." Bioengineering 12, no. 4 (2025): 391. https://doi.org/10.3390/bioengineering12040391.

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Background: Primary and secondary salivary gland malignancies differ significantly in treatment and prognosis. However, conventional ultrasonography often struggles to differentiate between these malignancies due to overlapping imaging features. We aimed to develop and evaluate noninvasive diagnostic models based on traditional ultrasound features, radiomics, and deep learning—independently or in combination—for distinguishing between primary and secondary salivary gland malignancies. Methods: This retrospective study included a total of 140 patients, comprising 68 with primary and 72 with sec

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Gelardi, Fabrizia, Lara Cavinato, Rita De Sanctis, et al. "The Predictive Role of Radiomics in Breast Cancer Patients Imaged by [18F]FDG PET: Preliminary Results from a Prospective Cohort." Diagnostics 14, no. 20 (2024): 2312. http://dx.doi.org/10.3390/diagnostics14202312.

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Background: Recently, radiomics has emerged as a possible image-derived biomarker, predominantly stemming from retrospective analyses. We aimed to prospectively assess the predictive role of [18F]FDG-PET radiomics in breast cancer (BC). Methods: Patients affected by stage I–III BC eligible for neoadjuvant chemotherapy (NAC) staged with [18F]FDG-PET/CT were prospectively enrolled. The pathological response to NAC was assessed on surgical specimens. From each primary breast lesion, we extracted radiomic PET features and their predictive role with respect to pCR was assessed. Uni- and multivariat

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Chilaca-Rosas, Maria-Fatima, Melissa Garcia-Lezama, Sergio Moreno-Jimenez, and Ernesto Roldan-Valadez. "Diagnostic Performance of Selected MRI-Derived Radiomics Able to Discriminate Progression-Free and Overall Survival in Patients with Midline Glioma and the H3F3AK27M Mutation." Diagnostics 13, no. 5 (2023): 849. http://dx.doi.org/10.3390/diagnostics13050849.

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Background: Radiomics refers to a recent area of knowledge that studies features extracted from different imaging techniques and subsequently transformed into high-dimensional data that can be associated with biological events. Diffuse midline gliomas (DMG) are one of the most devastating types of cancer, with a median survival of approximately 11 months after diagnosis and 4–5 months after radiological and clinical progression. Methods: A retrospective study. From a database of 91 patients with DMG, only 12 had the H3.3K27M mutation and brain MRI DICOM files available. Radiomic features were

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Hu, Yumin, Qiaoyou Weng, Haihong Xia, et al. "A radiomic nomogram based on arterial phase of CT for differential diagnosis of ovarian cancer." Abdominal Radiology 46, no. 6 (2021): 2384–92. http://dx.doi.org/10.1007/s00261-021-03120-w.

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Abstract Purpose To develop and validate a radiomic nomogram based on arterial phase of CT to discriminate the primary ovarian cancers (POCs) and secondary ovarian cancers (SOCs). Methods A total of 110 ovarian cancer patients in our hospital were reviewed from January 2010 to December 2018. Radiomic features based on the arterial phase of CT were extracted by Artificial Intelligence Kit software (A.K. software). The least absolute shrinkage and selection operation regression (LASSO) was employed to select features and construct the radiomics score (Rad-score) for further radiomics signature c

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7

Cinarer, Gokalp, and Bulent Gursel Emiroglu. "Statistical analysis of radiomic features in differentiation of glioma grades." New Trends and Issues Proceedings on Advances in Pure and Applied Sciences, no. 12 (April 30, 2020): 68–79. http://dx.doi.org/10.18844/gjpaas.v0i12.4988.

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Radiomics is an important quantitative feature extraction tool used in many areas such as image processing and computer-aided diagnosis. In this study, the discriminability of brain cancer tumour grades (Grade II and Grade III) with radiomic features were analysed statistically. The data set consists of 121 patients, 77 patients with Grade II tumours and 44 patients with Grade III tumours. A total of 107 radiomic features were extracted, including three groups of radiomic features such as morphological, first-order and texture. Relationships between the characteristics of each group were teste

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Hu, Lili, Jingjing Zhang, Xiaofei Wu, et al. "CT-based multi-regional radiomics model for predicting contrast medium extravasation in patients with tumors: A case-control study." PLOS ONE 20, no. 3 (2025): e0314601. https://doi.org/10.1371/journal.pone.0314601.

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Objective To develop a non-contrast CT based multi-regional radiomics model for predicting contrast medium (CM) extravasation in patients with tumors. Methods A retrospective analysis of non-contrast CT scans from 282 tumor patients across two medical centers led to the development of a radiomics model, using 157 patients for training, 68 for validation, and 57 from an external center as an independent test cohort. The different volumes of interest from right common carotid artery/right internal jugular vein, right subclavian artery/vein and thoracic aorta were delineated. Radiomics features f

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Wei, Zhi-Yao, Zhe Zhang, Dong-Li Zhao, Wen-Ming Zhao, and Yuan-Guang Meng. "Magnetic resonance imaging-based radiomics model for preoperative assessment of risk stratification in endometrial cancer." World Journal of Clinical Cases 12, no. 26 (2024): 5908–21. http://dx.doi.org/10.12998/wjcc.v12.i26.5908.

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BACKGROUND Preoperative risk stratification is significant for the management of endometrial cancer (EC) patients. Radiomics based on magnetic resonance imaging (MRI) in combination with clinical features may be useful to predict the risk grade of EC. AIM To construct machine learning models to predict preoperative risk stratification of patients with EC based on radiomics features extracted from MRI. METHODS The study comprised 112 EC patients. The participants were randomly separated into training and validation groups with a 7:3 ratio. Logistic regression analysis was applied to uncover ind

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Lei, Chu-qian, Wei Wei, Zhen-yu Liu, et al. "Radiomics analysis for pathological classification prediction in BI-RADS category 4 mammographic calcifications." Journal of Clinical Oncology 37, no. 15_suppl (2019): e13055-e13055. http://dx.doi.org/10.1200/jco.2019.37.15_suppl.e13055.

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e13055 Background: To establish and validate a radiomics-based imaging diagnostic model to predict Breast Imaging Reporting and Data System (BI-RADS) category 4 calcification of breast with mammographic images before biopsy and assess its value. Methods: A total of 212 BI-RADS category 4 pathology-proven mammographic calcifications without obvious mass on mammography were retrospectively enrolled (159 in primary cohort and 53 in validation cohort). All patients received ultrasound inspection and the results were available. 8286 radiomic features were extracted from each mammography images. We

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Kalasauskas, Darius, Michael Kosterhon, Naureen Keric, et al. "Beyond Glioma: The Utility of Radiomic Analysis for Non-Glial Intracranial Tumors." Cancers 14, no. 3 (2022): 836. http://dx.doi.org/10.3390/cancers14030836.

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The field of radiomics is rapidly expanding and gaining a valuable role in neuro-oncology. The possibilities related to the use of radiomic analysis, such as distinguishing types of malignancies, predicting tumor grade, determining the presence of particular molecular markers, consistency, therapy response, and prognosis, can considerably influence decision-making in medicine in the near future. Even though the main focus of radiomic analyses has been on glial CNS tumors, studies on other intracranial tumors have shown encouraging results. Therefore, as the main focus of this review, we perfor

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Huang, Yen-Cho, Shih-Ming Huang, Jih-Hsiang Yeh, et al. "Utility of CT Radiomics and Delta Radiomics for Survival Evaluation in Locally Advanced Nasopharyngeal Carcinoma with Concurrent Chemoradiotherapy." Diagnostics 14, no. 9 (2024): 941. http://dx.doi.org/10.3390/diagnostics14090941.

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Background: A high incidence rate of nasopharyngeal carcinoma (NPC) has been observed in Southeast Asia compared to other parts of the world. Radiomics is a computational tool to predict outcomes and may be used as a prognostic biomarker for advanced NPC treated with concurrent chemoradiotherapy. Recently, radiomic analysis of the peripheral tumor microenvironment (TME), which is the region surrounding the gross tumor volume (GTV), has shown prognostic usefulness. In this study, not only was gross tumor volume (GTVt) analyzed but also tumor peripheral regions (GTVp) were explored in terms of t

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S, Saroh, Saikiran Pendem, Prakashini K, et al. "Machine learning based radiomics approach for outcome prediction of meningioma – a systematic review." F1000Research 14 (March 25, 2025): 330. https://doi.org/10.12688/f1000research.162306.1.

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Introduction Meningioma is the most common brain tumor in adults. Magnetic resonance imaging (MRI) is the preferred imaging modality for assessing tumor outcomes. Radiomics, an advanced imaging technique, assesses tumor heterogeneity and identifies predictive markers, offering a non-invasive alternative to biopsies. Machine learning (ML) based radiomics models enhances diagnostic and prognostic accuracy of tumors. Comprehensive review on ML-based radiomics models for predicting meningioma recurrence and survival are lacking. Hence, the aim of the study is to summarize the performance measures

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Harrison, Rebecca, Bryce Wei Quan Tan, Hong Qi Tan, et al. "NIMG-32. THE PREDICTIVE CAPACITY OF PRE-OPERATIVE IMAGING ANALYSIS IN DIFFUSE GLIOMA: A COMPARISON OF CONNECTOMICS, RADIOMICS, AND CLINICAL PREDICTIVE MODELS." Neuro-Oncology 22, Supplement_2 (2020): ii154—ii155. http://dx.doi.org/10.1093/neuonc/noaa215.645.

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Abstract BACKGROUND Radiomics and connectome analysis are distinct and non-invasive methods of deriving biologic information from MRI. Radiomics analyzes features intrinsic to the tumor, and connectomics incorporates data regarding the tumor and surrounding neural circuitry. In this study we used both techniques to predict glioma survival. METHODS We retrospectively identified 305 adult patients with histopathologically confirmed WHO grade II–IV gliomas who had presurgical, 3D, T1-weighted brain MRI. Available clinical variables included tumor lobe, hemisphere, multifocal nature grade, histolo

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Gangil, Tarun, Krishna Sharan, B. Dinesh Rao, Krishnamoorthy Palanisamy, Biswaroop Chakrabarti, and Rajagopal Kadavigere. "Utility of adding Radiomics to clinical features in predicting the outcomes of radiotherapy for head and neck cancer using machine learning." PLOS ONE 17, no. 12 (2022): e0277168. http://dx.doi.org/10.1371/journal.pone.0277168.

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Background Radiomics involves the extraction of quantitative information from annotated Computed-Tomography (CT) images, and has been used to predict outcomes in Head and Neck Squamous Cell Carcinoma (HNSCC). Subjecting combined Radiomics and Clinical features to Machine Learning (ML) could offer better predictions of clinical outcomes. This study is a comparative performance analysis of ML models with Clinical, Radiomics, and Clinico-Radiomic datasets for predicting four outcomes of HNSCC treated with Curative Radiation Therapy (RT): Distant Metastases, Locoregional Recurrence, New Primary, a

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Chiu, Hwa-Yen, Ting-Wei Wang, Ming-Sheng Hsu, et al. "Progress in Serial Imaging for Prognostic Stratification of Lung Cancer Patients Receiving Immunotherapy: A Systematic Review and Meta-Analysis." Cancers 16, no. 3 (2024): 615. http://dx.doi.org/10.3390/cancers16030615.

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Immunotherapy, particularly with checkpoint inhibitors, has revolutionized non-small cell lung cancer treatment. Enhancing the selection of potential responders is crucial, and researchers are exploring predictive biomarkers. Delta radiomics, a derivative of radiomics, holds promise in this regard. For this study, a meta-analysis was conducted that adhered to PRISMA guidelines, searching PubMed, Embase, Web of Science, and the Cochrane Library for studies on the use of delta radiomics in stratifying lung cancer patients receiving immunotherapy. Out of 223 initially collected studies, 10 were i

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Wang, Yong, Liang Zhang, Lin Qi, et al. "Machine Learning: Applications and Advanced Progresses of Radiomics in Endocrine Neoplasms." Journal of Oncology 2021 (October 11, 2021): 1–17. http://dx.doi.org/10.1155/2021/8615450.

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Endocrine neoplasms remain a great threat to human health. It is extremely important to make a clear diagnosis and timely treatment of endocrine tumors. Machine learning includes radiomics, which has long been utilized in clinical cancer research. Radiomics refers to the extraction of valuable information by analyzing a large amount of standard data with high-throughput medical images mainly including computed tomography, positron emission tomography, magnetic resonance imaging, and ultrasound. With the quantitative imaging analysis and model building, radiomics can reflect specific underlying

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Lee, Hyunjong, Seung Hwan Moon, Jung Yong Hong, Jeeyun Lee, and Seung Hyup Hyun. "A Machine Learning Approach Using FDG PET-Based Radiomics for Prediction of Tumor Mutational Burden and Prognosis in Stage IV Colorectal Cancer." Cancers 15, no. 15 (2023): 3841. http://dx.doi.org/10.3390/cancers15153841.

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Introduction: We assessed the performance of F-18 fluorodeoxyglucose positron emission tomography (FDG PET)-based radiomics for the prediction of tumor mutational burden (TMB) and prognosis using a machine learning (ML) approach in patients with stage IV colorectal cancer (CRC). Methods: Ninety-one CRC patients who underwent pretreatment FDG PET/computed tomography (CT) and palliative chemotherapy were retrospectively included. PET-based radiomics were extracted from the primary tumor on PET imaging using the software LIFEx. For feature selection, PET-based radiomics associated with TMB were s

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Sun, Zongqiong, Linfang Jin, Shuai Zhang, Shaofeng Duan, Wei Xing, and Shudong Hu. "Preoperative prediction for lauren type of gastric cancer: A radiomics nomogram analysis based on CT images and clinical features." Journal of X-Ray Science and Technology 29, no. 4 (2021): 675–86. http://dx.doi.org/10.3233/xst-210888.

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PURPOSE: To investigate feasibility of predicting Lauren type of gastric cancer based on CT radiomics nomogram before operation. MATERIALS AND METHODS: The clinical data and pre-treatment CT images of 300 gastric cancer patients with Lauren intestinal or diffuse type confirmed by postoperative pathology were retrospectively analyzed, who were randomly divided into training set and testing set with a ratio of 2:1. Clinical features were compared between the two Lauren types in the training set and testing set, respectively. Gastric tumors on CT images were manually segmented using ITK-SNAP soft

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Miccò, Maura, Benedetta Gui, Luca Russo, et al. "Preoperative Tumor Texture Analysis on MRI for High-Risk Disease Prediction in Endometrial Cancer: A Hypothesis-Generating Study." Journal of Personalized Medicine 12, no. 11 (2022): 1854. http://dx.doi.org/10.3390/jpm12111854.

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Objective: To develop and validate magnetic resonance (MR) imaging-based radiomics models for high-risk endometrial cancer (EC) prediction preoperatively, to be able to estimate deep myometrial invasion (DMI) and lymphovascular space invasion (LVSI), and to discriminate between low-risk and other categories of risk as proposed by ESGO/ESTRO/ESP (European Society of Gynaecological Oncology—European Society for Radiotherapy & Oncology and European Society of Pathology) guidelines. Methods: This retrospective study included 96 women with EC who underwent 1.5-T MR imaging before surgical stagi

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Proskura, Alexandra V., Khalil M. Ismailov, Alexander G. Smoleevskiy, Amina I. Salpagarova, Irina N. Bobkova, and Andrei M. Shestiuk. "Radiomics capabilities in the interpretation of ultrasound and CT data in patients with chronic kidney disease: A review." Terapevticheskii arkhiv 97, no. 6 (2025): 503–8. https://doi.org/10.26442/00403660.2025.06.203259.

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The purpose of this review is to explore the possibilities of radiomics in interpreting ultrasound and multislice spiral computed tomography data in patients with chronic kidney disease (CKD). Radiomics is a promising area of medical image analysis based on the extraction of quantitative features not available in standard visual analysis and the subsequent use of artificial intelligence methods for their processing and interpretation. The article discusses the basics of radiomic methods, including texture analysis of images and the creation of diagnostic models using machine learning algorithm

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Hotton, Judicael, Arnaud Beddok, Abdenasser Moubtakir, Dimitri Papathanassiou, and David Morland. "[18F]FDG PET/CT Radiomics in Cervical Cancer: A Systematic Review." Diagnostics 15, no. 1 (2024): 65. https://doi.org/10.3390/diagnostics15010065.

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Background/Objectives: Cervical cancer is a significant global health concern, with high incidence and mortality rates, especially in less-developed regions. [18F]FDG PET/CT is now indicated at various stages of management, but its analysis is essentially based on SUVmax, a measure of [18F]FDG uptake. Radiomics, by extracting a multitude of parameters, promises to improve the diagnostic and prognostic performance of the examination. However, studies remain heterogeneous, both in terms of patient numbers and methods, so a synthesis is needed. Methods: This systematic review was conducted follow

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Gill, Andrew B., Leonardo Rundo, Jonathan C. M. Wan, et al. "Correlating Radiomic Features of Heterogeneity on CT with Circulating Tumor DNA in Metastatic Melanoma." Cancers 12, no. 12 (2020): 3493. http://dx.doi.org/10.3390/cancers12123493.

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Clinical imaging methods, such as computed tomography (CT), are used for routine tumor response monitoring. Imaging can also reveal intratumoral, intermetastatic, and interpatient heterogeneity, which can be quantified using radiomics. Circulating tumor DNA (ctDNA) in the plasma is a sensitive and specific biomarker for response monitoring. Here we evaluated the interrelationship between circulating tumor DNA mutant allele fraction (ctDNAmaf), obtained by targeted amplicon sequencing and shallow whole genome sequencing, and radiomic measurements of CT heterogeneity in patients with stage IV me

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Stoyanova, Radka, Olmo Zavala-Romero, Deukwoo Kwon, et al. "Clinical-Genomic Risk Group Classification of Suspicious Lesions on Prostate Multiparametric-MRI." Cancers 15, no. 21 (2023): 5240. http://dx.doi.org/10.3390/cancers15215240.

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The utilization of multi-parametric MRI (mpMRI) in clinical decisions regarding prostate cancer patients’ management has recently increased. After biopsy, clinicians can assess risk using National Comprehensive Cancer Network (NCCN) risk stratification schema and commercially available genomic classifiers, such as Decipher. We built radiomics-based models to predict lesions/patients at low risk prior to biopsy based on an established three-tier clinical-genomic classification system. Radiomic features were extracted from regions of positive biopsies and Normally Appearing Tissues (NAT) on T2-w

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Chilaca-Rosas, Maria-Fatima, Manuel-Tadeo Contreras-Aguilar, Melissa Garcia-Lezama, et al. "Identification of Radiomic Signatures in Brain MRI Sequences T1 and T2 That Differentiate Tumor Regions of Midline Gliomas with H3.3K27M Mutation." Diagnostics 13, no. 16 (2023): 2669. http://dx.doi.org/10.3390/diagnostics13162669.

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Background: Radiomics refers to the acquisition of traces of quantitative features that are usually non-perceptible to human vision and are obtained from different imaging techniques and subsequently transformed into high-dimensional data. Diffuse midline gliomas (DMG) represent approximately 20% of pediatric CNS tumors, with a median survival of less than one year after diagnosis. We aimed to identify which radiomics can discriminate DMG tumor regions (viable tumor and peritumoral edema) from equivalent midline normal tissue (EMNT) in patients with the positive H3.F3K27M mutation, which is as

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Park, Jung Ho, Lyo Min Kwon, Hong Kyu Lee, et al. "Radiomic Analysis of Magnetic Resonance Imaging for Breast Cancer with TP53 Mutation: A Single Center Study." Diagnostics 15, no. 4 (2025): 428. https://doi.org/10.3390/diagnostics15040428.

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Background: Radiomics is a non-invasive and cost-effective method for predicting the biological characteristics of tumors. In this study, we explored the association between radiomic features derived from magnetic resonance imaging (MRI) and genetic alterations in patients with breast cancer. Methods: We reviewed electronic medical records of patients with breast cancer patients with available targeted next-generation sequencing data available between August 2018 and May 2021. Substraction imaging of T1-weighted sequences was utilized. The tumor area on MRI was segmented semi-automatically, ba

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Magnin, Cheryl Y., David Lauer, Michael Ammeter, and Janine Gote-Schniering. "From images to clinical insights: an educational review on radiomics in lung diseases." Breathe 21, no. 1 (2025): 230225. https://doi.org/10.1183/20734735.0225-2023.

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Radiological imaging is a cornerstone in the clinical workup of lung diseases. Radiomics represents a significant advancement in clinical lung imaging, offering a powerful tool to complement traditional qualitative image analysis. Radiomic features are quantitative and computationally describe shape, intensity, texture and wavelet characteristics from medical images that can uncover detailed and often subtle information that goes beyond the visual capabilities of radiological examiners. By extracting this quantitative information, radiomics can provide deep insights into the pathophysiology of

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Costa, Guido, Lara Cavinato, Chiara Masci, et al. "Virtual Biopsy for Diagnosis of Chemotherapy-Associated Liver Injuries and Steatohepatitis: A Combined Radiomic and Clinical Model in Patients with Colorectal Liver Metastases." Cancers 13, no. 12 (2021): 3077. http://dx.doi.org/10.3390/cancers13123077.

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Non-invasive diagnosis of chemotherapy-associated liver injuries (CALI) is still an unmet need. The present study aims to elucidate the contribution of radiomics to the diagnosis of sinusoidal dilatation (SinDil), nodular regenerative hyperplasia (NRH), and non-alcoholic steatohepatitis (NASH). Patients undergoing hepatectomy for colorectal metastases after chemotherapy (January 2018-February 2020) were retrospectively analyzed. Radiomic features were extracted from a standardized volume of non-tumoral liver parenchyma outlined in the portal phase of preoperative post-chemotherapy computed tom

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Lucia, François, Vincent Bourbonne, Dimitris Visvikis, et al. "Radiomics Analysis of 3D Dose Distributions to Predict Toxicity of Radiotherapy for Cervical Cancer." Journal of Personalized Medicine 11, no. 5 (2021): 398. http://dx.doi.org/10.3390/jpm11050398.

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Standard treatment for locally advanced cervical cancer (LACC) is chemoradiotherapy followed by brachytherapy. Despite radiation therapy advances, the toxicity rate remains significant. In this study, we compared the prediction of toxicity events after radiotherapy for locally advanced cervical cancer (LACC), based on either dose-volume histogram (DVH) parameters or the use of a radiomics approach applied to dose maps at the voxel level. Toxicity scores using the Common Terminology Criteria for Adverse Events (CTCAE v4), spatial dose distributions, and usual clinical predictors for the toxicit

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Baine, Michael, Justin Burr, Qian Du, et al. "The Potential Use of Radiomics with Pre-Radiation Therapy MR Imaging in Predicting Risk of Pseudoprogression in Glioblastoma Patients." Journal of Imaging 7, no. 2 (2021): 17. http://dx.doi.org/10.3390/jimaging7020017.

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Glioblastoma (GBM) is the most common adult glioma. Differentiating post-treatment effects such as pseudoprogression from true progression is paramount for treatment. Radiomics has been shown to predict overall survival and MGMT (methylguanine-DNA methyltransferase) promoter status in those with GBM. A potential application of radiomics is predicting pseudoprogression on pre-radiotherapy (RT) scans for patients with GBM. A retrospective review was performed with radiomic data analyzed using pre-RT MRI scans. Pseudoprogression was defined as post-treatment findings on imaging that resolved with

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Müller, Dominik, Jakob Christoph Voran, Mário Macedo, et al. "Assessing Patient Health Dynamics by Comparative CT Analysis: An Automatic Approach to Organ and Body Feature Evaluation." Diagnostics 14, no. 23 (2024): 2760. https://doi.org/10.3390/diagnostics14232760.

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Background/Objectives: The integration of machine learning into the domain of radiomics has revolutionized the approach to personalized medicine, particularly in oncology. Our research presents RadTA (RADiomics Trend Analysis), a novel framework developed to facilitate the automatic analysis of quantitative imaging biomarkers (QIBs) from time-series CT volumes. Methods: RadTA is designed to bridge a technical gap for medical experts and enable sophisticated radiomic analyses without deep learning expertise. The core of RadTA includes an automated command line interface, streamlined image segme

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Wei, JingWei, Jie Tian, Sirui Fu, and Ligong Lu. "Noninvasive prediction of future macrovascular invasion occurrence in hepatocellular carcinoma based on quantitative imaging analysis: A multi-center study." Journal of Clinical Oncology 37, no. 15_suppl (2019): e14623-e14623. http://dx.doi.org/10.1200/jco.2019.37.15_suppl.e14623.

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e14623 Background: To investigate whether preoperative imaging-based analysis could help to predict future macrovascular invasion (MaVI) occurrence in hepatocellular carcinoma (HCC). Methods: A cohort of 224 patients with HCC was enrolled from five independent medical centers (training cohort: n = 154; independent validation cohort: n = 70). Predictive clinical factors were primarily selected by uni- and multi-variable analysis. CT-based imaging analysis was performed based on extraction of 1217 radiomic features. Recursive feature elimination and random forest (RF) were chosen as the optimal

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Zhang, Junjie, Ligang Hao, Min Li, Qian Xu, and Gaofeng Shi. "CT Radiomics Combined With Clinicopathological Features to Predict Invasive Mucinous Adenocarcinoma in Patients With Lung Adenocarcinoma." Technology in Cancer Research & Treatment 22 (January 2023): 153303382311743. http://dx.doi.org/10.1177/15330338231174306.

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Objective: This study aimed to develop and validate predictive models using clinical parameters, radiomic features, and a combination of both for invasive mucinous adenocarcinoma (IMA) of the lung in patients with lung adenocarcinoma. Method: A total of 173 and 391 patients with IMA and non-IMA, respectively, were retrospectively analyzed from January 2017 to September 2022 in our hospital. Propensity Score Matching was used to match the 2 groups of patients. A total of 1037 radiomic features were extracted from contrast-enhanced computed tomography (CT). The patients were randomly divided int

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Solopova, A. E., J. V. Nosova, and B. B. Bendzhenova. "Magnetic resonance imaging in cervical cancer: current opportunities of radiomics analysis and prospects for its further developmen." Obstetrics, Gynecology and Reproduction 17, no. 4 (2023): 500–511. http://dx.doi.org/10.17749/2313-7347/ob.gyn.rep.2023.440.

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Introduction. Due to the dynamic development of modern imaging technologies in recent years, much attention has been paid to radiomics particularly texture analysis. The complexity of clinically evaluated tumor procession in cervical cancer (CC) accounts for a need to expand knowledge on applying medical imaging technologies in oncologic diagnostics spanning from predominantly qualitative analysis to a multiparametric approach, including a quantitative assessment of study parameters.Aim: to analyze the literature data on the use of radiomics and image texture analysis in diagnostics and predic

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Abdurixiti, Meilinuer, Mayila Nijiati, Rongfang Shen, Qiu Ya, Naibijiang Abuduxiku, and Mayidili Nijiati. "Current progress and quality of radiomic studies for predicting EGFR mutation in patients with non-small cell lung cancer using PET/CT images: a systematic review." British Journal of Radiology 94, no. 1122 (2021): 20201272. http://dx.doi.org/10.1259/bjr.20201272.

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Objectives: To assess the methodological quality of radiomic studies based on positron emission tomography/computed tomography (PET/CT) images predicting epidermal growth factor receptor (EGFR) mutation status in patients with non-small cell lung cancer (NSCLC). Methods: We systematically searched for eligible studies in the PubMed and Web of Science datasets using the terms “radiomics”, “PET/CT”, “NSCLC”, and “EGFR”. The included studies were screened by two reviewers independently. The quality of the radiomic workflow of studies was assessed using the Radiomics Quality Score (RQS). Interclas

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Jiang, Yan-Wei, Xiong-Jie Xu, Rui Wang, and Chun-Mei Chen. "Radiomics analysis based on lumbar spine CT to detect osteoporosis." European Radiology, April 30, 2022. http://dx.doi.org/10.1007/s00330-022-08805-4.

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Abstract Objectives Undiagnosed osteoporosis may lead to severe complications after spinal surgery. This study aimed to construct and validate a radiomic signature based on CT scans to screen for lumbar spine osteoporosis. Methods Using a stratified random sample method, 386 vertebral bodies were randomly divided into a training set (n = 270) and a test set (n = 116). A total of 1040 radiomics features were automatically retracted from lumbar spine CT scans using the 3D slicer pyradiomics module, and a radiomic signature was created. The sensitivity, specificity, accuracy, and area under the r

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Wu, Hongyu, Ban Luo, Yali Zhao, et al. "Radiomics analysis of the optic nerve for detecting dysthyroid optic neuropathy, based on water-fat imaging." Insights into Imaging 13, no. 1 (2022). http://dx.doi.org/10.1186/s13244-022-01292-7.

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Abstract Objective Detecting dysthyroid optic neuropathy (DON) in the early stages is vital for clinical decision-making. The aim of this study was to determine the feasibility of using an optic-nerve-based radiomics nomogram on water-fat imaging for detecting DON. Methods This study included 104 orbits (83 in the training cohort) from 59 DON patients and 131 orbits (80 in the training cohort) from 69 thyroid-associated ophthalmopathy (TAO) without DON patients. Radiomic features were extracted from the optic-nerve T2-weighted water-fat images for each patient. Selected radiomics features were

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Santinha, João, Daniel Pinto dos Santos, Fabian Laqua, et al. "ESR Essentials: radiomics—practice recommendations by the European Society of Medical Imaging Informatics." European Radiology, October 25, 2024. http://dx.doi.org/10.1007/s00330-024-11093-9.

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Abstract Radiomics is a method to extract detailed information from diagnostic images that cannot be perceived by the naked eye. Although radiomics research carries great potential to improve clinical decision-making, its inherent methodological complexities make it difficult to comprehend every step of the analysis, often causing reproducibility and generalizability issues that hinder clinical adoption. Critical steps in the radiomics analysis and model development pipeline—such as image, application of image filters, and selection of feature extraction parameters—can greatly affect the value

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Wang, Yixin, Zongtao Hu, and Hongzhi Wang. "The clinical implications and interpretability of computational medical imaging (radiomics) in brain tumors." Insights into Imaging 16, no. 1 (2025). https://doi.org/10.1186/s13244-025-01950-6.

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Abstract Radiomics has widespread applications in the field of brain tumor research. However, radiomic analyses often function as a ‘black box’ due to their use of complex algorithms, which hinders the translation of brain tumor radiomics into clinical applications. In this review, we will elaborate extensively on the application of radiomics in brain tumors. Additionally, we will address the interpretability of handcrafted-feature radiomics and deep learning-based radiomics by integrating biological domain knowledge of brain tumors with interpretability methods. Furthermore, we will discuss t

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Cai, Du, Xin Duan, Wei Wang, et al. "A Metabolism-Related Radiomics Signature for Predicting the Prognosis of Colorectal Cancer." Frontiers in Molecular Biosciences 7 (January 7, 2021). http://dx.doi.org/10.3389/fmolb.2020.613918.

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Background: Radiomics refers to the extraction of a large amount of image information from medical images, which can provide decision support for clinicians. In this study, we developed and validated a radiomics-based nomogram to predict the prognosis of colorectal cancer (CRC).Methods: A total of 381 patients with colorectal cancer (primary cohort: n = 242; validation cohort: n = 139) were enrolled and radiomic features were extracted from the vein phase of preoperative computed tomography (CT). The radiomics score was generated by using the least absolute shrinkage and selection operator alg

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Mou, Meiyan, Ruizhi Gao, Yuquan Wu, et al. "Endoscopic Rectal Ultrasound‐Based Radiomics Analysis for the Prediction of Synchronous Liver Metastasis in Patients With Primary Rectal Cancer." Journal of Ultrasound in Medicine, November 11, 2023. http://dx.doi.org/10.1002/jum.16369.

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ObjectivesTo develop and validate an ultrasound‐based radiomics model to predict synchronous liver metastases (SLM) in rectal cancer (RC) patients preoperatively.MethodsTwo hundred and thirty‐nine RC patients were included in this study and randomly divided into training and validation cohorts. A total of 5936 radiomics features were calculated on the basis of ultrasound images to build a radiomic model and obtain a radiomics score (Rad‐score) using logistic regression. Meanwhile, clinical characteristics were collected to construct a clinical model. The radiomics–clinical model was developed

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Li, Xinhua, Minping Hong, Zhendong Lu, Zilin Liu, Lifu Lin, and Hongfa Xu. "Radiomics models to predict axillary lymph node metastasis in breast cancer and analysis of the biological significance of radiomic features." Frontiers in Oncology 15 (June 19, 2025). https://doi.org/10.3389/fonc.2025.1546229.

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ObjectivesTo explore the effectiveness of radiomics in predicting axillary lymph node metastasis (ALNM) and the relationship between radiomics features and genes.MethodThe 379 patients with breast cancer (186 ALNM-positive and 193 ALNM-negative) recruited from three hospitals were divided into the training (n=224), testing (n=96), and validation (n=59) cohorts. The Cancer Imaging Archive-The Cancer Genome Atlas (TCIA-TCGA) group included 107 patients with breast cancer. A total of 1888 intratumoral and peritumoral radiomics features were extracted from DCE-MRI sequences. Radiomics models were

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Liu, Wangmi, Xiaxuan Zhang, Ruofu Tang, Chengcheng Yu, and Guofang Sun. "Radiomics analysis based on plain X-rays to detect spinal fractures with posterior wall injury." DIGITAL HEALTH 11 (January 2025). https://doi.org/10.1177/20552076251324436.

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Purpose Spinal fractures, particularly those involving posterior wall injury, pose a heightened risk of instability and significantly influence treatment strategies. This study aimed to improve early diagnosis and treatment planning for spinal fractures through radiomics analysis based on plain X-ray imaging. Methods This retrospective study analyzed plain X-rays of patients with spinal fractures at the thoracolumbar junction. Radiomic features were extracted from both anteroposterior and lateral plain spine radiographs to evaluate the utility of radiomics in detecting posterior wall injury. D

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Shaheen, Asma, Syed Talha Bukhari, Maria Nadeem, Stefano Burigat, Ulas Bagci, and Hassan Mohy-ud-Din. "Overall Survival Prediction of Glioma Patients With Multiregional Radiomics." Frontiers in Neuroscience 16 (July 7, 2022). http://dx.doi.org/10.3389/fnins.2022.911065.

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Radiomics-guided prediction of overall survival (OS) in brain gliomas is seen as a significant problem in Neuro-oncology. The ultimate goal is to develop a robust MRI-based approach (i.e., a radiomics model) that can accurately classify a novel subject as a short-term survivor, a medium-term survivor, or a long-term survivor. The BraTS 2020 challenge provides radiological imaging and clinical data (178 subjects) to develop and validate radiomics-based methods for OS classification in brain gliomas. In this study, we empirically evaluated the efficacy of four multiregional radiomic models, for

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Li, Yue, Huaibi Huo, Hui Liu, et al. "Coronary CTA-based radiomic signature of pericoronary adipose tissue predict rapid plaque progression." Insights into Imaging 15, no. 1 (2024). http://dx.doi.org/10.1186/s13244-024-01731-7.

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Abstract Objectives To explore the value of radiomic features derived from pericoronary adipose tissue (PCAT) obtained by coronary computed tomography angiography for prediction of coronary rapid plaque progression (RPP). Methods A total of 1233 patients from two centers were included in this multicenter retrospective study. The participants were divided into training, internal validation, and external validation cohorts. Conventional plaque characteristics and radiomic features of PCAT were extracted and analyzed. Random Forest was used to construct five models. Model 1: clinical model. Model

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Yang, Qinzhu, Haofan Huang, Guizhi Zhang, et al. "Contrast‐enhanced CT‐based radiomic analysis for determining the response to anti‐programmed death‐1 therapy in esophageal squamous cell carcinoma patients: A pilot study." Thoracic Cancer, September 24, 2023. http://dx.doi.org/10.1111/1759-7714.15117.

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AbstractBackgroundIn view of the fact that radiomics features have been reported as predictors of immunotherapy to various cancers, this study aimed to develop a prediction model to determine the response to anti‐programmed death‐1 (anti‐PD‐1) therapy in esophageal squamous cell carcinoma (ESCC) patients from contrast‐enhanced CT (CECT) radiomics features.MethodsRadiomic analysis of images was performed retrospectively for image samples before and after anti‐PD‐1 treatment, and efficacy analysis was performed for the results of two different time node evaluations. A total of 68 image samples w

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Li, Mei hua, Long Liu, Lian Feng, et al. "Prediction of cervical lymph node metastasis in solitary papillary thyroid carcinoma based on ultrasound radiomics analysis." Frontiers in Oncology 14 (January 25, 2024). http://dx.doi.org/10.3389/fonc.2024.1291767.

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ObjectiveTo assess the utility of predictive models using ultrasound radiomic features to predict cervical lymph node metastasis (CLNM) in solitary papillary thyroid carcinoma (PTC) patients.MethodsA total of 570 PTC patients were included (456 patients in the training set and 114 in the testing set). Pyradiomics was employed to extract radiomic features from preoperative ultrasound images. After dimensionality reduction and meticulous selection, we developed radiomics models using various machine learning algorithms. Univariate and multivariate logistic regressions were conducted to identify

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Hapaer, Gulizaina, Feng Che, Qing Xu, et al. "Radiomics-based biomarker for PD-1 status and prognosis analysis in patients with HCC." Frontiers in Immunology 16 (January 29, 2025). https://doi.org/10.3389/fimmu.2025.1435668.

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PurposeTo investigate the impact of preoperative contrast-enhanced CT-based radiomics model on PD-1 prediction in hepatocellular carcinoma (HCC) patients.MethodsThe study included 105 HCC patients (training cohort: 72; validation cohort: 33) who underwent preoperative contrast-enhanced CT and received systemic sorafenib treatment after surgery. Radiomics score was built for each patient and was integrated with independent clinic radiologic predictors into the radiomics model using multivariable logistic regression analysis.ResultsSeventeen radiomics features were finally selected to construct

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Meng, Huan, Tian-Da Wang, Li-Yong Zhuo, et al. "Quantitative radiomics analysis of imaging features in adults and children Mycoplasma pneumonia." Frontiers in Medicine 11 (May 20, 2024). http://dx.doi.org/10.3389/fmed.2024.1409477.

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PurposeThis study aims to explore the value of clinical features, CT imaging signs, and radiomics features in differentiating between adults and children with Mycoplasma pneumonia and seeking quantitative radiomic representations of CT imaging signs.Materials and methodsIn a retrospective analysis of 981 cases of mycoplasmal pneumonia patients from November 2021 to December 2023, 590 internal data (adults:450, children: 140) randomly divided into a training set and a validation set with an 8:2 ratio and 391 external test data (adults:121; children:270) were included. Using univariate analysis,

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Wang, Jincheng, Shengnan Tang, Jin Wu, et al. "Radiomic features at Contrast-enhanced CT Predict Virus-driven Liver Fibrosis: A Multi-institutional Study." Clinical and Translational Gastroenterology, May 27, 2024. http://dx.doi.org/10.14309/ctg.0000000000000712.

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Background: Liver fibrosis is a major cause of morbidity and mortality among in chronic hepatitis patients. Radiomics, particularly of the spleen, may improve diagnostic accuracy and treatment strategies. External validations are necessary to ensure reliability and generalizability. Methods: In this retrospective study, we developed three radiomics models using contrast-enhanced CT scans from 167 patients with liver fibrosis (training group) between January 2020 and December 2021. Radiomic features were extracted from arterial venous, portal venous, and equilibrium phase images. Recursive feat

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