ArticleCancer imaging : the official publication of the International Cancer Imaging Society2021
MRI-based radiomics models to assess prostate cancer, extracapsular extension and positive surgical margins.
Article in Cancer imaging : the official publication of the International Cancer Imaging Society, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 3 of them syntheses that pooled it.
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Who cites it
47 citing papers in PubMed, 3 syntheses or guidelines pooled it, 68 citations in OpenAlex.
- Machine learning-based MRI imaging for prostate cancer diagnosis: systematic review and meta-analysis.Prostate cancer and prostatic diseases · 2026Pooled it
- Artificial Intelligence-Enabled Imaging for Predicting Preoperative Extraprostatic Extension in Prostate Cancer: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Radiomics for the identification of extraprostatic extension with prostate MRI: a systematic review and meta-analysis.European radiology · 2024Pooled it
- Optimized Biopsy Strategy for Transition Zone Prostate Cancer With Enhanced Perilesional Sampling: A Retrospective Analysis and Clinical Trial Validation.JCO precision oncology · 2025Trial
- Exploring the feasibility of inferring prostate cancer pathological grade from multiparametric MRI text reports using natural language processing: assessment of four large language models.Abdominal radiology (New York) · 2026Article
- Preoperative prediction of positive surgical margins in prostate cancer using multimodal deep learning model: a multicenter study.Abdominal radiology (New York) · 2026Article
- MRI-Derived Biomarkers and Radiomic Signatures for Early, Dose-Dependent Evaluation of Prostate Cancer Radiotherapy: An Exploratory Study.Journal of imaging · 2026Article
- Preoperative dual-ROI mpMRI radiomics integrating intratumoral and periprostatic adipose tissue features with clinical factors for predicting positive surgical margins after radical prostatectomy.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Review
- From AI-based image analysis to surgical decision support in prostate cancer: interdisciplinary application of the international radiomics platform.Frontiers in oncology · 2026Article
- Automated machine learning for prostate cancer detection and Gleason score prediction using T2WI: a diagnostic multi-center study.BMC cancer · 2025Article
- Combining multi-parametric MRI radiomics features with tumor abnormal protein to construct a machine learning-based predictive model for prostate cancer.Scientific reports · 2025Article
- Using radiomics model for predicting extraprostatic extension with PSMA PET/CT studies: a comparative study with the Mehralivand grading system.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Multiparametric MRI lesion dimension as a significant predictor of positive surgical margins following laparoscopic radical prostatectomy for transitional zone prostate cancer.World journal of urology · 2025Article
- Clinical-radiomics hybrid modeling outperforms conventional models: machine learning enhances stratification of adverse prognostic features in prostate cancer.Frontiers in oncology · 2025Article
- AutoRadAI: a versatile artificial intelligence framework validated for detecting extracapsular extension in prostate cancer.Biology methods & protocols · 2025Article
- Interpretable multiparametric MRI radiomics-based machine learning model for preoperative differentiation between benign and malignant prostate masses: a diagnostic, multicenter study.Frontiers in oncology · 2025Article
- Challenges in magnetic resonance imaging-based detection of clinically significant prostate cancer in young patients: two alternative approaches.Quantitative imaging in medicine and surgery · 2024Article
- Prostate cancer lesions in transition zone exhibit a higher propensity for pathological upgrading in radical prostatectomy.World journal of urology · 2024Article
- Prediction of extracapsular extension of prostate cancer by MRI radiomic signature: a systematic review.Insights into imaging · 2024Review
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Authors and funding
11 authors at 5 institutions in 1 country.
Funding
Abstract
purposeTo investigate the performance of magnetic resonance imaging (MRI)-based radiomics models for benign and malignant prostate lesion discrimination and extracapsular extension (ECE) and positive surgical margins (PSM) prediction. METHODS AND MATERIALS: In total, 459 patients who underwent multiparametric MRI (mpMRI) before prostate biopsy were included. Radiomic features were extracted from both T2-weighted imaging (T2WI) and the apparent diffusion coefficient (ADC). Patients were divided into different training sets and testing sets for different targets according to a ratio of 7:3. Radiomics signatures were built using radiomic features on the training set, and integrated models were built by adding clinical characteristics. The areas under the receiver operating characteristic curves (AUCs) were calculated to assess the classification performance on the testing sets.
resultsThe radiomics signatures for benign and malignant lesion discrimination achieved AUCs of 0.775 (T2WI), 0.863 (ADC) and 0.855 (ADC + T2WI). The corresponding integrated models improved the AUC to 0.851/0.912/0.905, respectively. The radiomics signatures for ECE achieved the highest AUC of 0.625 (ADC), and the corresponding integrated model achieved the highest AUC (0.728). The radiomics signatures for PSM prediction achieved AUCs of 0.614 (T2WI) and 0.733 (ADC). The corresponding integrated models reached AUCs of 0.680 and 0.766, respectively.
conclusionsThe MRI-based radiomics models, which took advantage of radiomic features on ADC and T2WI scans, showed good performance in discriminating benign and malignant prostate lesions and predicting ECE and PSM. Combining radiomics signatures and clinical factors enhanced the performance of the models, which may contribute to clinical diagnosis and treatment.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.