ArticleJournal of magnetic resonance imaging : JMRI2019
Prostate Cancer Differentiation and Aggressiveness: Assessment With a Radiomic-Based Model vs. PI-RADS v2.
Article in Journal of magnetic resonance imaging : JMRI, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 82 papers, 5 of them syntheses that pooled it.
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Who cites it
82 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Machine learning-based MRI imaging for prostate cancer diagnosis: systematic review and meta-analysis.Prostate cancer and prostatic diseases · 2026Pooled it
- Radiomics vs radiologist in prostate cancer. Results from a systematic review.World journal of urology · 2023Pooled it
- Radiomic and Genomic Machine Learning Method Performance for Prostate Cancer Diagnosis: Systematic Literature Review.Journal of medical Internet research · 2021Pooled it
- Radiomics in prostate cancer imaging for a personalized treatment approach - current aspects of methodology and a systematic review on validated studies.Theranostics · 2021Pooled it
- Machine learning for the identification of clinically significant prostate cancer on MRI: a meta-analysis.European radiology · 2020Pooled it
- Feasibility of automated AI-based contouring and stable radiomic feature assessment by HyperSight-CBCT Imaging for adaptive high-precision radiotherapy of prostate cancer.Scientific reports · 2026Article
- Diagnostic performance of magnetic resonance imaging-based radiomics for detecting prostate cancer: A systematic review and meta-analysis.World journal of radiology · 2026Article
- Reproducibility of MRI Radiomics Measurements in Men with Prostate Cancer Undergoing Active Surveillance.Cancers · 2026Article
- Prostate Cancer Classification Using Quantum Machine Learning on Multi-parametric MRI.Proceedings of SPIE--the International Society for Optical Engineering · 2026Article
- Machine learning based on radiomics for discriminating sellar region langerhans cell histiocytosis from germ cell tumors.Frontiers in pediatrics · 2026Article
- Developing an interpretable clinical-radiomics machine learning model using whole transition zone MRI analysis for improving diagnosis of transition zone prostate cancer.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
- The Role of Radiomic Analysis and Different Machine Learning Models in Prostate Cancer Diagnosis.Journal of imaging · 2025Article
- Machine learning models for discriminating clinically significant from clinically insignificant prostate cancer using bi-parametric magnetic resonance imagingDiagnostic and interventional radiology (Ankara, Turkey) · 2025Article
- Article
- Longitudinal stability of HyperSightScientific reports · 2025Article
- Article
- Retrospectively Quantified T2 Improves Detection of Clinically Significant Peripheral Zone Prostate Cancer.Cancers · 2025Article
- Evaluating the feasibility of AI-predicted bpMRI image features for predicting prostate cancer aggressiveness: a multi-center study.Insights into imaging · 2025Article
- Automatic Characterization of Prostate Suspect Lesions on T2-Weighted Image Acquisitions Using Texture Features and Machine-Learning Methods: A Pilot Study.Diagnostics (Basel, Switzerland) · 2025Article
22 more citing papers are in PubMed but not listed here.
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMultiparametric MRI (mp-MRI) combined with machine-aided approaches have shown high accuracy and sensitivity in prostate cancer (PCa) diagnosis. However, radiomics-based analysis has not been thoroughly compared with Prostate Imaging and Reporting and Data System version 2 (PI-RADS v2) scores. PURPOSE: To develop and validate a radiomics-based model for differentiating PCa and assessing its aggressiveness compared with PI-RADS v2 scores. STUDY TYPE: Retrospective. POPULATION: In all, 182 patients with biopsy-proven PCa and 199 patients with a biopsy-proven absence of cancer were enrolled in our study. FIELD STRENGTH/SEQUENCE: Conventional and diffusion-weighted MR images (b values = 0, 1000 sec/mm ASSESSMENT: A total of 396 features and 385 features were extracted from apparent diffusion coefficient (ADC) images and T STATISTICAL TESTS: A radiomics-based predictive model was constructed by logistic regression analysis. 70% of the patients were assigned to the training group, and the remaining were assigned to the validation group. The diagnostic efficacy was analyzed with receiver operating characteristic (ROC) in both the training and validation groups.
resultsFor PCa versus non-PCa, the validation model had an area under the ROC curve (AUC) of 0.985, 0.982, and 0.999 with T DATA
conclusionBoth the T LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:875-884.
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