ReviewCancers2024
The Role of Radiomics in the Prediction of Clinically Significant Prostate Cancer in the PI-RADS v2 and v2.1 Era: A Systematic Review.
Review in Cancers, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- Recent advances in discriminating prostate cancer from benign prostatic diseases in the pre-biopsy population.Medical oncology (Northwood, London, England) · 2026Review
- Article
- Apparent diffusion coefficient-based single-sequence radiomics integrated with clinical variables and Prostate Imaging Reporting and Data System for predicting clinically significant prostate cancer.Translational andrology and urology · 2026Article
- PI-RADS v2.1 for prostate MRI: current applications, ongoing debates, and synergies with advanced technologies-a narrative review.Translational andrology and urology · 2026Review
- Combined radiomics, PI-RADS, and clinical model improve significant prostate cancer prediction and guide biopsy decision.Insights into imaging · 2026Article
- Artificial Intelligence in Prostate MRI: Comparison of an AI-Based Software and an Experienced Radiologist for Detecting Clinically Significant Prostate Cancer.Current oncology (Toronto, Ont.) · 2026Article
- Redefining Prostate Cancer Precision: Radiogenomics, Theragnostics, and AI-Driven Biomarkers.Cancers · 2025Review
- The Role of Radiomic Analysis and Different Machine Learning Models in Prostate Cancer Diagnosis.Journal of imaging · 2025Article
- Intralesional and perilesional radiomics strategy based on different machine learning for the prediction of international society of urological pathology grade group in prostate cancer.BMC medical imaging · 2025Article
- Article
- Integrating radiological and clinical data for clinically significant prostate cancer detection with machine learning techniques.Scientific reports · 2025Article
- Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Early detection of clinically significant prostate cancer (csPCa) has substantially improved with the latest PI-RADS versions. However, there is still an overdiagnosis of indolent lesions (iPCa), and radiomics has emerged as a potential solution. The aim of this systematic review is to evaluate the role of handcrafted and deep radiomics in differentiating lesions with csPCa from those with iPCa and benign lesions on prostate MRI assessed with PI-RADS v2 and/or 2.1. The literature search was conducted in PubMed, Cochrane, and Web of Science databases to select relevant studies. Quality assessment was carried out with Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2), Radiomic Quality Score (RQS), and Checklist for Artificial Intelligence in Medical Imaging (CLAIM) tools. A total of 14 studies were deemed as relevant from 411 publications. The results highlighted a good performance of handcrafted and deep radiomics methods for csPCa detection, but without significant differences compared to radiologists (PI-RADS) in the few studies in which it was assessed. Moreover, heterogeneity and restrictions were found in the studies and quality analysis, which might induce bias. Future studies should tackle these problems to encourage clinical applicability. Prospective studies and comparison with radiologists (PI-RADS) are needed to better understand its potential.
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