ArticleCancers2020
Multiparametric MRI for Prostate Cancer Characterization: Combined Use of Radiomics Model with PI-RADS and Clinical Parameters.
Article in Cancers, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05024162 (Can Magnetic Resonance Imaging of the Prostate Combined With a Radiomics Evaluation Determine the Invasive Capacity of a Tumour), which is not on this map. Cited by 78 papers, 2 of them syntheses that pooled it.
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.
Can Magnetic Resonance Imaging of the Prostate Combined With a Radiomics Evaluation Determine the Invasive Capacity of a Tumour (Can MRI-PREDICT)
Who cites it
78 citing papers in PubMed, 2 syntheses or guidelines pooled it, 113 citations in OpenAlex.
- Artificial-intelligence models vs. radiologists in the detection of clinically significant prostate cancer on mpMRI: a meta-analysis.European radiology · 2026Pooled 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-based radiomics for histological classification of parotid tumors using morphological MRI: a comparative study.European radiology · 2022Trial
- Deep learning for fully automatic detection, segmentation, and Gleason grade estimation of prostate cancer in multiparametric magnetic resonance images.Scientific reports · 2022Trial
- Impact of Different Energy Levels of Virtual Monoenergetic Reconstructions on Radiomic Features Stability in Organic Phantom Imaging Using Photon-Counting CT.Tomography (Ann Arbor, Mich.) · 2026Article
- Advancing Genitourinary Cancer Surgery: The Role of Artificial Intelligence and Robotics.Journal of clinical medicine · 2026Review
- Diagnostic performance of magnetic resonance imaging-based radiomics for detecting prostate cancer: A systematic review and meta-analysis.World journal of radiology · 2026Article
- Integrating Multiparametric MRI and PSMA PET Imaging in Prostate Cancer: Toward a Unified Diagnostic and Risk-Stratification Paradigm.Medicina (Kaunas, Lithuania) · 2026Review
- Artificial intelligence in urological malignancy diagnosis and prognosis: current status and future prospects.The Canadian journal of urology · 2026Review
- ADC-based radiomics for risk stratification in prostate cancer: a clinical decision support study.Frontiers in oncology · 2026Article
- Advancements in artificial intelligence for prostate cancer: Optimizing diagnosis, treatment, and prognostic assessment.Asian journal of urology · 2025Review
- Toward a Refined PI-RADS: The Feasibility and Limitations of More Informative Metrics in Reviewing MRI Scans.Journal of magnetic resonance imaging : JMRI · 2025Review
- Radiomics Features from Different Prostatic Zones onCancers · 2025Article
- Development and validation of a visual nomogram for predicting clinically significant prostate cancer in negative mpMRI usingScientific reports · 2025Article
- Performance of GPT-4 for automated prostate biopsy decision-making based on mpMRI: a multi-center evidence study.Military Medical Research · 2025Article
- Integrating multimodal data to predict the progression of hormone-sensitive prostate cancer.Clinical proteomics · 2025Article
- A two-stage model for precise identification and Gleason grading of clinically significant prostate cancer: a hybrid approach.Journal of medical radiation sciences · 2025Article
- Using a novel PSMA-PET and PSA-based model to enhance the diagnostic accuracy for clinically significant prostate cancer and avoid unnecessary biopsy in men with PI-RADS ≤ 3 MRI.European journal of nuclear medicine and molecular imaging · 2025Article
- Biparametric MRI-based radiomics for noninvastive discrimination of benign prostatic hyperplasia nodules (BPH) and prostate cancer nodules: a bio-centric retrospective cohort study.Scientific reports · 2025Article
- An MRI radiomics model for predicting a prostate-specific antigen response following abiraterone treatment in patients with metastatic castration-resistant prostate cancer.Frontiers in oncology · 2025Article
18 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Radiomics is an emerging field of image analysis with potential applications in patient risk stratification. This study developed and evaluated machine learning models using quantitative radiomic features extracted from multiparametric magnetic resonance imaging (mpMRI) to detect and classify prostate cancer (PCa). In total, 191 patients that underwent prostatic mpMRI and combined targeted and systematic fusion biopsy were retrospectively included. Segmentations of the whole prostate glands and index lesions were performed manually in apparent diffusion coefficient (ADC) maps and T2-weighted MRI. Radiomic features were extracted from regions corresponding to the whole prostate gland and index lesion. The best performing combination of feature setup and classifier was selected to compare its predictive ability of the radiologist's evaluation (PI-RADS), mean ADC, prostate specific antigen density (PSAD) and digital rectal examination (DRE) using receiver operating characteristic (ROC) analysis. Models were evaluated using repeated 5-fold cross-validation and a separate independent test cohort. In the test cohort, an ensemble model combining a radiomics model, with models for PI-RADS, PSAD and DRE achieved high predictive AUCs for the differentiation of (i) malignant from benign prostatic lesions (AUC = 0.889) and of (ii) clinically significant (csPCa) from clinically insignificant PCa (cisPCa) (AUC = 0.844). Our combined model was numerically superior to PI-RADS for cancer detection (AUC = 0.779;
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Registered trials
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.