SynthesisEuropean radiology2024
Radiomics for the identification of extraprostatic extension with prostate MRI: a systematic review and meta-analysis.
Synthesis in European radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 27 citations in OpenAlex.
- 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-Based Characterization of Aggressive Prostate Cancer Variants: Diagnostic Challenges and Opportunities.Cancers · 2026Review
- Review
- From AI-based image analysis to surgical decision support in prostate cancer: interdisciplinary application of the international radiomics platform.Frontiers in oncology · 2026Article
- Clinical and MRI features for predicting maximum cancer involvement of >50% in a single biopsy core among patients diagnosed with prostate cancer at initial systematic biopsy: a machine learning study.American journal of cancer research · 2026Article
- Diagnostic accuracy of MRI radiomics in predicting lymph node metastasis in prostate cancer: A systematic review.European journal of radiology open · 2025Review
- An innovative approach for predicting prostate cancer Gleason grading: machine learning-based fusion of multimodal ultrasound, clinical and laboratory indicators.European journal of medical research · 2025Article
- Prognostic significance of the mEPE score in intermediate-risk prostate cancer patients undergoing ultrahypofractionated robotic SBRT.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2025Article
- Reproducibility of methodological radiomics score (METRICS): an intra- and inter-rater reliability study endorsed by EuSoMII.European radiology · 2025Article
- Quantitative Prostate MRI, From theAJR. American journal of roentgenology · 2025Review
- Performance of GPT-4 for automated prostate biopsy decision-making based on mpMRI: a multi-center evidence study.Military Medical Research · 2025Article
- Optimizing clinical risk stratification of localized prostate cancer.Current opinion in urology · 2025Review
- Development and validation of a novel clinical-radiological-pathological scoring system for preoperative prediction of extraprostatic extension in prostate cancer: a multicenter retrospective study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Article
- 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
- Predicting HER2 overexpression in prostate cancer using machine learning: implications for personalized therapy.Frontiers in oncology · 2025Article
- Preoperative detection of extraprostatic tumor extension in patients with primary prostate cancer utilizing [Insights into imaging · 2024Article
- Recent trends in AI applications for pelvic MRI: a comprehensive review.La Radiologia medica · 2024Review
- Different radiomics annotation methods comparison in rectal cancer characterisation and prognosis prediction: a two-centre study.Insights into imaging · 2024Article
Corrections and comments
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Authors and funding
10 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesExtraprostatic extension (EPE) of prostate cancer (PCa) is predicted using clinical nomograms. Incorporating MRI could represent a leap forward, although poor sensitivity and standardization represent unsolved issues. MRI radiomics has been proposed for EPE prediction. The aim of the study was to systematically review the literature and perform a meta-analysis of MRI-based radiomics approaches for EPE prediction. MATERIALS AND
methodsMultiple databases were systematically searched for radiomics studies on EPE detection up to June 2022. Methodological quality was appraised according to Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool and radiomics quality score (RQS). The area under the receiver operating characteristic curves (AUC) was pooled to estimate predictive accuracy. A random-effects model estimated overall effect size. Statistical heterogeneity was assessed with I
resultsThirteen studies were included, showing limitations in study design and methodological quality (median RQS 10/36), with high statistical heterogeneity. Pooled AUC for EPE identification was 0.80. In subgroup analysis, test-set and cross-validation-based studies had pooled AUC of 0.85 and 0.89 respectively. Pooled AUC was 0.72 for deep learning (DL)-based and 0.82 for handcrafted radiomics studies and 0.79 and 0.83 for studies with multiple and single scanner data, respectively. Finally, models with the best predictive performance obtained using radiomics features showed pooled AUC of 0.82, while those including clinical data of 0.76.
conclusionMRI radiomics-powered models to identify EPE in PCa showed a promising predictive performance overall. However, methodologically robust, clinically driven research evaluating their diagnostic and therapeutic impact is still needed. CLINICAL RELEVANCE STATEMENT: Radiomics might improve the management of prostate cancer patients increasing the value of MRI in the assessment of extraprostatic extension. However, it is imperative that forthcoming research prioritizes confirmation studies and a stronger clinical orientation to solidify these advancements. KEY POINTS: • MRI radiomics deserves attention as a tool to overcome the limitations of MRI in prostate cancer local staging. • Pooled AUC was 0.80 for the 13 included studies, with high heterogeneity (84.7%, p < .001), methodological issues, and poor clinical orientation. • Methodologically robust radiomics research needs to focus on increasing MRI sensitivity and bringing added value to clinical nomograms at patient level.
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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.