ArticleBritish journal of cancer2023
Biopsy-free AI-aided precision MRI assessment in prediction of prostate cancer biochemical recurrence.
Article in British journal of cancer, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 5 of them syntheses that pooled it.
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Who cites it
18 citing papers in PubMed, 5 syntheses or guidelines pooled it, 20 citations in OpenAlex.
- Magnetic Resonance Imaging-Based Artificial Intelligence in Predicting Prostate Cancer Biochemical Recurrence: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Radiomics as a tool for predicting biochemical recurrence after total prostatectomy: a systematic review and meta-analysis.International urology and nephrology · 2026Pooled it
- Prediction models in prostate cancer: a systematic review and meta-analysis.Frontiers in oncology · 2026Pooled it
- MRI-based radiomics for prediction of biochemical recurrence in prostate cancer: a systematic review and meta-analysis.Abdominal radiology (New York) · 2025Pooled it
- Diagnostic systematic review and meta-analysis of machine learning in predicting biochemical recurrence of prostate cancer.Scientific reports · 2025Pooled it
- Preoperative prediction of positive surgical margins in prostate cancer using multimodal deep learning model: a multicenter study.Abdominal radiology (New York) · 2026Article
- Explainable and visualizable machine learning model development and validation for 5-year postoperative survival prediction in prostate cancer patients aged ≥ 65 years.BMC geriatrics · 2026Article
- Diagnostic value of prostate magnetic resonance imaging in men with prostate-specific antigen levels ≥ 15 ng/mL for biopsy decision-making.Insights into imaging · 2026Article
- Artificial Intelligence in Prostate MRI: Addressing Current Limitations Through Emerging Technologies.Journal of magnetic resonance imaging : JMRI · 2026Review
- Machine learning-DeepSurv prediction model integrating mpMRI radiomics and genomic biomarkers for BCR-free survival and tumor response in prostate radiotherapy.Journal of radiation research · 2026Article
- An AI-Based Radiomics Model Using MRI ADC Maps for Accurate Prediction of Advanced Prostate Cancer Progression.Current oncology (Toronto, Ont.) · 2026Article
- A multimodal automated deep learning-based model for predicting biochemical recurrence of prostate cancer following prostatectomy from baseline MRI, Presurgical clinical covariates.Clinical imaging · 2025Article
- Risk prediction models for biochemical recurrence of Chinese prostate cancer patients after radical prostatectomy based on magnetic resonance imaging examination: a systematic review.Quantitative imaging in medicine and surgery · 2025Review
- Significant reduction in manual annotation costs in ultrasound medical image database construction through step by step artificial intelligence pre-annotation.PLOS digital health · 2025Article
- Development of a deep learning system for predicting biochemical recurrence in prostate cancer.BMC cancer · 2025Article
- Predicting Biochemical Recurrence of Prostate Cancer Post-Prostatectomy Using Artificial Intelligence: A Systematic Review.Cancers · 2024Review
- Development and Validation of a Multimodality Model Based on Whole-Slide Imaging and Biparametric MRI for Predicting Postoperative Biochemical Recurrence in Prostate Cancer.Radiology. Imaging cancer · 2024Article
- Harnessing machine learning to predict prostate cancer survival: a review.Frontiers in oncology · 2024Review
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
backgroundTo investigate the predictive ability of high-throughput MRI with deep survival networks for biochemical recurrence (BCR) of prostate cancer (PCa) after prostatectomy.
methodsClinical-MRI and histopathologic data of 579 (train/test, 463/116) PCa patients were retrospectively collected. The deep survival network (iBCR-Net) is based on stepwise processing operations, which first built an MRI radiomics signature (RadS) for BCR, and predicted the T3 stage and lymph node metastasis (LN+) of tumour using two predefined AI models. Subsequently, clinical, imaging and histopathological variables were integrated into iBCR-Net for BCR prediction.
resultsRadS, derived from 2554 MRI features, was identified as an independent predictor of BCR. Two predefined AI models achieved an accuracy of 82.6% and 78.4% in staging T3 and LN+. The iBCR-Net, when expressed as a presurgical model by integrating RadS, AI-diagnosed T3 stage and PSA, can match a state-of-the-art histopathological model (C-index, 0.81 to 0.83 vs 0.79 to 0.81, p > 0.05); and has maximally 5.16-fold, 12.8-fold, and 2.09-fold (p < 0.05) benefit to conventional D'Amico score, the Cancer of the Prostate Risk Assessment (CAPRA) score and the CAPRA Postsurgical score.
conclusionsAI-aided iBCR-Net using high-throughput MRI can predict PCa BCR accurately and thus may provide an alternative to the conventional method for PCa risk stratification.
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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.