ArticleRadiology. Imaging cancer2024
Development and Validation of a Multimodality Model Based on Whole-Slide Imaging and Biparametric MRI for Predicting Postoperative Biochemical Recurrence in Prostate Cancer.
Article in Radiology. Imaging cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 3 of them syntheses that pooled it.
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Who cites it
15 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- 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
- Diagnostic systematic review and meta-analysis of machine learning in predicting biochemical recurrence of prostate cancer.Scientific reports · 2025Pooled it
- Multi-task deep learning integrating pretreatment MRI and whole slide images predicts induction chemotherapy response and survival in locally advanced nasopharyngeal carcinoma.PLOS digital health · 2026Article
- A multimodal study on predicting extrathyroidal extension of papillary thyroid carcinoma based on radiopathomics.BMC endocrine disorders · 2026Article
- A Deep Learning-Based Multimodal Clinico-Histology-Genomic Prognostic Model in Prostate Cancer.Annals of surgical oncology · 2026Article
- Development and Validation of a Multimodal AI-Based Model for Predicting Post-Prostatectomy Treatment Outcomes from Baseline Biparametric Prostate MRI.medRxiv : the preprint server for health sciences · 2026Article
- Artificial intelligence in genitourinary pathology.Histopathology · 2026Review
- 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
- The future of multimodal artificial intelligence models for integrating imaging and clinical metadata: a narrative reviewDiagnostic and interventional radiology (Ankara, Turkey) · 2025Review
- Integrated single-cell genomics, transcriptomics, and pathomics to identify potential biomarkers in muscle-invasive bladder cancer.Translational andrology and urology · 2025Article
- An overview of utilizing artificial intelligence in localized prostate cancer imaging.Expert review of medical devices · 2025Review
- A novel deep learning radiopathomics model for predicting carcinogenesis promotor cyclooxygenase-2 expression in common bile duct in children with pancreaticobiliary maljunction: a multicenter study.Insights into imaging · 2025Article
- Integrating Whole-Slide Imaging and MRI Data for Outcome Prediction after Prostatectomy in Localized Prostate Cancer.Radiology. Imaging cancer · 2024Article
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6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose To develop and validate a machine learning multimodality model based on preoperative MRI, surgical whole-slide imaging (WSI), and clinical variables for predicting prostate cancer (PCa) biochemical recurrence (BCR) following radical prostatectomy (RP). Materials and Methods In this retrospective study (September 2015 to April 2021), 363 male patients with PCa who underwent RP were divided into training (
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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.