ArticleCommunications medicine2022
Predicting biochemical recurrence of prostate cancer with artificial intelligence.
Article in Communications medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.
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Who cites it
33 citing papers in PubMed, 1 synthesis or guideline pooled it.
- MRI-based radiomics for prediction of biochemical recurrence in prostate cancer: a systematic review and meta-analysis.Abdominal radiology (New York) · 2025Pooled it
- A multimodal AI biomarker PATH-ORACLE improves prediction of recurrence in stage I lung adenocarcinoma.Immuno-oncology technology · 2026Article
- HistoEncoder: A digital pathology foundation model for prostate cancer.Journal of pathology informatics · 2026Article
- Immune Spatial Organization Predicts Distant Metastasis Risk in Aggressive Localized Prostate Cancer.Clinical cancer research : an official journal of the American Association for Cancer Research · 2026Article
- Multi-Scale Tri-Modal Histology Dataset Integrating Tumor Morphology, Immune Patterns, and Clinical Outcomes.bioRxiv : the preprint server for biology · 2026Article
- A human-in-the-loop explanation framework for morphologically transparent AI predictions from whole-slide images.NPJ digital medicine · 2026Article
- M2 macrophage related genes predict prognosis and drug response in prostate cancer.Discover oncology · 2026Article
- Multimodal integration of pathomics and single-cell transcriptomics reveals CDC20 as a link between prostate cancer recurrence and benzo[a]pyrene-responsive phenotypes.Frontiers in cell and developmental biology · 2026Article
- Multimodal artificial intelligence in urologic precision oncology: from algorithm to translational medicine (a systemized narrative review).Frontiers in oncology · 2026Review
- Review
- AI-driven precision diagnosis and treatment of prostate cancer: a narrative review.Frontiers in oncology · 2026Review
- Longitudinal outcome prediction of prostate cancer patients on active surveillance using multiple instance learning.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Artificial Intelligence-Based Digital Histologic Classifier for Prostate Cancer Risk Stratification: Independent Blinded Validation in Patients Treated With Radical Prostatectomy.JCO clinical cancer informatics · 2025Article
- The Role of Artificial Intelligence in the Evaluation of Prostate Pathology.Pathology international · 2025Review
- A concept-based interpretable model for the diagnosis of choroid neoplasias using multimodal data.Nature communications · 2025Article
- Artificial intelligence in digital pathology - time for a reality check.Nature reviews. Clinical oncology · 2025Review
- Towards understanding cancer dormancy over strategic hitching up mechanisms to technologies.Molecular cancer · 2025Review
- Development of a deep learning system for predicting biochemical recurrence in prostate cancer.BMC cancer · 2025Article
- Prediction of biochemical prostate cancer recurrence from any Gleason score using robust tissue structure and clinically available information.Discover oncology · 2025Article
- Artificial intelligence in prostate cancer: navigating the new frontier of precision uro-oncology.American journal of clinical and experimental urology · 2025Review
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Background: The first sign of metastatic prostate cancer after radical prostatectomy is rising PSA levels in the blood, termed biochemical recurrence. The prediction of recurrence relies mainly on the morphological assessment of prostate cancer using the Gleason grading system. However, in this system, within-grade morphological patterns and subtle histopathological features are currently omitted, leaving a significant amount of prognostic potential unexplored. Methods: To discover additional prognostic information using artificial intelligence, we trained a deep learning system to predict biochemical recurrence from tissue in H&E-stained microarray cores directly. We developed a morphological biomarker using convolutional neural networks leveraging a nested case-control study of 685 patients and validated on an independent cohort of 204 patients. We use concept-based explainability methods to interpret the learned tissue patterns. Results: The biomarker provides a strong correlation with biochemical recurrence in two sets ( Conclusions: These results show that the model finds predictive power in the tissue beyond the morphological ISUP grading.
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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.