ArticleJCO clinical cancer informatics2022
A Novel Artificial Intelligence-Powered Method for Prediction of Early Recurrence of Prostate Cancer After Prostatectomy and Cancer Drivers.
Article in JCO clinical cancer informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 26 citations in OpenAlex.
- Androgen deprivation, androgen receptor-targeted vaccination, and nivolumab in patients with high-risk localized prostate cancer.Journal for immunotherapy of cancer · 2026Trial
- 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
- Development and validation of a digital pathology artificial intelligence (DPAI)-derived risk score predicting Gleason grade group reclassification for patients who are candidates for active surveillance.Future oncology (London, England) · 2026Article
- The impact of tissue detection on diagnostic artificial intelligence algorithms in prostate digital pathology.Scientific reports · 2026Article
- Advancements in nanobiosensors for early cancer detection, challenges, treatment, and future prospects: a comprehensive review.Discover nano · 2026Review
- Multimodal artificial intelligence in urologic precision oncology: from algorithm to translational medicine (a systemized narrative review).Frontiers in oncology · 2026Review
- Article
- Quantifying partial pathological response rate in prostate cancer patients who underwent neoadjuvant chemotherapy using a novel morphometric approach.Journal of pathology informatics · 2025Article
- Beyond PSA: The Future of Prostate Cancer Diagnosis Using Artificial Intelligence, Novel Biomarkers, and Advanced Imagery.Life (Basel, Switzerland) · 2025Review
- 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
- Development of a deep learning system for predicting biochemical recurrence in prostate cancer.BMC cancer · 2025Article
- Unlocking artificial intelligence, machine learning and deep learning to combat therapeutic resistance in metastatic castration-resistant prostate cancer: a comprehensive review.Ecancermedicalscience · 2025Review
- Review
- Current advances in the use of artificial intelligence in predicting and managing urological complications.International urology and nephrology · 2024Review
- Predicting Biochemical Recurrence of Prostate Cancer Post-Prostatectomy Using Artificial Intelligence: A Systematic Review.Cancers · 2024Review
- The Role of Artificial Intelligence on Tumor Boards: Perspectives from Surgeons, Medical Oncologists and Radiation Oncologists.Current oncology (Toronto, Ont.) · 2024Review
- Harnessing artificial intelligence for prostate cancer management.Cell reports. Medicine · 2024Review
- Machine learning approaches for spatial omics data analysis in digital pathology: tools and applications in genitourinary oncology.Frontiers in oncology · 2024Review
- Activation of Stimulator of Interferon Genes (STING): Promising Strategy to Overcome Immune Resistance in Prostate Cancer.Current medicinal chemistry · 2024Review
- Deep Learning Methodologies Applied to Digital Pathology in Prostate Cancer: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
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Authors and funding
9 authors at 4 institutions in 1 country.
Funding
Abstract
purposeTo develop a novel artificial intelligence (AI)-powered method for the prediction of prostate cancer (PCa) early recurrence and identification of driver regions in PCa of all Gleason Grade Group (GGG). MATERIALS AND
methodsDeep convolutional neural networks were used to develop the AI model. The AI model was trained on The Cancer Genome Atlas Prostatic Adenocarcinoma (TCGA-PRAD) whole slide images (WSI) and data set (n = 243) to predict 3-year biochemical recurrence after radical prostatectomy (RP) and was subsequently validated on WSI from patients with PCa (n = 173) from the University of Wisconsin-Madison.
resultsOur AI-powered platform can extract visual and subvisual morphologic features from WSI to identify driver regions predictive of early recurrence of PCa (regions of interest [ROIs]) after RP. The ROIs were ranked with AI-morphometric scores, which were prognostic for 3-year biochemical recurrence (area under the curve [AUC], 0.78), which is significantly better than the GGG overall (AUC, 0.62). The AI-morphometric scores also showed high accuracy in the prediction of recurrence for low- or intermediate-risk PCa-AUC, 0.76, 0.84, and 0.81 for GGG1, GGG2, and GGG3, respectively. These patients could benefit the most from timely adjuvant therapy after RP. The predictive value of the high-scored ROIs was validated by known PCa biomarkers studied. With this focused biomarker analysis, a potentially new STING pathway-related PCa biomarker-TMEM173-was identified.
conclusionOur study introduces a novel approach for identifying patients with PCa at risk for early recurrence regardless of their GGG status and for identifying cancer drivers for focused evolution-aware novel biomarker discovery.
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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.