ArticleClinical cancer research : an official journal of the American Association for Cancer Research2020
Computationally Derived Image Signature of Stromal Morphology Is Prognostic of Prostate Cancer Recurrence Following Prostatectomy in African American Patients.
Article in Clinical cancer research : an official journal of the American Association for Cancer Research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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Who cites it
42 citing papers in PubMed, 65 citations in OpenAlex.
- Artificial intelligence for detection, grading, and prognostication in prostate cancer pathology: A scoping review.Histology and histopathology · 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
- Advancements in artificial intelligence for prostate cancer: Optimizing diagnosis, treatment, and prognostic assessment.Asian journal of urology · 2025Review
- Computational Morphological Assessment of Bladder Cancer Tissue Is Prognostic of Recurrence and Overall Survival Following Transurethral Resection.JCO clinical cancer informatics · 2025Article
- Population-Specific Radiomics From Biparametric Magnetic Resonance Imaging Improves Prostate Cancer Risk Stratification in African American Men.JU open plus · 2025Article
- Computational image and molecular analysis reveal unique prognostic features of immune architecture in African Versus European American women with endometrial cancer.NPJ precision oncology · 2025Article
- Mitigating bias in prostate cancer diagnosis using synthetic data for improved AI driven Gleason grading.NPJ precision oncology · 2025Article
- Stress testing deep learning models for prostate cancer detection on biopsies and surgical specimens.The Journal of pathology · 2025Article
- Advances in the identification of novel cell signatures in benign prostatic hyperplasia and prostate cancer using single-cell RNA sequencing.Frontiers in immunology · 2025Review
- Deciphering the Tumor Microenvironment in Prostate Cancer: A Focus on the Stromal Component.Cancers · 2024Review
- Ten challenges and opportunities in computational immuno-oncology.Journal for immunotherapy of cancer · 2024Review
- A New Era of Data-Driven Cancer Research and Care: Opportunities and Challenges.Cancer discovery · 2024Article
- Towards equitable AI in oncology.Nature reviews. Clinical oncology · 2024Review
- PatchSorter: a high throughput deep learning digital pathology tool for object labeling.NPJ digital medicine · 2024Article
- 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
- Article
- Harnessing artificial intelligence for prostate cancer management.Cell reports. Medicine · 2024Review
- Demographic bias in misdiagnosis by computational pathology models.Nature medicine · 2024Article
- Artificial intelligence applications in prostate cancer.Prostate cancer and prostatic diseases · 2024Review
- Pathogenomics for accurate diagnosis, treatment, prognosis of oncology: a cutting edge overview.Journal of translational medicine · 2024Review
Corrections and comments
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Authors and funding
14 authors at 7 institutions in 1 country.
Funding
Abstract
purposeBetween 30%-40% of patients with prostate cancer experience disease recurrence following radical prostatectomy. Existing clinical models for recurrence risk prediction do not account for population-based variation in the tumor phenotype, despite recent evidence suggesting the presence of a unique, more aggressive prostate cancer phenotype in African American (AA) patients. We investigated the capacity of digitally measured, population-specific phenotypes of the intratumoral stroma to create improved models for prediction of recurrence following radical prostatectomy. EXPERIMENTAL
designThis study included 334 radical prostatectomy patients subdivided into training (V
resultsAn AA-specific, automated stromal signature, AAstro, was prognostic of recurrence risk in both independent validation datasets [V
conclusionsOur results suggest that considering population-specific information and stromal morphology has the potential to substantially improve accuracy of prognosis and risk stratification in AA patients with prostate cancer.
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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.