ArticleEuropean urology focus2021
Computationally Derived Cribriform Area Index from Prostate Cancer Hematoxylin and Eosin Images Is Associated with Biochemical Recurrence Following Radical Prostatectomy and Is Most Prognostic in Gleason Grade Group 2.
Article in European urology focus, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- A guide for the deployment, validation, and accreditation of clinical digital pathology tools.Journal of pathology informatics · 2026Article
- Artificial intelligence for detection, grading, and prognostication in prostate cancer pathology: A scoping review.Histology and histopathology · 2026Article
- The evolution of prostate cancer grading: from Gleason score to risk taxonomy and the artificial intelligence revolution.Virchows Archiv : an international journal of pathology · 2026Review
- Cribriform Tumor Burden in Grade Group 4 Prostate Cancer: A Quantitative Threshold Predicting Lymphovascular Invasion and Metastasis.Journal of clinical medicine · 2026Article
- A Computational Pathology Model to Predict Docetaxel Benefit in Localized High-Risk and Metastatic Prostate Cancer.Clinical cancer research : an official journal of the American Association for Cancer Research · 2026Article
- Intraductal carcinoma of the prostate: A comprehensive literature review focused on grading challenges and controversies.Histology and histopathology · 2025Review
- Clinical Relevance of Computationally Derived Attributes of Arteries and Arterioles in focal segmental glomerulosclerosis and minimal change disease.medRxiv : the preprint server for health sciences · 2025Article
- Undetected Cribriform and Intraductal Prostate Cancer at biopsy is associated with adverse outcomes.Prostate cancer and prostatic diseases · 2025Article
- Development of a deep learning system for predicting biochemical recurrence in prostate cancer.BMC cancer · 2025Article
- Interpretable weakly-supervised learning through kernel density matrices: A digital pathology use case.PloS one · 2025Article
- A selective CutMix approach improves generalizability of deep learning-based grading and risk assessment of prostate cancer.Journal of pathology informatics · 2024Article
- Predicting Biochemical Recurrence of Prostate Cancer Post-Prostatectomy Using Artificial Intelligence: A Systematic Review.Cancers · 2024Review
- Article
- Characterization of arteriosclerosis based on computer-aided measurements of intra-arterial thickness.Journal of medical imaging (Bellingham, Wash.) · 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
- Harnessing artificial intelligence for prostate cancer management.Cell reports. Medicine · 2024Review
- Artificial intelligence applications in prostate cancer.Prostate cancer and prostatic diseases · 2024Review
- Distinct Adverse Clinical Outcomes of Small and Large Cribriform Patterns on Gleason 7 Prostate Cancer: A Preliminary Study.Urology research & practice · 2023Article
- Deep Learning Methodologies Applied to Digital Pathology in Prostate Cancer: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- Nondestructive 3D pathology with analysis of nuclear features for prostate cancer risk assessment.The Journal of pathology · 2023Article
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Authors and funding
22 authors.
Funding
Abstract
backgroundThe presence of invasive cribriform adenocarcinoma (ICC), an expanse of cells containing punched-out lumina uninterrupted by stroma, in radical prostatectomy (RP) specimens has been associated with biochemical recurrence (BCR). However, ICC identification has only moderate inter-reviewer agreement.
objectiveTo investigate quantitative machine-based assessment of the extent and prognostic utility of ICC, especially within individual Gleason grade groups. DESIGN, SETTING, AND
participantsA machine learning approach was developed for ICC segmentation using 70 RP patients and validated in a cohort of 749 patients from four sites whose median year of surgery was 2007 and with median follow-up of 28 mo. ICC was segmented on one representative hematoxylin and eosin RP slide per patient and the fraction of tumor area composed of ICC, the cribriform area index (CAI), was measured. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The association between CAI and BCR was measured in terms of the concordance index (c index) and hazard ratio (HR). RESULTS AND LIMITATIONS: CAI was correlated with BCR (c index 0.62) in the validation set of 411 patients with ICC morphology, especially those with Gleason grade group 2 cancer (n = 192; c index 0.66), and was less prognostic when patients without ICC were included (c index 0.54). A doubling of CAI in the group with ICC morphology was prognostic after controlling for Gleason grade, surgical margin positivity, preoperative prostate-specific antigen level, pathological T stage, and age (HR 1.19, 95% confidence interval 1.03-1.38; p = 0.018).
conclusionsAutomated image analysis and machine learning could provide an objective, quantitative, reproducible, and high-throughput method of quantifying ICC area. The performance of CAI for grade group 2 cancer suggests that for patients with little Gleason 4 pattern, the ICC fraction has a strong prognostic role. PATIENT SUMMARY: Machine-based measurement of a specific cell pattern (cribriform; sieve-like, with lots of spaces) in images of prostate specimens could improve risk stratification for patients with prostate cancer. In the future, this could help in expanding the criteria for active surveillance.
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