ArticleiScience2022
Using deep learning to detect patients at risk for prostate cancer despite benign biopsies.
Article in iScience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 21 citations in OpenAlex.
- Automatic Grade Classification in Prostate Histopathological Images Using EfficientNet and Ordinal Focal Loss.Bioengineering (Basel, Switzerland) · 2026Article
- Longitudinal outcome prediction of prostate cancer patients on active surveillance using multiple instance learning.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Discovery of tumour indicating morphological changes in benign prostate biopsies through AI.Scientific reports · 2025Article
- A clinical prostate biopsy dataset with undetected cancer.Scientific data · 2025Article
- Providing a Prostate Cancer Detection and Prevention Method With Developed Deep Learning Approach.Prostate cancer · 2025Article
- Current paradigm and futuristic vision on new-onset diabetes and pancreatic cancer research.Frontiers in pharmacology · 2025Review
- Ensemble-based deep learning improves detection of invasive breast cancer in routine histopathology images.Heliyon · 2024Article
- Multi-Scale Digital Pathology Patch-Level Prostate Cancer Grading Using Deep Learning: Use Case Evaluation of DiagSet Dataset.Bioengineering (Basel, Switzerland) · 2024Article
- Harnessing artificial intelligence for prostate cancer management.Cell reports. Medicine · 2024Review
- A Multi-Stain Breast Cancer Histological Whole-Slide-Image Data Set from Routine Diagnostics.Scientific data · 2023Article
- Deep Learning Methodologies Applied to Digital Pathology in Prostate Cancer: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- A Blood-Based Immune Gene Signature with Prognostic Significance in Localized Prostate Cancer.Cancers · 2023Article
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Routine transrectal ultrasound-guided systematic prostate biopsy only samples a small volume of the prostate and tumors between biopsy cores can be missed, leading to low sensitivity to detect clinically relevant prostate cancers (PCa). Deep learning may enable detection of PCa despite benign biopsies. We included 14,354 hematoxylin-eosin stained benign prostate biopsies from 1,508 men in two groups: men without established PCa diagnosis and men with at least one core biopsy diagnosed with PCa. A 10-Convolutional Neural Network ensemble was optimized to distinguish benign biopsies from benign men or patients with PCa. Area under the receiver operating characteristic curve was estimated at 0.739 (bootstrap 95% CI:0.682-0.796) on man level in the held-out test set. At the specificity of 0.90, the model sensitivity was 0.348. The proposed model can detect men with risk of missed PCa and has the potential to reduce false negatives and to indicate men who could benefit from rebiopsies.
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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.