ArticleScientific reports2020
Histologic tissue components provide major cues for machine learning-based prostate cancer detection and grading on prostatectomy specimens.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 36 citations in OpenAlex.
- The state of the art in artificial intelligence and digital pathology in prostate cancer.Nature reviews. Urology · 2026Review
- NUTM2A-AS1 as a potential key regulator in cancer: unraveling its ceRNA networks and impact on tumor biology.European journal of medical research · 2025Review
- Clinical implications of deep learning based image analysis of whole radical prostatectomy specimens.Scientific reports · 2025Article
- Artificial intelligence in digital pathology: a systematic review and meta-analysis of diagnostic test accuracy.NPJ digital medicine · 2024Review
- Harnessing artificial intelligence for prostate cancer management.Cell reports. Medicine · 2024Review
- Advantage of whole-mount histopathology in prostate cancer: current applications and future prospects.BMC cancer · 2024Review
- Deep Learning Methodologies Applied to Digital Pathology in Prostate Cancer: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- Predicting Response of Triple-Negative Breast Cancer to Neoadjuvant Chemotherapy Using a Deep Convolutional Neural Network-Based Artificial Intelligence Tool.JCO clinical cancer informatics · 2023Article
- Archimedes Optimization Algorithm with Deep Learning-Based Prostate Cancer Classification on Magnetic Resonance Imaging.Healthcare (Basel, Switzerland) · 2023Article
- An update on computational pathology tools for genitourinary pathology practice: A review paper from the Genitourinary Pathology Society (GUPS).Journal of pathology informatics · 2023Review
- Artificial intelligence for prostate cancer histopathology diagnostics.Canadian Urological Association journal = Journal de l'Association des urologues du Canada · 2022Article
- Cultivating Clinical Clarity through Computer Vision: A Current Perspective on Whole Slide Imaging and Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2022Review
- Association between Nuclear Morphometry Parameters and Gleason Grade in Patients with Prostatic Cancer.Diagnostics (Basel, Switzerland) · 2022Article
- Computer-Aided Assessment of Melanocytic Lesions by Means of a Mitosis Algorithm.Diagnostics (Basel, Switzerland) · 2022Article
- Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge.Nature medicine · 2022Article
- Deep Learning-Assisted Burn Wound Diagnosis: Diagnostic Model Development Study.JMIR medical informatics · 2021Article
- Development and Validation of an Artificial Intelligence-Powered Platform for Prostate Cancer Grading and Quantification.JAMA network open · 2021Article
- Outsmarting Metastatic Prostate Cancer: Integration of Imaging, Liquid Biopsies and Biomarkers With Artificial Intelligence.Technology in cancer research & treatmentReview
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Automatically detecting and grading cancerous regions on radical prostatectomy (RP) sections facilitates graphical and quantitative pathology reporting, potentially benefitting post-surgery prognosis, recurrence prediction, and treatment planning after RP. Promising results for detecting and grading prostate cancer on digital histopathology images have been reported using machine learning techniques. However, the importance and applicability of those methods have not been fully investigated. We computed three-class tissue component maps (TCMs) from the images, where each pixel was labeled as nuclei, lumina, or other. We applied seven different machine learning approaches: three non-deep learning classifiers with features extracted from TCMs, and four deep learning, using transfer learning with the 1) TCMs, 2) nuclei maps, 3) lumina maps, and 4) raw images for cancer detection and grading on whole-mount RP tissue sections. We performed leave-one-patient-out cross-validation against expert annotations using 286 whole-slide images from 68 patients. For both cancer detection and grading, transfer learning using TCMs performed best. Transfer learning using nuclei maps yielded slightly inferior overall performance, but the best performance for classifying higher-grade cancer. This suggests that 3-class TCMs provide the major cues for cancer detection and grading primarily using nucleus features, which are the most important information for identifying higher-grade 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.