ArticleThe American journal of pathology2024
A Deep Learning Approach for the Identification of the Molecular Subtypes of Pancreatic Ductal Adenocarcinoma Based on Whole Slide Pathology Images.
Article in The American journal of pathology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Artificial Intelligence for Molecular Biomarker Identification in Gastrointestinal and Hepatobiliary Cancers.International journal of molecular sciences · 2026Review
- Spatially resolved tissue architecture and computational pathology in pancreatic cancer.Experimental & molecular medicine · 2026Review
- Inferring Clinically Relevant Molecular Subtypes of Pancreatic Cancer from Routine Histopathology Using Deep Learning.Research square · 2026Article
- MetaPaCS: A Novel Meta-Learning Framework for Pancreatic Cancer Subtype Identification.bioRxiv : the preprint server for biology · 2026Article
- Inferring Clinically Relevant Molecular Subtypes of Pancreatic Cancer from Routine Histopathology Using Deep Learning.medRxiv : the preprint server for health sciences · 2026Article
- MMD-Net: a weakly supervised solution for quantification of nonalcoholic fatty liver biopsies.Quantitative imaging in medicine and surgery · 2026Article
- Artificial intelligence in pancreatic cancer histopathology and diagnostics - implications for clinical decisions and biomarker discovery?Cell division · 2025Review
- Advancing Precision Medicine in PDAC: An Ethical Scoping Review and Call to Action for IHC Implementation.Cancers · 2025Review
- Diagnosis of Pancreatic Ductal Adenocarcinoma Using Deep Learning.Sensors (Basel, Switzerland) · 2024Article
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Authors and funding
12 authors.
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Abstract
Delayed diagnosis and treatment resistance result in high pancreatic ductal adenocarcinoma (PDAC) mortality rates. Identifying molecular subtypes can improve treatment, but current methods are costly and time-consuming. In this study, deep learning models were used to identify histologic features that classify PDAC molecular subtypes based on routine hematoxylin-eosin-stained histopathologic slides. A total of 97 histopathology slides associated with resectable PDAC from The Cancer Genome Atlas project were used to train a deep learning model and test the performance on 44 needle biopsy material (110 slides) from a local annotated patient cohort. The model achieved balanced accuracy of 96.19% and 83.03% in identifying the classical and basal subtypes of PDAC in The Cancer Genome Atlas and the local cohort, respectively. This study provides a promising method to cost-effectively and rapidly classify PDAC molecular subtypes based on routine hematoxylin-eosin-stained slides, potentially leading to more effective clinical management of this disease.
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