ArticleArchives of pathology & laboratory medicine2023
Graph Convolutional Neural Networks for Histologic Classification of Pancreatic Cancer.
Article in Archives of pathology & laboratory medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Graph Neural Networks for Medical Imaging Analysis and Biological Data: Integrating Topology, Geometry, Radiomics, and Generative AI.Bioengineering (Basel, Switzerland) · 2026Review
- Improving Colorectal Cancer Screening and Risk Assessment through Predictive Modeling on Medical Images and Records.The American journal of pathology · 2026Article
- Deep learning for digital pathology: A critical overview of methodological framework.Journal of pathology informatics · 2025Review
- Deep Multiple Instance Learning Model to Predict Outcome of Pancreatic Cancer Following Surgery.Biomedicines · 2024Article
- Spatial Omics Driven Crossmodal Pretraining Applied to Graph-based Deep Learning for Cancer Pathology Analysis.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2024Article
- MHAttnSurv: Multi-head attention for survival prediction using whole-slide pathology images.Computers in biology and medicine · 2023Article
- Improving Representation Learning for Histopathologic Images with Cluster Constraints.Proceedings. IEEE International Conference on Computer Vision · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
CONTEXT.—: Pancreatic ductal adenocarcinoma has some of the worst prognostic outcomes among various cancer types. Detection of histologic patterns of pancreatic tumors is essential to predict prognosis and decide the treatment for patients. This histologic classification can have a large degree of variability even among expert pathologists. OBJECTIVE.—: To detect aggressive adenocarcinoma and less aggressive pancreatic tumors from nonneoplasm cases using a graph convolutional network-based deep learning model. DESIGN.—: Our model uses a convolutional neural network to extract detailed information from every small region in a whole slide image. Then, we use a graph architecture to aggregate the extracted features from these regions and their positional information to capture the whole slide-level structure and make the final prediction. RESULTS.—: We evaluated our model on an independent test set and achieved an F1 score of 0.85 for detecting neoplastic cells and ductal adenocarcinoma, significantly outperforming other baseline methods. CONCLUSIONS.—: If validated in prospective studies, this approach has a great potential to assist pathologists in identifying adenocarcinoma and other types of pancreatic tumors in clinical settings.
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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.