ArticleNature biomedical engineering2022
Derivation of prognostic contextual histopathological features from whole-slide images of tumours via graph deep learning.
Article in Nature biomedical engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 1 of them a synthesis that pooled it.
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Who cites it
41 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Digital pathology and artificial intelligence in renal cell carcinoma focusing on feature extraction: a literature review.Frontiers in oncology · 2025Pooled it
- HistoEncoder: A digital pathology foundation model for prostate cancer.Journal of pathology informatics · 2026Article
- From unlabeled to labeled: self-supervised deep learning in computational pathology.BioData mining · 2026Article
- Scalable 3D cell-interaction analysis via supercell graphs for prostate cancer risk stratification.bioRxiv : the preprint server for biology · 2026Article
- Meta-encoder: a unified integration framework for multiple pathological foundation models in cancer detection.Nature communications · 2026Article
- Artificial intelligence in the diagnosis of thyroid diseases: applications and challenges.Frontiers in radiology · 2026Review
- Multimodal Fusion of 3D CT and Pathological Images for Gastric Cancer Recurrence Prediction.Cancer management and research · 2026Article
- Construction and validation of deep learning-based pathomics signature model for predicting postoperative recurrence of patients with clear cell renal cell carcinoma.American journal of cancer research · 2026Article
- Multiview deep-learning-enabled histopathology for prognostic and therapeutic stratification in stage II colorectal cancer: A retrospective multicenter study.PLoS medicine · 2026Observational
- Computational Pathology with Topological signatures and Visual Word Encoding.Research square · 2025Article
- SMMILe enables accurate spatial quantification in digital pathology using multiple-instance learning.Nature cancer · 2025Article
- A multimodal AI model for precision prognosis in clear cell renal cell carcinoma: A multicenter study.NPJ digital medicine · 2025Article
- Artificial intelligence-assisted diagnosis of glomerular nephritis using a pathological image analysis approach: a multicentre model development and validation study.EClinicalMedicine · 2025Article
- A multimodal whole-slide foundation model for pathology.Nature medicine · 2025Article
- Pancancer outcome prediction via a unified weakly supervised deep learning model.Signal transduction and targeted therapy · 2025Article
- Deep learning-based histomorphological subtyping and risk stratification of small cell lung cancer from hematoxylin and eosin-stained whole slide images.Genome medicine · 2025Article
- Cell graph analysis in hepatocellular carcinoma: predicting local recurrence and identifying spatial relationship biomarkers.NPJ precision oncology · 2025Article
- Classifying retinal diseases via pyramid vision graph convolutional network for optical coherence tomography images.Biomedical optics express · 2025Article
- Denoised recurrence label-based deep learning for prediction of postoperative recurrence risk and sorafenib response in HCC.BMC medicine · 2025Article
- Bi-level graph learning unveils prognosis-relevant tumor microenvironment patterns in breast multiplexed digital pathology.Patterns (New York, N.Y.) · 2025Article
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11 authors.
Funding
Abstract
Methods of computational pathology applied to the analysis of whole-slide images (WSIs) do not typically consider histopathological features from the tumour microenvironment. Here, we show that a graph deep neural network that considers such contextual features in gigapixel-sized WSIs in a semi-supervised manner can provide interpretable prognostic biomarkers. We designed a neural-network model that leverages attention techniques to learn features of the heterogeneous tumour microenvironment from memory-efficient representations of aggregates of highly correlated image patches. We trained the model with WSIs of kidney, breast, lung and uterine cancers and validated it by predicting the prognosis of 3,950 patients with these four different types of cancer. We also show that the model provides interpretable contextual features of clear cell renal cell carcinoma that allowed for the risk-based retrospective stratification of 1,333 patients. Deep graph neural networks that derive contextual histopathological features from WSIs may aid diagnostic and prognostic tasks.
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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.