ArticleGigaScience2025
SurGen: 1020 H&E-stained whole-slide images with survival and genetic markers.
Article in GigaScience, 2025. 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.
- A Generalizable Multimodal Model for Treatment-Stratified Risk and Survival Assessment under Real-World Constraints: A Multi-Center Study of Colorectal Cancer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Ensemble learning of pathology foundation models for precision oncology.Cancer cell · 2026Article
- Artificial Intelligence-Based Prediction of Molecular Alterations in Colorectal Cancer Using Routine H&E Whole-Slide Images.International journal of molecular sciences · 2026Review
- A protocol for evaluating robustness to H&E staining variation in computational pathology models.Journal of pathology informatics · 2026Article
- Review
- HISTAI: a valuable dataset with a valuable lesson.The journal of pathology. Clinical research · 2026Article
- Systematically decoding pathological morphologies and molecular profiles with unified multimodal embedding.Nature methods · 2026Article
- Meta-encoder: a unified integration framework for multiple pathological foundation models in cancer detection.Nature communications · 2026Article
- Diversity over scale: Whole-slide image variety enables H&E foundation model training with fewer patches.Journal of pathology informatics · 2026Article
- STPath: a generative foundation model for integrating spatial transcriptomics and whole-slide images.NPJ digital medicine · 2025Article
- Pancancer outcome prediction via a unified weakly supervised deep learning model.Signal transduction and targeted therapy · 2025Article
- Article
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5 authors.
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Abstract
backgroundCancer remains one of the leading causes of morbidity and mortality worldwide. Comprehensive datasets that combine histopathological images with genetic and survival data across various tumour sites are essential for advancing computational pathology and personalised medicine.
resultsWe present SurGen, a dataset comprising 1,020 H&E-stained whole-slide images (WSIs) from 843 colorectal cancer cases. The dataset includes detailed annotations for key genetic mutations (KRAS, NRAS, BRAF) and mismatch repair status, as well as survival data for 426 cases. We illustrate SurGen's utility with a proof-of-concept model that predicts mismatch repair status directly from WSIs, achieving a test area under the receiver operating characteristic curve of 0.8273. These preliminary results underscore the dataset's potential to facilitate research in biomarker discovery, prognostic modelling, and advanced machine learning applications in colorectal cancer and beyond.
conclusionsSurGen offers a valuable resource for the scientific community, enabling studies that require high-quality WSIs linked with comprehensive clinical and genetic information on colorectal cancer. Our initial findings affirm the dataset's capacity to advance diagnostic precision and foster the development of personalised treatment strategies in colorectal oncology. Data available online: https://doi.org/10.6019/S-BIAD1285.
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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.