SurGen整合1020张结直肠癌病理切片与基因、生存数据，助力精准医疗研究。
SurGen: 1020 H&E-stained Whole Slide Images With Survival and Genetic Markers
- 构建包含1020张全切片图像的多模态数据集，关联基因突变与生存信息。
- 基于WSI预测错配修复状态，测试AUC达0.8273，验证数据可用性。
- 适合癌症生物标志物发现、预后建模及人工智能辅助诊断研究者使用。
癌症仍是全球主要致病和致死原因。整合组织病理图像与基因、生存数据的综合性数据集对推动计算病理学和个性化医疗至关重要。本文提出SurGen数据集，包含来自843例结直肠癌病例的1,020张H&E染色全切片图像（WSIs），涵盖关键基因突变（KRAS、NRAS、BRAF）与错配修复状态的详细标注，以及426例患者的生存数据。我们通过一个概念验证模型，直接从WSIs预测错配修复状态，测试集AUC达0.8273。初步结果表明该数据集在生物标志物发现、预后建模及深度学习应用方面具有潜力。SurGen为科学界提供了一个高质量、多维度的资源，支持结直肠癌及相关领域研究。数据可在线获取：https://doi.org/10.6019/S-BIAD1285。
原文摘要 · Abstract (English)
Cancer 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. We 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. SurGen 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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