整合病理图像与组学数据的机器学习模型可更准确预测癌症患者生存期。
Machine learning-based multimodal prognostic models integrating pathology images and high-throughput omic data for overall survival prediction in cancer: a systematic review
- 融合全切片病理图像和高通量组学数据，用机器学习预测生存期。
- 模型预测准确率c-指数达0.550至0.857，多模态优于单一模态。
- 适合关注精准肿瘤学和临床转化的研究者，需提升方法严谨性。
将组织病理学与分子数据结合的多模态机器学习在癌症预后评估中展现潜力。本研究系统回顾了整合全切片图像（WSIs）与高通量组学数据预测总体生存期的研究。通过检索EMBASE、PubMed和Cochrane CENTRAL（截至2024年12月8日），并辅以引文筛查，共识别出符合条件的研究。数据提取采用CHARMS标准，偏倚风险评估使用PROBAST+AI，综合分析遵循SWiM和PRISMA 2020指南。研究注册于PROSPERO（CRD42024594745）。共纳入48项研究（均发表于2017年后），涵盖19种癌症类型，全部基于《癌症基因图谱》（TCGA）数据。方法包括正则化Cox回归（n=4）、传统机器学习（n=13）和深度学习（n=31）。报告的c-指数范围为0.550–0.857，多模态模型普遍优于单模态模型。但所有研究均存在偏倚风险高或不明确、外部验证不足、临床应用价值未充分探讨的问题。尽管多模态WSI-omics生存预测领域发展迅速且前景良好，仍需提升方法学严谨性、拓展数据集覆盖范围，并加强临床评估。本研究由英国国家癌症研究所（NPIC）、利兹教学医院NHS信托基金（项目104687）资助，获英国研究与创新署工业战略挑战基金支持。
原文摘要 · Abstract (English)
Multimodal machine learning integrating histopathology and molecular data shows promise for cancer prognostication. We systematically reviewed studies combining whole slide images (WSIs) and high-throughput omics to predict overall survival. Searches of EMBASE, PubMed, and Cochrane CENTRAL (12/08/2024), plus citation screening, identified eligible studies. Data extraction used CHARMS; bias was assessed with PROBAST+AI; synthesis followed SWiM and PRISMA 2020. Protocol: PROSPERO (CRD42024594745). Forty-eight studies (all since 2017) across 19 cancer types met criteria; all used The Cancer Genome Atlas. Approaches included regularised Cox regression (n=4), classical ML (n=13), and deep learning (n=31). Reported c-indices ranged 0.550-0.857; multimodal models typically outperformed unimodal ones. However, all studies showed unclear/high bias, limited external validation, and little focus on clinical utility. Multimodal WSI-omics survival prediction is a fast-growing field with promising results but needs improved methodological rigor, broader datasets, and clinical evaluation. Funded by NPIC, Leeds Teaching Hospitals NHS Trust, UK (Project 104687), supported by UKRI Industrial Strategy Challenge Fund.
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