整合五类数据预测肾癌生存期，模型准确率超80%。
A multimodal ensemble approach for clear cell renal cell carcinoma treatment outcome prediction
- 融合临床、基因组、病理图像等五类数据构建集成模型
- 预测3年死亡和复发的准确率分别达83.1%和86.2%
- 适合肿瘤精准治疗与预后评估研究者参考
目的：可靠的透明细胞肾细胞癌（ccRCC）预后模型可提升个性化治疗水平。我们开发了多模态集成模型（MMEM），整合治疗前临床数据、多组学数据及组织病理全切片图像（WSI）数据，用于预测ccRCC患者的总生存期（OS）和无病生存期（DFS）。方法：分析来自癌症基因组图谱肾透明细胞癌（TCGA-KIRC）数据集的226例患者，包含OS、DFS随访数据及五类数据：临床数据、WSI以及三种多组学数据（mRNA、miRNA、DNA甲基化）。为OS和DFS分别建立生存模型。临床与多组学数据采用前向特征选择的Cox比例风险（CPH）模型；WSI特征通过ResNet及三个通用基础模型提取，使用基于深度学习的CPH模型预测生存。各模型风险评分依据训练表现加权融合。结果：通过一致性指数（C-index）和AUROC评估性能。临床特征模型在OS和DFS任务中权重最高。在基于WSI的模型中，通用基础模型（UNI）表现最佳。最终的MMEM模型优于单模态模型，达到OS的C-index为0.820，DFS为0.833，3年死亡的AUROC为0.831，复发的AUROC为0.862。以预测风险中位数分层高低风险组，log-rank检验显示其在OS和DFS上的表现均优于单模态模型。结论：MMEM是首个整合五类数据的ccRCC预后模型，显著提升预测能力，若经独立验证，有望辅助患者管理。
原文摘要 · Abstract (English)
Purpose: A reliable cancer prognosis model for clear cell renal cell carcinoma (ccRCC) can enhance personalized treatment. We developed a multi-modal ensemble model (MMEM) that integrates pretreatment clinical data, multi-omics data, and histopathology whole slide image (WSI) data to predict overall survival (OS) and disease-free survival (DFS) for ccRCC patients. Methods: We analyzed 226 patients from The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) dataset, which includes OS, DFS follow-up data, and five data modalities: clinical data, WSIs, and three multi-omics datasets (mRNA, miRNA, and DNA methylation). Separate survival models were built for OS and DFS. Cox-proportional hazards (CPH) model with forward feature selection is used for clinical and multi-omics data. Features from WSIs were extracted using ResNet and three general-purpose foundation models. A deep learning-based CPH model predicted survival using encoded WSI features. Risk scores from all models were combined based on training performance. Results: Performance was assessed using concordance index (C-index) and AUROC. The clinical feature-based CPH model received the highest weight for both OS and DFS tasks. Among WSI-based models, the general-purpose foundation model (UNI) achieved the best performance. The final MMEM model surpassed single-modality models, achieving C-indices of 0.820 (OS) and 0.833 (DFS), and AUROC values of 0.831 (3-year patient death) and 0.862 (cancer recurrence). Using predicted risk medians to stratify high- and low-risk groups, log-rank tests showed improved performance in both OS and DFS compared to single-modality models. Conclusion: MMEM is the first multi-modal model for ccRCC patients, integrating five data modalities. It outperformed single-modality models in prognostic ability and has the potential to assist in ccRCC patient management if independently validated.
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