跨模态对比学习：基于组织病理学与CT的肾细胞癌自动分级

中文摘要 AI 辅助

背景：透明细胞肾细胞癌（ccRCC）表现出显著的临床异质性，准确的分级评估对于风险分层和治疗计划至关重要。然而，传统分级需要侵入性组织取样。我们开发了RCC-Align，一种跨模态对比学习框架，在训练期间利用配对的组织病理学和计算机断层扫描（CT）数据来改善基于CT的非侵入性ccRCC分级预测。方法：RCC-Align通过对比跨模态目标对齐配对的全切片组织病理学图像（WSIs）和CT扫描，将分级判别信息从微观组织形态转移到宏观放射学表征。该框架在配对的TCGA和CPTAC队列上使用患者级五折交叉验证进行训练和评估。低级别与高级别ccRCC分类的性能与仅CT基线（DINOv2-Base和DINOv2-Finetuned）以及基于WSI的参考模型（GigaPath-Finetuned）进行了比较。跨模态对齐通过余弦相似性分析进行评估。结果：RCC-Align实现了AUC为0.601（95% CI，0.524-0.673）和AUPRC为0.599（95% CI，0.541-0.676），优于DINOv2-Finetuned（AUC 0.545；AUPRC 0.543），且低级别预测显著改善（p = 0.004）。RCC-Align还展示了与基线相比更强的配对WSI-CT嵌入对齐。基于WSI的GigaPath参考实现了AUC为0.719。结论：病理引导的对比学习改善了基于CT的ccRCC分级，同时在推理时仅需CT。当活检不安全、不可行或受瘤内异质性限制时，这种方法可补充组织诊断。在临床转化之前，需要在更大规模、多机构队列中进行外部测试验证。

英文摘要

Background: Clear cell renal cell carcinoma (ccRCC) exhibits substantial clinical heterogeneity, and accurate grade assessment is essential for risk stratification and treatment planning. However, conventional grading requires invasive tissue sampling. We developed RCC-Align, a cross-modal contrastive learning framework that leverages paired histopathology and computed tomography (CT) data during training to improve noninvasive CT-based ccRCC grade prediction. Methods: RCC-Align aligns paired whole-slide histopathology images (WSIs) and CT scans through contrastive cross-modal objectives, transferring grade-discriminative information from microscopic tissue morphology to macroscopic radiologic representations. The framework was trained and evaluated on paired TCGA and CPTAC cohorts using patient-level five-fold cross-validation. Performance for low- versus high-grade ccRCC classification was compared against CT-only baselines (DINOv2-Base and DINOv2-Finetuned) and a WSI-based reference model (GigaPath-Finetuned). Cross-modal alignment was assessed using cosine similarity analysis. Results: RCC-Align achieved an AUC of 0.601 (95% CI, 0.524-0.673) and AUPRC of 0.599 (95% CI, 0.541-0.676), outperforming DINOv2-Finetuned (AUC 0.545; AUPRC 0.543) with significantly improved low-grade prediction (p = 0.004). RCC-Align also demonstrated stronger paired WSI-CT embedding alignment compared with baselines. The WSI-based GigaPath reference achieved an AUC of 0.719. Conclusion: Pathology-guided contrastive learning improves CT-based ccRCC grading while requiring only CT at inference. This approach may complement tissue diagnosis when biopsy is unsafe, infeasible, or limited by intratumoral heterogeneity. Validation in larger, multi-institutional cohorts with external testing is needed before clinical translation.

发表机构

• Dartmouth College（达特茅斯学院）
• Geisel School of Medicine at Dartmouth（达特茅斯盖泽尔医学院）
• Massachusetts General Hospital（麻省总医院）
• Dartmouth-Hitchcock Medical Center（达特茅斯-希契科克医疗中心）
• Memorial Sloan Kettering Cancer Center（纪念斯隆-凯特琳癌症中心）

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