Colorectal liver metastases (CRLM) are a major cause of cancer-related mortality, and reliable detection on CT remains challenging in multi-centre settings. We developed a foundation model-based AI pipeline for patient-level classification and lesion-level detection of CRLM on contrast-enhanced CT, integrating uncertainty quantification and explainability. CT data from the EuCanImage consortium (n=2437) and an external TCIA cohort (n=197) were used. Among several pretrained models, UMedPT achieved the best performance and was fine-tuned with an MLP head for classification and an FCOS-based head for lesion detection. The classification model achieved an AUC of 0.90 and a sensitivity of 0.82 on the combined test set, with a sensitivity of 0.85 on the external cohort. Excluding the most uncertain 20 percent of cases improved AUC to 0.91 and balanced accuracy to 0.86. Decision curve analysis showed clinical benefit for threshold probabilities between 0.30 and 0.40. The detection model identified 69.1 percent of lesions overall, increasing from 30 percent to 98 percent across lesion size quartiles. Grad-CAM highlighted lesion-corresponding regions in high-confidence cases. These results demonstrate that foundation model-based pipelines can support robust and interpretable CRLM detection and classification across heterogeneous CT data.

翻译：结直肠肝转移（CRLM）是癌症相关死亡的主要原因，在多中心环境下实现CT影像的可靠检测仍具挑战性。我们开发了一种基于基础模型的人工智能流程，用于增强CT影像中患者层面的CRLM分类和病灶层面的检测，并整合了不确定性量化和可解释性方法。研究使用了来自EuCanImage联盟（n=2437）和外部TCIA队列（n=197）的CT数据。在多个预训练模型中，UMedPT取得了最佳性能，并通过MLP头部进行微调用于分类，以及基于FCOS的头部用于病灶检测。分类模型在合并测试集上实现了0.90的AUC和0.82的灵敏度，在外部队列上的灵敏度为0.85。排除不确定性最高的20%病例后，AUC提升至0.91，平衡准确率提升至0.86。决策曲线分析显示，在0.30至0.40的阈值概率区间内具有临床获益。检测模型总体识别了69.1%的病灶，在不同病灶大小四分位数区间内，识别率从30%提升至98%。Grad-CAM在高置信度病例中突出了与病灶对应的区域。这些结果表明，基于基础模型的流程能够支持跨异构CT数据的稳健且可解释的CRLM检测与分类。