用医学影像基础模型提升肺癌生存预测，数据少也能用。
CT-CLIP Representations for Multimodal Lung Cancer Survival Prediction
- 用CT-CLIP提取影像和临床数据特征，不需大量标注数据
- 冻结模型+轻量头设计，在242例患者上表现优于临床基线
- 适合医疗数据稀缺场景，可帮医生做更准的预后判断
准确的预后预测对肺癌治疗规划至关重要，但深度学习驱动的生存建模常受限于高质量影像队列与可靠结局数据的稀缺。本研究评估领域专用基础模型CT-CLIP在数据受限临床环境下的多模态生存预测能力。基于242例确诊肺癌患者的术前CT影像与临床变量，我们测试了冻结编码器、全微调及低秩适应等适配策略，并进行模态消融与临床/多模态基线对比。结果表明，冻结的CT-CLIP模型结合可训练的轻量级生存头，性能优于临床基线，且与其它多模态方法相当或更优，能有效区分临床上有意义的高危与低危人群。
原文摘要 · Abstract (English)
Accurate prognosis prediction is important for treatment planning in lung cancer, but deep learning-driven survival modelling is often limited by the scarcity of curated imaging cohorts with reliable outcome data. This study evaluates whether representations from a domain-specific foundation model can be used for multimodal survival prediction in data-constrained clinical settings. We assess the foundation model CT-CLIP as a feature extractor for pretreatment computed tomography images and clinical variables from 242 diagnosed lung cancer patients. The evaluation includes adaptation strategies based on frozen encoders, full fine-tuning, and low-rank adaptation, together with modality ablations and comparisons with clinical and multimodal baselines. The results show that a frozen CT-CLIP model combined with a trainable lightweight survival head outperforms the clinical baseline and achieves comparable or improved performance relative to other multimodal approaches, and separates patients into clinically meaningful high- and low-risk groups.
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