3D CT 基础模型与无监督适应用于头颈癌复发预测的实证研究
Empirical investigation of 3D CT Foundation Models and Unsupervised Adaptation for Head and Neck Cancer Recurrence Prediction
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中文总结 AI 辅助
该研究通过两个含 3644 名患者的公开数据集，实证评估 3D CT 基础模型用于头颈癌复发预测的泛化能力，发现其跨场景泛化困难，影像与临床数据结合的预后方法最准确。
中文摘要 AI 辅助
3D CT 基础模型的快速兴起为基于 CT 影像的预测建模开辟了新途径，相较于存在可重复性问题且对采集方案变化敏感的传统放射组学，它是极具吸引力的替代方案。然而，随着这些模型的可用性提升，迫切需要评估其学习到的表征在不同临床场景中的泛化能力，以及是否需要针对特定下游任务进行调整以充分发挥其潜力。为解决这些问题，我们在总计 3644 名患者的两个公开数据集上，对多种 3D CT 基础模型用于头颈癌无复发生存预测进行了基准测试，评估了多种适应策略和模态融合机制。研究发现，识别能在不同影像分布间一致泛化的特征仍存在持续困难，外部验证队列上的性能显著下降证实了这一点。最终，将影像特征与临床数据结合仍是预后预测最准确的方法，不过在不同临床场景中实现通用泛化，对当前一代模型而言仍是重大挑战。
英文摘要
The rapid emergence of 3D CT foundation models has opened new avenues for predictive modeling from CT imaging, offering a compelling alternative to traditional radiomics which is known to suffer from reproducibility issues and sensitivity to acquisition protocol variations. Yet, as these models grow in availability, a critical need arises to evaluate how well their learned representations generalize across diverse clinical settings and whether adaptation to specific downstream tasks is necessary to unlock their full potential. To address these questions, we benchmarked several 3D CT foundation models for predicting recurrence-free survival in head and neck cancer across two public datasets totaling 3,644 patients, evaluating various adaptation strategies and modality fusion mechanisms. Our findings reveal persistent difficulty in identifying features that generalize consistently across different imaging distributions, as evidenced by significant performance drops on external validation cohorts. Ultimately, the integration of imaging features with clinical data remains the most accurate approach for prognostic prediction, though achieving universal generalization across varied clinical contexts continues to represent a substantial challenge for the current generation of models.
发表机构
- University of Lille（里尔大学）
- Academic Department of Radiation Oncology, Centre Oscar Lambret（奥斯卡·兰布雷特中心放射肿瘤学学术部）
- Junia（朱尼亚学院）
- Centrale Lille（里尔中央理工学院）
- IMT Nord Europe, Institut Mines-Télécom（北欧电信学院、矿业电信学院）
机构由 AI 辅助整理，请以论文原文为准。
