用大模型提取影像特征，预测头颈癌远处转移风险更准且省专家标注。
Comparing the Performance of Foundation Model Derived Embeddings with Traditional Approaches for Distant Metastasis Prediction in Head and Neck Cancer
- 用CT基础模型直接提取影像特征，无需人工勾画肿瘤区域。
- 大模型特征在2327例患者上达AUC 0.791，优于传统方法。
- 适合临床快速部署，减少对专业标注和复杂预处理的依赖。
背景：早期预测头颈部癌症（HNC）远处转移（DM）风险有助于及时干预，改善治疗效果。当前许多机器学习方法依赖于感兴趣区域（如肿瘤分割）的先验知识，这需要专家参与、耗时长且存在人为差异。近期医学影像基础模型被开发用于特定成像模态，通过提取模态相关特征来简化下游预测任务。目的：本研究评估使用基础模型作为特征提取器预测HNC患者DM风险的效果，并与需先验知识的传统方法进行比较。方法：基于RADCURE数据集的2327例术前CT图像，构建三类特征集：放射组学特征、基于深度学习的特征及基于CT基础模型的特征。每类特征单独输入多层感知机（MLP）以预测DM风险。结果：基于CT基础模型的模型表现最佳，其受试者工作特征曲线下面积（AUC）为0.791，高于放射组学模型（AUC 0.772）和深度学习模型（AUC 0.753）。该模型性能接近融合放射组学与深度学习特征的模型（AUC 0.794）。结论：基于基础模型的特征是传统放射组学的有力替代方案，可减少领域专业知识和大量标注数据的需求，其极低的预处理要求使其更具可访问性和可扩展性。
原文摘要 · Abstract (English)
Background: Early prediction of distant metastasis (DM) risk in head and neck cancer (HNC) can enable timely interventions that may improve treatment outcomes. Many current machine learning methods rely on prior knowledge of the region of interest such as tumor segmentations, which require expert knowledge, is time-consuming and introduces user-dependent variability. Medical image-based foundation models have recently been developed for specific imaging modalities to streamline down-stream prediction tasks by extracting modality-relevant features. Purpose: In this study, we evaluate the effectiveness of using a foundation model as the feature extractor to predict DM risk in HNC patients and compare its performance with traditional approaches that require prior knowledge on the regions of interest. Methods: Preoperative CT images of 2327 patients from the RADCURE dataset were used. Three features-sets were created including radiomics, deep-learning based features, and CT Foundation derived features. The feature-sets were used individually in a multi-layer perceptron (MLP) to predict DM risk. Results: The model using CT Foundation embeddings outperformed the radiomics and deep learning-based models, achieving a Receiver Operating Characteristic Area Under the Curve (AUC) of 0.791, compared to AUC values of 0.772 and 0.753 for the radiomics and deep learning-based models, respectively. The CT Foundation based model had similar performance to a model that combined the use of radiomics and deep learning-based features that achieved an AUC of 0.794. Conclusions: Features based on foundation models offer a promising alternative to traditional radiomics while reducing the need for domain expertise and extensively annotated datasets. Their minimal preprocessing requirements also make them a more accessible and scalable option.
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