AI 中文总结
本研究以RADCURE数据集2327例HNC患者术前CT图像为基础，对比CT基础模型嵌入、放射组学等特征集预测远处转移的性能，发现CT基础模型嵌入性能更优，可作为传统放射组学的替代方案
AI 中文摘要
背景：早期预测头颈部癌（HNC）患者的远处转移（DM）风险，可实现及时干预以改善治疗结局。当前许多机器学习方法依赖感兴趣区域（如肿瘤分割）的先验知识，这类方法需要专业知识、耗时且引入用户相关变异性。近期已开发出针对特定成像模态的医学图像基础模型，可通过提取模态相关特征简化下游预测任务。目的：本研究评估将基础模型作为特征提取器预测HNC患者DM风险的有效性，并将其性能与需要感兴趣区域先验知识的传统方法进行比较。方法：使用RADCURE数据集中2327例患者的术前CT图像，创建三组特征集，包括放射组学特征、深度学习特征及CT基础模型衍生特征，将各特征集分别输入多层感知器（MLP）以预测DM风险。结果：使用CT基础模型嵌入的模型性能优于放射组学模型和深度学习模型，其受试者工作特征曲线下面积（AUC）达0.791，而放射组学模型和深度学习模型的AUC分别为0.772和0.753；CT基础模型模型的性能与结合放射组学和深度学习特征的模型相近，后者AUC为0.794。结论：基于基础模型的特征为传统放射组学提供了有前景的替代方案，同时减少了对领域专业知识和大量标注数据集的需求，其最小的预处理要求也使其成为更易获取且可扩展的选择。
英文摘要
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.
Comments27 pages including supplemental materials, 5 main figures, 2 supplemental figures, 5 main tables, 7 supplemental tables. Poster Abstract at 2026 AAPM Meeting and Exhibition
