这篇论文用338例训练、295例测试的严格基准，告诉你肺CT分析到底该选Curia还是放射组学，以及为什么任务不同最优方案不同。
该研究在5个任务（肿瘤体积与分期分类、2年生存预测、组织学分类、年龄预测）上系统对比了5种特征提取器（Curia、Curia-2、DINOv3、Radiomics2D、Radiomics3D）、7种分类头（TabPFN、TabICL、XGBoost、CatBoost、Random Forest、logistic regression、Ridge）和3种分割方案。模型在LUNG1（n=338）上训练，在内部测试集（n=84）和外部LUNG2队列（n=211）上评估，以最差跨队列性能为主要指标。结果表明，主导设计因素与任务相关：分割影响体积和分期分类，而分类器选择主导生存、组织学和年龄预测。放射组学在肿瘤体积、分期和生存上具有竞争力（部分由于标签推导效应），Curia变体在生存上达到相近峰值分数，DINOv3在所有任务上略逊一筹。推荐Curia结合肿瘤分割和CatBoost头作为安全默认方案，在三个主要临床任务上平均排名最佳。
Foundation Models vs. Radiomics for Lung Computed Tomography: A Benchmark of Feature Extractors, Classification Heads, and Segmentation Choices
Radiomics is the established approach for CT-based lung cancer phenotyping, yet comparisons with foundation models rarely isolate contributions of feature extractor, classification head, and segmentation choice, or test cross-cohort robustness. We benchmark five feature extractors (Curia, Curia-2, DINOv3, Radiomics2D, Radiomics3D), seven classification heads (TabPFN, TabICL, XGBoost, CatBoost, Random Forest, logistic regression, Ridge), and three segmentation regimes on five tasks: tumor volume and stage classification, 2-year survival prediction, histology classification, and age prediction. Models are trained on LUNG1 (n=338) and evaluated on an internal test set (n=84) and the external LUNG2 cohort (n=211), with worst-case cross-cohort performance as the primary metric. The dominant design factor is task-dependent: segmentation drives volume and stage classification, while classifier choice drives survival, histology, and age prediction. Radiomics is competitive for tumor volume, tumor stage and survival (partly due to label-derivation effects for the former); Curia variants reach comparable peak scores for survival; DINOv3 falls slightly short across tasks. Patch and slice aggregation have negligible impact. We recommend Curia with tumor segmentation and a CatBoost head as a safe default, achieving the best mean rank across the three primary clinical tasks, though task-specific selection consistently outperforms any cross-task default. When tumor delineations are unavailable, Curia-2 with lung segmentation and logistic regression offers a competitive alternative. All pipelines use a two-stage design suited to small cohort sizes where end-to-end fine-tuning would risk overfitting.