Harnessing Native-Resolution 2D Embeddings for Lung Cancer Classification: A Feasibility Study with the RAD-DINO Self-supervised Foundation Model

Low‑dose CT (LDCT) screening reduces lung cancer mortality but yields high false‑positive findings, motivating practical AI that aligns with slice‑based clinical review. We evaluate a label‑efficient 2D pipeline that uses frozen RAD‑DINO embeddings from native‑resolution axial slices and a lightweight multilayer perceptron for patient‑level risk estimation via mean aggregation and isotonic calibration. Using the NLST CT arm with outcomes defined over a 0-24‑month window, we construct a fixed patient‑level split (one CT per patient; no cross‑split leakage) and perform 25 repeated imbalanced test draws (~ 6% prevalence) to approximate screening conditions. At screening prevalence, RAD‑DINO + MLP achieves PR‑AUC = 0.705 (calibrated; raw 0.554) and ROC‑AUC = 0.817 (raw; 0.736 calibrated), with improved probability reliability following calibration; operating points are selected on validation and reported on test. For secondary ablations only, a near‑balanced cohort (N = 1984) yields accuracy 0.966, precision 0.974, recall 0.973, F1 0.973, and ROC‑AUC 0.912. Beyond classification, retrieval with triplet‑fine‑tuned embeddings attains Precision@5 = 0.853. Interpretability analyses show that cancer cases sustain higher top‑k slice scores and that directional SHAP concentrates on a small subset of high‑probability slices; label‑colored t‑SNE provides qualitative views of embedding structure. Limitations include single‑cohort evaluation, lack of Lung‑RADS labels in public NLST, and a CXR → CT pretraining shift; future work will pursue external validation and CT‑native self‑supervised continuation. Overall, frozen 2D foundation embeddings provide a strong, transparent, and computationally practical starting point for LDCT screening workflows under realistic prevalence.