Abstract
<jats:p>Accurate overall survival (OS) prediction in non-small cell lung cancer (NSCLC) is crucial but challenging due to high-dimensional 3D computed tomography (CT) data, limited annotations, and time-to-event outcomes. Traditional 3D CNNs are computationally expensive and prone to overfitting on small datasets. We propose a lightweight framework that aggregates 2D CT slice embeddings via soft attention to form a 3D patient representation. In our approach, features are extracted with EfficientNetB0, and DeepHit models time-to-event survival. Validated on LUNG1 (415 patients), our method outperforms 3D ResNet (+0.077) and alternative aggregation strategies (+0.005) in time-dependent concordance index (Ctd-index). Furthermore, transfer learning from LUNG1 improves performance on the small private CLARO dataset (0.579 vs 0.503). This shows that 2D CNNs with soft attention provide a computationally efficient yet effective alternative to 3D CNN architectures for NSCLC OS prediction, with a substantially lower computational cost (54.3 GFLOPs vs. 2924.6 GFLOPs for ResNet3D-18).</jats:p>