Radiomics-Based Model Using Tumor and Peritumoral Features with Semi-Supervised and Privileged Learning for Metastatic Risk Prediction in Lung Cancer: A Multi-Site Study

The rim size analysis provided insights into tumor aggressiveness and metastasis occurrence, especially when combined with the patient's stage and tumor characteristics. Integrating semi-supervised and privileged learning techniques significantly enhanced SVM models' performance in identifying lung cancer patients at risk for metastasis, outperforming human-derived predictions in a small external dataset. The model's generalizability was demonstrated on an external validation set from two different regions. More extensive validation is necessary to confirm these findings.