Un modelo multirradiómico de hábitats multihospedaje para predecir la respuesta a la inmunoterapia en CPNM avanzado

A robust predictive biomarker is critical for identifying patients with NSCLC who may benefit from immunotherapy. This study developed a CT-based habitat model using 590 advanced NSCLC cases. The model was constructed in contrast-enhanced CT images and validated on an independent cohort with non-contrast CT. Tumor volumes were segmented into three subregions via K-means clustering. Radiomic features were extracted from each habitat and used to build predictive models with six machine learning classifiers. The ExtraTrees-based habitat model demonstrated superior predictive performance in the test cohort (AUC = 0.814). Compared to traditional radiomics, 3D deep learning, clinical, and PD-L1 expression models, the habitat model maintained strong predictive advantages, enabling efficient prediction of immunotherapy benefit and aiding in the identification of suitable patients for personalized.