Abstract
BACKGROUND: PD-1/PD-L1 immunotherapy represents the primary treatment for advanced NSCLC patients; however, response rates to this therapy vary among individuals. This dual-center study aimed to integrate habitat radiomics and multi-instance deep learning to predict durable clinical benefits from immunotherapy. METHODS: We retrospectively collected 590 NSCLC patients from two medical centers who received PD-1/PD-L1 inhibitor immunotherapy. Patients from the GMU center were divided into a training cohort (n = 375) and an internal validation cohort (n = 161) for habitat analysis and multi-instance deep learning model development. Patients from the YJ center formed an external testing cohort (n = 54) for model validation. We implemented a DenseNet121-based architecture extracting radiomics features from triplanar (axial/coronal/sagittal) tumor sequences to construct a 2.5D deep-learning dataset. Then, we fuse 2.5D features through multi-instance learning. Additionally, we use K-means clustering to divide the tumor VOI into three subregions to extract radiological features for building a Habitat model. Finally, we use the Extra-Trees classifier to construct MIL, Habitat, and Combined models, the Combined model integrating age factors into the analysis. The primary endpoint was durable clinical benefit. Finally, a separate PD-L1 expression dataset was used to compare the predictive performance of imaging models against PD-L1 status (positive/negative) and expression levels (high/low) to identify the optimal model for predicting immunotherapy clinical benefit. RESULTS: The Combined model combining Habitat, MIL, and patient age demonstrated robust DCB prediction with AUCs of 0.906(95% CI: 0.874-0.936), 0.889(95% CI: 0.826-0.948), and 0.831 (95% CI: 0.710-0.927)in training, validation, and testing cohorts respectively. Comparative analysis revealed all imaging models outperformed PD-L1 expression status (positive/negative) and levels (high/low) in predicting therapeutic response, with Habitat analysis showing superior performance to MIL alone. Notably, peritumoral structural features emerged as significant predictors of treatment efficacy. CONCLUSION: This non-invasive predictive framework provides clinically actionable insights for immunotherapy stratification, potentially overcoming limitations of current biomarker testing while highlighting the prognostic value of spatial tumor heterogeneity analysis.