Interpretable habitat radiomics based nomogram for predicting T790M mutation in non-small cell lung cancer with brain...

Spatial heterogeneity within the tumor drives T790M resistance mutation. This study aimed to develop a habitat radiomics-based nomogram for predicting T790M resistance mutation for non-small cell lung cancer (NSCLC) patients with brain metastases (BM).

A total of 301 patients from Beijing Chest Hospital, Capital Medical University and Shandong Cancer Hospital and Institute, Shandong First Medical University were retrospectively collected. Tumors were manually delineated on contrast-enhanced T1-weighted (T1-CE), and the segmentation was combined with an automatically generated 5 mm peritumoral region to form the whole region of interest (ROI). Predictive models were established using radiomics features extracted from intratumoral, peritumoral, whole ROI, and habitat-generated subregions within the whole ROI. A nomogram was developed by integrating a representative radiomics signature and meaningful clinical factors. Model performance was assessed using the area under the receiver operating characteristic (ROC) curve (AUC) and decision curve analysis (DCA). SHapley Additive exPlanations (SHAP) was used to interpret feature distribution in the habitat model, and proteomics analysis was performed to explore the biological mechanisms of the nomogram.

The habitat model based on three subregions achieved higher AUCs of 0.924 and 0.902 in the training and test cohorts, respectively. The habitat radiomics-based nomogram yielded the highest AUCs of 0.943 and 0.931 in the two cohorts, separately. Meanwhile, clinical and other radiomics models showed moderate performance with AUCs ranging from 0.644 to 0.800 in the test cohort. Proteomics analysis revealed that RNA processing and splicing may represent the most critical pathway distinguishing high- and low-risk groups.

Habitat imaging combining tumoral and peritumoral radiomics was beneficial for identifying T790M resistance mutation in NSCLC BM patients. Furthermore, a habitat radiomics-based nomogram can further improve the predictive ability.