Enhanced prediction of gene mutation and risk stratification in non-small-cell lung cancer through dual-pathway fusion of radiomics and pathomics.

Purpose: This study aimed to develop and validate a multimodal combined model that integrated radiomics, pathomics and clinical features to precisely predict EGFR status and risk stratification in NSCLC. Materials and methods: We retrospectively analyzed 387 patients with NSCLC from two hospitals (the train cohort: n=193; the internal validation cohort: n=83; the external validation cohort: n=111). Radiomics models were developed using 3D CNN for the construction of deep learning radiomics (DLRadiomics). Weakly supervised learning and multi-instance learning were used to develop pathomics signature (Pathomics). We conducted an in-depth analysis of clinical features resulting in a clinical signature (Clinical). Finally, we integrated them into a comprehensive nomogram-Nomogram. The comparative analysis of all models was conducted through a comprehensive evaluation. The distribution of predictive features for Nomogram across different EGFR mutation subtypes was evaluated. The Kaplan-Meier curve was employed to assess the predictive capability of Nomogram in risk stratification among cases with survival outcomes. Results: In comparison to Clinical, DLRadiomics and Pathomics models, Nomogram exhibits superior predictive performance (the train cohort: AUC=0.986, 95%CI=0.969-1.000; the internal validation cohort: AUC=0.796, 95%CI=0.659-0.932; the external test cohort: AUC=0.850, 95%CI=0.719-0.981). Nomogram could also be used to predict effectively EGFR mutation subtype (P<0.05). In the validation and test cohorts, the log rank test proved the effectiveness of Nomogram in predicting risk stratification (P<0.05). Conclusions: We demonstrated that Nomogram which integrated radiomics, pathomics and clinical features, could be served as a noninvasive and reusable tool to precisely predict EGFR status and risk stratification in NSCLC.