Background: Computed tomography (CT) imaging is used to inform staging and treatment decisions for stage I non-small cell lung cancer (NSCLC) patients. We have previously used deep learning applied to pretreatment CTs to generate an imaging-based prognostication (IPRO) score that automatically quantifies mortality risk and stratifies patients beyond tumor, node, metastasis (TNM) substages. Here we present validation data of its prognostic impact. Methods: We developed a fully automated deep learning model, IPRO, designed to process a CT scan, localize the 36cm³ space centered on the lungs, and learn prognostic imaging features to predict mortality risk. IPRO was trained on pretreatment CTs acquired from 1,696 patients treated for NSCLC at a tertiary care center between 2004 and 2018. We withheld 20% of the cases for validation, including 162 patients that were diagnosed with stage I NSCLC by clinical staging. We evaluated IPRO’s ability to stratify stage I NSCLC patients into mortality risk quintiles using the Cox proportional hazards model and assessed differences in median overall survival (mOS). Results: Of the 162 stage I NSCLC patients in the validation set, the mOS was 68.5 months (95% CI 66.7-69.6), 85 (52.5%) were male, and 125 (77.2%) were diagnosed with stage IA. Of these, 111 patients received surgery, 40 received radiotherapy (RT), 9 received surgery + adjuvant systemic therapy (ST), and 2 patients received surgery + ST + RT. According to IPRO, the patients predicted to have the highest risk had significantly increased 5-year mortality compared to those predicted to have the lowest risk (OR 8.4, 95% CI 2.4-29.2, p < 0.01); median survival 48.0 months and 69.5 months, respectively. Conclusions: Deep learning applied to pretreatment CTs provides personalized prognostic insights for stage I NSCLC beyond current TNM staging.

HR, hazard ratio; IPRO, imaging-based prognostication; mOS, median overall survival; RT, radiation therapy; ST, systemic therapy