Deep-Learning Model for Real-Time Prediction of Recurrence in Early-Stage Non-Small Cell Lung Cancer: A Multimodal Approach (RADAR CARE Study).

Abstract

The surveillance protocol for early-stage non-small cell lung cancer (NSCLC) is not contingent upon individualized risk factors for recurrence. This study aimed to use comprehensive data from clinical practice to develop a deep-learning model for practical longitudinal monitoring. A multimodal deep-learning model with transformers was developed for real-time recurrence prediction using baseline clinical, pathological, and molecular data with longitudinal laboratory and radiologic data collected during surveillance. Patients with NSCLC (stage I to III) who underwent surgery with curative intent between January 2008 and September 2022 were included. The primary outcome was predicting recurrence within 1 year after the monitoring point. This study demonstrates the timely provision of risk scores (RADAR score) and determined thresholds and the corresponding AUC. A total of 14,177 patients were enrolled (10,262 with stage I, 2,380 with stage II, and 1,703 with stage III). The model incorporated 64 clinical-pathological-molecular factors at baseline, along with longitudinal laboratory and computed tomography imaging interpretation data. The mean baseline RADAR score was 0.324 (standard deviation [SD], 0.256) in stage I, 0.660 (SD, 0.210) in stage II, and 0.824 (SD, 0.140) in stage III. The AUC for predicting relapse within 1 year of the monitoring point was 0.854 across all stages, with a sensitivity of 86.0% and a specificity of 71.3% (AUC = 0.872 in stage I, AUC = 0.737 in stage II, and AUC = 0.724 in stage III). This pilot study introduces a deep-learning model that uses multimodal data from routine clinical practice to predict relapses in early-stage NSCLC. It demonstrates the timely provision of RADAR risk scores to clinicians for recurrence prediction, potentially guiding risk-adapted surveillance strategies and aggressive adjuvant systemic treatment.