3D deep learning model to predict the recurrence of stage IA invasive lung adenocarcinoma after sub-lobar resection: a multicenter retrospective cohort study.

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Abstract

The purpose of this study was to investigate the efficacy of a three-dimensional (3D) deep learning (DL) model in predicting recurrence risk of stage IA invasive lung adenocarcinoma (ILADC) after sub-lobar resection (SLR). A total of 287 stage IA ILADC patients were assigned to training and internal validation sets (4:1), with an external test cohort of 112 patients from two institutions. Three clinical models, five 3D DL models and a combined clinic-radiological-DL model were developed. Model performance was compared to identify the best-performing one. Patients were stratified into high/low-risk groups using the optimal predictive probability threshold from the best model. Survival analysis was performed to compare prognosis between groups. Furthermore, the pathological-molecular characteristics of tumors were compared between high/low-risk groups. Among clinical models, SVM achieved the highest AUCs (training: 0.819, internal validation: 0.785, and external testing: 0.758). The 3D VGG-16 DL model outperformed others with AUCs of 0.921, 0.856, and 0.830, respectively. The combined model yielded AUCs of 0.932, 0.882, and 0.854, respectively. Both 3D VGG-16 and the combined model showed significantly higher sensitivity than the clinical model (all p < 0.05). High-risk patients classified by 3D VGG-16 model had shorter recurrence-free survival/overall survival (all p < 0.05) and higher prevalence of micropapillary/solid-predominant growth pattern, STAS, and mutations or fusions in KRAS and ALK (all p < 0.05). 3D VGG-16 effectively predicts post-SLR recurrence risk for stage IA ILADC, serving as a potential tool to guide surgical treatment decisions.

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