Total nodule number as an independent prognostic factor in resected stage III non-small cell lung cancer: a deep learning-powered study.

ABSTRACT

Background: Almost every patient with lung cancer has multiple pulmonary nodules; however, the significance of nodule multiplicity in locally advanced non-small cell lung cancer (NSCLC) remains unclear. Methods: We identified patients who had undergone surgical resection for stage I-III NSCLC at the Peking University People's Hospital from 2005 to 2018 for whom preoperative chest computed tomography (CT) scans were available. Deep learning-based artificial intelligence (AI) algorithms using convolutional neural networks (CNN) were applied to detect and classify pulmonary nodules (PNs). Maximally selected log-rank statistics were used to determine the optimal cutoff value of the total nodule number (TNN) for predicting survival. Results: A total of 33,410 PNs were detected by AI among the 2,126 participants. The median TNN detected per person was 12 [interquartile range (IQR) 7-20]. It was revealed that AI-detected TNN (analyzed as a continuous variable) was an independent prognostic factor for both recurrence-free survival (RFS) [hazard ratio (HR) 1.012, 95% confidence interval (CI) 1.002 to 1.022, P=0.021] and overall survival (OS) (HR 1.013, 95% CI 1.002 to 1.025, P=0.021) in multivariate analyses of the stage III cohort. In contrast, AI-detected TNN was not significantly associated with survival in the stage I and II cohorts. In a survival tree analysis, rather than using traditional IIIA and IIIB classifications, the model grouped cases according to AI-detected TNN (lower vs. higher log-rank P<0.001), which led to a more effective determination of survival rates in the stage III cohort. Conclusions: The AI-detected TNN is significantly associated with survival rates in patients with surgically resected stage III NSCLC. A lower TNN detected on preoperative CT scans indicates a better prognosis for patients who have undergone complete surgical resection.