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

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

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.

Informations de copyright

2022 Annals of Translational Medicine. All rights reserved.

Déclaration de conflit d'intérêts

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://atm.amegroups.com/article/view/10.21037/atm-21-3231/coif). XC reports funding from the National Natural Science Foundation of China (82002983). DW, JS, and WT were employed by the company Beijing Infervision Technology Co., Ltd. The other authors have no conflicts of interest to declare.