Deep learning-powered 3D segmentation derives factors associated with lymphovascular invasion and prognosis in clinical T1 stage non-small cell lung cancer

Zhichao Zuo; Xiaohong Fan; Yao Tang; Yang Zhang; Xin Peng; Weihua Zeng; Ying Zeng

doi:10.1016/j.heliyon.2023.e15147

Deep learning-powered 3D segmentation derives factors associated with lymphovascular invasion and prognosis in clinical T1 stage non-small cell lung cancer

Heliyon. 2023 Apr 1;9(4):e15147. doi: 10.1016/j.heliyon.2023.e15147. eCollection 2023 Apr.

Authors

Zhichao Zuo¹, Xiaohong Fan², Yao Tang³, Yang Zhang³, Xin Peng¹, Weihua Zeng¹, Ying Zeng¹

Affiliations

¹ Department of Radiology, Xiangtan Central Hospital, Xiangtan, Hunan, 411000, China.
² The School of Mathematics and Computational Science, Xiangtan University, Xiangtan, Hunan, 411105, China.
³ Department of Pathology, Xiangtan Central Hospital, Xiangtan, Hunan, 411000, China.

Abstract

Background: Lymphovascular invasion (LVI) is an invasive biologic behavior that affects the treatment and prognosis of patients with early-stage lung cancer. This study aimed to identify LVI diagnostic and prognostic biomarkers using deep learning-powered 3D segmentation with artificial intelligence (AI) technology.

Methods: Between January 2016 and October 2021, we enrolled patients with clinical T1 stage non-small cell lung cancer (NSCLC). We used commercially available AI software (Dr. Wise system, Deep-wise Corporation, China) to extract quantitative AI features of pulmonary nodules automatically. Dimensionality reduction was achieved through least absolute shrinkage and selection operator regression; subsequently, the AI score was calculated.Then, the univariate and multivariate analysis was further performed on the AI score and patient baseline parameters.

Results: Among 175 enrolled patients, 22 tested positive for LVI at pathology review. Based on the multivariate logistic regression results, we incorporated the AI score, carcinoembryonic antigen, spiculation, and pleural indentation into the nomogram for predicting LVI. The nomogram showed good discrimination (C-index = 0.915 [95% confidence interval: 0.89-0.94]); moreover, calibration of the nomogram revealed good predictive ability (Brier score = 0.072). Kaplan-Meier analysis revealed that relapse-free survival and overall survival were significantly higher among patients with a low-risk AI score and without LVI than those among patients with a high-risk AI score (p = 0.008 and p = 0.002, respectively) and with LVI (p = 0.013 and p = 0.008, respectively).

Conclusions: Our findings indicate that a high-risk AI score is a diagnostic biomarker for LVI in patients with clinical T1 stage NSCLC; accordingly, it can serve as a prognostic biomarker for these patients.

Keywords: 3D segmentation; Artificial intelligence; Clinical T1 stage non-small cell lung cancer; Deep learning; Lymphovascular invasion.