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September 10, 2025Open Access

Prediction of pulmonary complications post-lobectomy or -sub-lobectomy in lung cancer using artificial intelligence-estimated lung function indexes based on preoperative chest computed tomography

Key Points

Favorable predictive value of AI-estimated lung function indexes for pulmonary complications after lobectomy.
Patients with pulmonary complications had significantly lower FEV1 values, emphasizing lung function's predictive role.
Univariate analysis revealed age, resected volume, and pulmonary function as critical factors predicting complications.
Favorable comparison of predictive models indicates clinical-AI-CT model outperformed others in accuracy.

Abstract

Several studies have demonstrated that artificial intelligence-based computed tomography (AI-CT) lung volume index can predict complications after lobectomy. However, previous studies have focused more on AI-CT results at the whole lung, left and right lung, and lobar levels. This study explored the value of the predicted postoperative pulmonary function values derived from preoperative chest AI-CT in predicting pulmonary complications (PCs) after lobectomy or sub-lobectomy in lung cancer. A total of 79 patients with lung cancer who underwent thoracoscopic lobectomy or sub-lobectomy were enrolled. Chest computed tomography (CT) and pulmonary function tests (PFTs) were performed one week before surgery. The patients were divided into PC+ (n=16) and PC- (n=63) groups according to whether PC occurred within 30 days after the operation. The total functional lung volume (TFLV) and resected functional lung volume (RFLV) were calculated using the AI-CT method. The predicted postoperative pulmonary function indexes, including total lung capacity (TLC), forced expiratory volume in the first second (FEV1), forced vital capacity (FVC), and diffusion capacity for carbon monoxide (DLCO), were calculated using the AI-CT and anatomical segmentation (AS) methods. PC-related factors were identified by univariate analysis to construct clinical, PFT, AS, AI-CT, clinical-PFT, clinical-AS, and clinical-AI-CT models. Sub-group analysis was performed based on the surgery procedure. Age, number of removed lung segments, and RFLV/TFLV in the PC+ group were higher than those in the PC- group (P AI-CT (0.807) > clinical-PFT (0.806) > clinical-AS (0.801) > AS (0.767) > clinical (0.757) > PFT (0.756) model. In sub-group analysis, only FEV1AI-CT showed statistical differences (P<0.05) between PC+ and PC- in both lobectomy and sub-lobectomy subgroups. FEV1AI-CT is a potential independent predictor of PC after lung lobectomy or sub-lobectomy in lung cancer.

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Cite This Study

Zheng et al. (Thu,) studied this question.

synapsesocial.com/papers/68c1a5eb54b1d3bfb60df704 https://doi.org/https://doi.org/10.21037/qims-24-1487

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