Artificial intelligence system-based histogram analysis of computed tomography features to predict tumor invasiveness of ground-glass nodules

Huairong Zhang; Dawei Wang; Wenling Li; Zhaorong Tian; Lirong Ma; Jiaxuan Guo; Yifan Wang; Xiao Sun; Xiaobin Ma; Li Ma; Li Zhu

doi:10.21037/qims-23-31

Artificial intelligence system-based histogram analysis of computed tomography features to predict tumor invasiveness of ground-glass nodules

Quant Imaging Med Surg. 2023 Sep 1;13(9):5783-5795. doi: 10.21037/qims-23-31. Epub 2023 Jul 31.

Authors

Huairong Zhang^#¹, Dawei Wang^#², Wenling Li¹, Zhaorong Tian¹, Lirong Ma¹, Jiaxuan Guo¹, Yifan Wang¹, Xiao Sun¹, Xiaobin Ma¹, Li Ma¹, Li Zhu¹

Affiliations

¹ Department of Radiology, General Hospital of Ningxia Medical University, Yinchuan, China.
² Institute of Advanced Research, Infervision Medical Technology Co., Ltd., Beijing, China.

^# Contributed equally.

Abstract

Background: The use of an artificial intelligence (AI)-based diagnostic system can significantly aid in analyzing the histogram of pulmonary nodules. The aim of our study was to evaluate the value of computed tomography (CT) histogram indicators analyzed by AI in predicting the tumor invasiveness of ground-glass nodules (GGNs) and to determine the added value of contrast-enhanced CT (CECT) compared with nonenhanced CT (NECT) in this prediction.

Methods: This study enrolled patients with persistent GGNs who underwent preoperative NECT and CECT scanning. AI-based histogram analysis was performed for pathologically confirmed GGNs, which was followed by screening invasiveness-related factors via univariable analysis. Multivariable logistic models were developed based on candidate CT histogram indicators measured on either NECT or CECT. Receiver operating characteristic (ROC) curve and precision-recall (PR) curve were used to evaluate the models' performance.

Results: A total of 116 patients comprising 121 GGNs were included and divided into the precancerous lesion and adenocarcinoma groups based on invasiveness. In the AI-based histogram analysis, the mean CT value [NECT: odds ratio (OR) =1.009; 95% confidence interval (CI): 1.004-1.013; P<0.001] and solid component volume (NECT: OR =1.005; 95% CI: 1.000-1.010; P=0.032) were associated with the adenocarcinoma and used for multivariable logistic modeling. The area under ROC curve (AUC) and PR curve (AUPR) were not significantly different between the NECT model (AUC =0.765, 95% CI: 0.679-0.837; AUPR =0.907, 95% CI: 0.825-0.953) and the optimal CECT model (delayed phase: AUC =0.772, 95% CI: 0.687-0.843; AUPR =0.895, 95% CI: 0.812-0.944). No significantly different metrics were observed between the NECT and CECT models (precision: 0.707 vs. 0.742; P=0.616).

Conclusions: The AI diagnostic system can help in the diagnosis of GGNs. The system displayed decent performance in GGN detection and alert to malignancy. Mean CT value and solid component volume were independent predictors of tumor invasiveness. CECT provided no additional improvement in diagnostic performance as compared with NECT.

Keywords: Ground-glass nodules (GGNs); adenocarcinoma; artificial intelligence system (AI system); computed tomography (CT); invasiveness.