Neural network-based model for evaluating inert nodules and volume doubling time in T1 lung adenocarcinoma: a nested case-control study

Bing Wang; Hui Zhang; Wei Li; Siyun Fu; Ye Li; Xiang Gao; Dongpo Wang; Xinjie Yang; Shaofa Xu; Jinghui Wang; Dailun Hou

doi:10.3389/fonc.2023.1037052

Neural network-based model for evaluating inert nodules and volume doubling time in T1 lung adenocarcinoma: a nested case-control study

Front Oncol. 2023 May 24:13:1037052. doi: 10.3389/fonc.2023.1037052. eCollection 2023.

Authors

Bing Wang¹, Hui Zhang², Wei Li³, Siyun Fu¹, Ye Li¹, Xiang Gao³, Dongpo Wang¹, Xinjie Yang², Shaofa Xu³, Jinghui Wang², Dailun Hou¹

Affiliations

¹ Department of Radiology, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing Chest Hospital, Capital Medical University, Beijing, China.
² Department of Medical Oncology, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing Chest Hospital, Capital Medical University, Beijing, China.
³ Cancer Research Center, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China.

Abstract

Objective: The purpose of this study is to establish model for assessing inert nodules predicting nodule volume-doubling.

Methods: A total of 201 patients with T1 lung adenocarcinoma were analysed retrospectively pulmonary nodule information was predicted by an AI pulmonary nodule auxiliary diagnosis system. The nodules were classified into two groups: inert nodules (volume-doubling time (VDT)>600 days n=152) noninert nodules (VDT<600 days n=49). Then taking the clinical imaging features obtained at the first examination as predictive variables the inert nodule judgement model <sn</sn>>(INM) volume-doubling time estimation model (VDTM) were constructed based on a deep learning-based neural network. The performance of the INM was evaluated by the area under the curve (AUC) obtained from receiver operating characteristic (ROC) analysis the performance of the VDTM was evaluated by R²(determination coefficient).

Results: The accuracy of the INM in the training and testing cohorts was 81.13% and 77.50%, respectively. The AUC of the INM in the training and testing cohorts was 0.7707 (95% CI 0.6779-0.8636) and 0.7700 (95% CI 0.5988-0.9412), respectively. The INM was effective in identifying inert pulmonary nodules; additionally, the R2 of the VDTM in the training cohort was 0.8008, and that in the testing cohort was 0.6268. The VDTM showed moderate performance in estimating the VDT, which can provide some reference during a patients' first examination and consultation.

Conclusion: The INM and the VDTM based on deep learning can help radiologists and clinicians distinguish among inert nodules and predict the nodule volume-doubling time to accurately treat patients with pulmonary nodules.

Keywords: T1 lung adenocarcinoma; computer tomograph; inert nodules; neural network; volume doubling time.