Exploration of optimal thresholds for predicting the invasive nature of stage T1 lung adenocarcinoma using artificial...

The accuracy of intraoperative rapid frozen pathology is suboptimal, and the assessment of invasiveness in malignant pulmonary nodules significantly influences surgical resection strategies. Predicting the invasiveness of lung adenocarcinoma based on preoperative imaging is a clinical challenge, and there are no established standards for the optimal threshold value using the threshold segmentation method to predict the invasiveness of stage T1 lung adenocarcinoma. This study aimed to explore the efficacy of three-dimensional solid component volume (3D SCV) [calculated by artificial intelligence (AI) threshold segmentation method] in predicting the aggressiveness of T1 lung adenocarcinoma and to determine its optimal threshold and cut-off point.

A retrospective case-control analysis was conducted on 1,179 confirmed T1 lung adenocarcinoma nodules from two centers. AI-based threshold segmentation was used to calculate seven sets of solid component volume data (V_-550, V_-450, V_-350, V_-250, V_-150, V_-50, V₀) at seven computed tomography (CT) threshold values (-550, -450, -350, -250, -150, -50, 0 HU). The receiver operating characteristic (ROC) curve was employed to explore the optimal threshold value for predicting the invasiveness of T1 lung adenocarcinoma based on solid component volume. Subgroup analysis was performed according to the diameter of the pulmonary nodule, divided into T1a (≤10 mm), T1b (10 mm < T1b ≤20 mm), and T1c (20 mm < T1b ≤30 mm), to investigate the optimal threshold for each subgroup.

The solid component volume was a stable predictive factor for the invasiveness of T1 lung adenocarcinoma. The optimal threshold value for predicting the invasiveness of T1 lung adenocarcinoma using AI-based 3D SCV segmentation was -350 HU, with an area under the curve (AUC) and 95% confidence interval (CI) of 0.930 (0.915-0.945), a cut-off value of 106.5 mm³, a sensitivity of 88.462%, and a specificity of 83.853%. For the T1a subgroup, the optimal threshold was -450 HU, with an AUC of 0.926, a cut-off value of 204.5 mm³, a sensitivity of 85.269%, and a specificity of 87.013%. For the T1b subgroup, the optimal threshold was -250 HU, with an AUC of 0.922, a cut-off value of 106 mm³, a sensitivity of 89.726%, and a specificity of 82.266%. For the T1c subgroup, the optimal threshold was -350 HU, with an AUC of 0.961, a cut-off value of 472 mm³, a sensitivity of 95.652%, and a specificity of 83.529%.

The -350 HU threshold is reasonable for predicting the invasiveness of T1 lung adenocarcinoma based on solid component volume using the threshold segmentation method. However, there are certain differences in the threshold values depending on the diameter of the pulmonary nodule.