Exploration of optimal thresholds for predicting the invasive nature of stage T1 lung adenocarcinoma using artificial intelligence-based 3D solid component volume segmentation.

ABSTRACT:

Background

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.

Methods

A retrospec

SUBMITTER: Shi W 

PROVIDER: S-EPMC11744170 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature