Exploring the optimal threshold of 3D consolidation tumor ratio value segmentation based on artificial intelligence for predicting the invasive degree of T1 lung adenocarcinoma.

ABSTRACT:

Background

The assessment of lung adenocarcinoma significantly depends on the proportion of solid components in lung nodules. Traditional one-dimensional consolidation tumor ratio (1D CTR) based on ideal, uniformly dense solid components lacks precision. There is no consensus on the CT threshold for evaluating invasiveness using the threshold segmentation method. This study aimed to explore the effectiveness of the three-dimensional CTR (3D CTR) calculated by the artificial intelligence threshold segmentation method in predicting invasive stage T1 lung adenocarcinoma and to identify its optimal threshold and cut-off point.

Methods

Data from 1,056 patients with 1,179 pulmonary nodules confirmed by postoperative pathology were collected retrospectively from two centers, Zhengz

SUBMITTER: Shi W 

PROVIDER: S-EPMC11651987 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature