Preoperative CT-based artificial intelligence-derived quantitative parameters and imaging features for predicting the invasiveness of histologically confirmed subcentimeter adenocarcinomatous nodules: a two-center study.

ABSTRACT:

Background

Assessing the invasiveness of subcentimeter pulmonary adenocarcinomas remains challenging. Computed tomography (CT) provides objective data, but radiologic interpretation is subjective. Artificial intelligence (AI)-derived quantitative parameters may offer a more reproducible assessment for small nodules. This study aimed to develop and validate models based on CT-derived AI quantitative parameters nodules and imaging features to predict the invasiveness of subcentimeter adenocarcinomatous nodules.

Methods

Patients diagnosed with subcentimeter adenocarcinomatous nodules from two centers between January 2021 and December 2022 were retrospectively included, and their associated quantitative AI parameters and CT radiologic features were recorded and analyzed. Indepen

SUBMITTER: Chen Q 

PROVIDER: S-EPMC12682512 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature