Peritumoral radiomics features on preoperative thin-slice CT images can predict the spread through air spaces of lung adenocarcinoma

ABSTRACT: The spread through air spaces (STAS) is recognized as a negative prognostic factor in patients with early-stage lung adenocarcinoma. The present study aimed to develop a machine learning model for the prediction of STAS using peritumoral radiomics features extracted from preoperative CT imaging. A total of 339 patients who underwent lobectomy or limited resection for lung adenocarcinoma were included. The patients were randomly divided (3:2) into training and test cohorts. Two prediction models were created using the training cohort: a conventional model based on the tumor consolidation/tumor (C/T) ratio and a machine learning model based on peritumoral radiomics features. The areas under the curve for the two models in the testing cohort were 0.70 and 0.76, respectively (P = 0.045). The c

SUBMITTER: Takehana K 

PROVIDER: S-EPMC9209514 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature