A machine learning approach for preoperatively assessing pulmonary function with computed tomography in patients with lung cancer.

ABSTRACT:

Background

It is clinically important to accurately assess the pulmonary function of patients with lung cancer, especially before surgery. This knowledge can help clinicians to monitor patients pre- and post-surgery, predict the impact of surgery on pulmonary function, and help to optimize postsurgical recovery. We used a deep learning approach for assessing pulmonary function on computed tomography (CT) scans in patients with lung cancer before they underwent surgery.

Methods

A total of 188 patients with lung cancer whose diagnoses had been pathologically confirmed were enrolled in this study. We used a software to automatically delineate regions of interest (ROIs) throughout the airways, lobes, and the whole lungs. We then used AK software to extract radiomics features of

SUBMITTER: Meng H 

PROVIDER: S-EPMC10006133 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature