Predicting the progressive-free survival of ALK-positive lung cancer: machine learning algorithm based on habitat imaging.

ABSTRACT:

Background

Lung cancer associated with the harbor oncogenic driver mutations or fusions, such as anaplastic lymphoma kinase (ALK), could benefit from representative clinical first-line treatment. And patients with ALK-positive (ALK+) show longer median survival than ALK-negative patients. This study aimed to develop a machine-learning model based on habitat analysis derived from computed tomography (CT) lung images to predict disease progression and explore its prognostic value for progression-free survival (PFS) in patients with (ALK+) lung cancer.

Methods

A total of 79 patients with ALK-positive lung cancer and advanced TNM stage (III-IV) at The First Affiliated Hospital of Nanjing Medical University between April 2016 and August 2022 were divided into training (n=63) and

SUBMITTER: Wang F 

PROVIDER: S-EPMC12433053 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature