Development of a deep learning-based method to diagnose pulmonary ground-glass nodules by sequential computed tomography imaging.

ABSTRACT:

Background

Early identification of the malignant propensity of pulmonary ground-glass nodules (GGNs) can relieve the pressure from tracking lesions and personalized treatment adaptation. The purpose of this study was to develop a deep learning-based method using sequential computed tomography (CT) imaging for diagnosing pulmonary GGNs.

Methods

This diagnostic study retrospectively enrolled 762 patients with GGNs from West China Hospital of Sichuan University between July 2009 and March 2019. All patients underwent surgical resection and at least two consecutive time-point CT scans. We developed a deep learning-based method to identify GGNs using sequential CT imaging on a training set consisting of 1524 CT sections from 508 patients and then evaluated 256 patients in the tes

SUBMITTER: Qiu Z 

PROVIDER: S-EPMC8841714 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature