Deep learning-assisted development and validation of an algorithm for predicting the growth of persistent pure ground-glass nodules.

ABSTRACT:

Background

The prediction of the persistent pure ground-glass nodule (pGGN) growth is challenging and limited by subjective assessment and variation across radiologists. A chest computed tomography (CT) image-based deep learning classification model (DLCM) may provide a more accurate growth prediction.

Methods

This retrospective study enrolled consecutive patients with pGGNs from January 2010 to December 2020 from two independent medical institutions. Four DLCM algorithms were built to predict the growth of pGGNs, which were extracted from the nodule areas of chest CT images annotated by two radiologists. All nodules were assigned to either the study, the inner validation, or the external validation cohort. Accuracy, sensitivity, specificity, receiver operating characteristi

SUBMITTER: Tang Y 

PROVIDER: S-EPMC10775010 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature