Evaluation of the invasiveness of pure ground-glass nodules based on dual-head ResNet technique.

ABSTRACT:

Objective

To intelligently evaluate the invasiveness of pure ground-glass nodules with multiple classifications using deep learning.

Methods

pGGNs in 1136 patients were pathologically confirmed as lung precursor lesions [atypical adenomatous hyperplasia (AAH) and adenocarcinoma in situ (AIS)], minimally invasive adenocarcinoma (MIA), or invasive adenocarcinoma (IAC). Four different models [EfficientNet-b0 2D, dual-head ResNet_3D, a 3D model combining three features (3D_3F), and a 3D model combining 19 features (3D_19F)] were constructed to evaluate the invasiveness of pGGNs using the EfficientNet and ResNet networks. The Obuchowski index was used to evaluate the differences in diagnostic efficiency among the four models.

Results

The patients with pGGNs (360 men, 776 w

SUBMITTER: Yang D 

PROVIDER: S-EPMC11367849 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature