Application of dual-stream 3D convolutional neural network based on ¹⁸F-FDG PET/CT in distinguishing benign and invasive adenocarcinoma in ground-glass lung nodules.

ABSTRACT:

Purpose

This work aims to train, validate, and test a dual-stream three-dimensional convolutional neural network (3D-CNN) based on fluorine 18 (¹⁸F)-fluorodeoxyglucose (FDG) PET/CT to distinguish benign lesions and invasive adenocarcinoma (IAC) in ground-glass nodules (GGNs).

Methods

We retrospectively analyzed patients with suspicious GGNs who underwent ¹⁸F-FDG PET/CT in our hospital from November 2011 to November 2020. The patients with benign lesions or IAC were selected for this study. According to the ratio of 7:3, the data were randomly divided into training data and testing data. Partial image feature extraction software was used to segment PET and CT images, and the training data after using the data augmentation were used for the training and v

SUBMITTER: Shao X 

PROVIDER: S-EPMC8561359 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature