Classification of Pulmonary Nodules in 2-[¹⁸F]FDG PET/CT Images with a 3D Convolutional Neural Network.

ABSTRACT:

Purpose

2-[¹⁸F]FDG PET/CT plays an important role in the management of pulmonary nodules. Convolutional neural networks (CNNs) automatically learn features from images and have the potential to improve the discrimination between malignant and benign pulmonary nodules. The purpose of this study was to develop and validate a CNN model for classification of pulmonary nodules from 2-[¹⁸F]FDG PET images.

Methods

One hundred thirteen participants were retrospectively selected. One nodule per participant. The 2-[¹⁸F]FDG PET images were preprocessed and annotated with the reference standard. The deep learning experiment entailed random data splitting in five sets. A test set was held out for evaluation of the final model. Four-fold cross-validation w

SUBMITTER: Alves VM 

PROVIDER: S-EPMC10796312 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature