Prediction of lung malignancy progression and survival with machine learning based on pre-treatment FDG-PET/CT.

ABSTRACT:

Background

Pre-treatment FDG-PET/CT scans were analyzed with machine learning to predict progression of lung malignancies and overall survival (OS).

Methods

A retrospective review across three institutions identified patients with a pre-procedure FDG-PET/CT and an associated malignancy diagnosis. Lesions were manually and automatically segmented, and convolutional neural networks (CNNs) were trained using FDG-PET/CT inputs to predict malignancy progression. Performance was evaluated using area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. Image features were extracted from CNNs and by radiomics feature extraction, and random survival forests (RSF) were constructed to predict OS. Concordance index (C-index) and integrated bri

SUBMITTER: Huang B 

PROVIDER: S-EPMC9278031 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature