COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: Analysis of a multi-institutional dataset of 14,339 patients.

ABSTRACT:

Background

We aimed to analyze the prognostic power of CT-based radiomics models using data of 14,339 COVID-19 patients.

Methods

Whole lung segmentations were performed automatically using a deep learning-based model to extract 107 intensity and texture radiomics features. We used four feature selection algorithms and seven classifiers. We evaluated the models using ten different splitting and cross-validation strategies, including non-harmonized and ComBat-harmonized datasets. The sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were reported.

Results

In the test dataset (4,301) consisting of CT and/or RT-PCR positive cases, AUC, sensitivity, and specificity of 0.83 ± 0.01 (CI95%: 0.81-0.85), 0.81, and 0.72, respectively, wer

SUBMITTER: Shiri I 

PROVIDER: S-EPMC8964015 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature