CT-based whole lung radiomics nomogram to identify middle-aged and elderly COVID-19 patients at high risk of progressing to critical disease.

Abstract

Background: COVID-19 remains widespread and poses a threat to people's physical and mental health, especially middle-aged and elderly individuals. Early identification of COVID-19 patients at high risk of progressing to critical disease helps improve overall patient outcomes and healthcare efficiency.

Purpose: To develop a radiomics nomogram to predict the risk of newly admitted middle-aged and elderly COVID-19 patients progressing to critical disease.

Methods: A total of 794 patients (aged 40 years or above) were retrospectively included in the study from two institutions, all of them were with non-critical COVID-19 on admission. At follow-up, patients were divided into non-critical group and critical group. About 443 patients (384 non-critical and 59 critical) from the first hospital were randomly assigned to the training (n = 311) and internal validation (n = 132) set in a 7:3 ratio. Additionally, an independent external cohort of 351 patients (292 non-critical and 59 critical) from another hospital was evaluated. Radiomics signatures and clinical indicators were used to build a radiomics model and a clinical model after computed tomography (CT) image processing, CT whole-lung segmentation, feature extraction, and feature selection. The radiomics nomogram model integrated radiomics model and clinical model. The receiver operating characteristic curve (AUC) was used to assess the performance of the proposed models. Calibration curves and decision curve analysis were used to assess the performance of the radiomics nomogram.

Results: For the training, internal validation, and external validation sets, the AUC values of the radiomic nomogram for the prediction of COVID-19 progression were 0.916, 0.917, and 0.890, respectively. Calibration curves indicated that there was no significant departure between prediction and observation in three sets. The decision curve image demonstrated the clinical utility of the nomogram model.

Conclusions: Our nomogram model incorporates radiomics features and clinical indicators, it provides a new pathway to increase predictive accuracy or clinical utility, further helping to provide personalized management for middle-aged and elderly patients with COVID-19.