An End-to-End Integrated Clinical and CT-Based Radiomics Nomogram for Predicting Disease Severity and Need for Ventilator Support in COVID-19 Patients: A Large Multisite Retrospective Study

Frontiers in Radiology

Amogh Hiremath¹  Cheng Lu¹  Pranjal Vaidya¹  Mehdi Alilou¹  Mengyao Ji²  Keith Armitage³  Jennifer Furin³  Lei Yuan⁴  Kaustav Bera⁵  Pingfu Fu⁶  Amit Gupta⁷  Robert Gilkeson⁷  Anant Madabhushi⁸ 

[1] Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States;Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China;Department of Infectious Diseases, University Hospitals Cleveland Medical Center, Cleveland, OH, United States;Department of Information Center, Renmin Hospital of Wuhan University, Wuhan, China;Department of Internal Medicine, Maimonides Medical Center, Brooklyn, NY, United States;Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, United States;Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, United States;Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, OH, United States;

关键词: COVID-19;    radiomics;    nomogram;    prognosis;    severity;    peritumoral radiomics;   

DOI  :  10.3389/fradi.2022.781536

来源: DOAJ

【 摘 要 】

ObjectiveThe disease COVID-19 has caused a widespread global pandemic with ~3. 93 million deaths worldwide. In this work, we present three models—radiomics (MRM), clinical (MCM), and combined clinical–radiomics (MRCM) nomogram to predict COVID-19-positive patients who will end up needing invasive mechanical ventilation from the baseline CT scans.MethodsWe performed a retrospective multicohort study of individuals with COVID-19-positive findings for a total of 897 patients from two different institutions (Renmin Hospital of Wuhan University, D1 = 787, and University Hospitals, US D2 = 110). The patients from institution-1 were divided into 60% training, D1T (N = 473), and 40% test set D1V (N = 314). The patients from institution-2 were used for an independent validation test set D2V (N = 110). A U-Net-based neural network (CNN) was trained to automatically segment out the COVID consolidation regions on the CT scans. The segmented regions from the CT scans were used for extracting first- and higher-order radiomic textural features. The top radiomic and clinical features were selected using the least absolute shrinkage and selection operator (LASSO) with an optimal binomial regression model within D1T.ResultsThe three out of the top five features identified using D1T were higher-order textural features (GLCM, GLRLM, GLSZM), whereas the last two features included the total absolute infection size on the CT scan and the total intensity of the COVID consolidations. The radiomics model (MRM) was constructed using the radiomic score built using the coefficients obtained from the LASSO logistic model used within the linear regression (LR) classifier. The MRM yielded an area under the receiver operating characteristic curve (AUC) of 0.754 (0.709–0.799) on D1T, 0.836 on D1V, and 0.748 D2V. The top prognostic clinical factors identified in the analysis were dehydrogenase (LDH), age, and albumin (ALB). The clinical model had an AUC of 0.784 (0.743–0.825) on D1T, 0.813 on D1V, and 0.688 on D2V. Finally, the combined model, MRCM integrating radiomic score, age, LDH and ALB, yielded an AUC of 0.814 (0.774–0.853) on D1T, 0.847 on D1V, and 0.771 on D2V. The MRCM had an overall improvement in the performance of ~5.85% (D1T: p = 0.0031; D1Vp = 0.0165; D2V: p = 0.0369) over MCM.ConclusionThe novel integrated imaging and clinical model (MRCM) outperformed both models (MRM) and (MCM). Our results across multiple sites suggest that the integrated nomogram could help identify COVID-19 patients with more severe disease phenotype and potentially require mechanical ventilation.

【 授权许可】

Unknown