An AI-based radiomics nomogram for disease prognosis in patients with COVID-19 pneumonia using initial CT images and clinical indicators. (October 2021)

• Record Type: Journal Article
• Authors: Zhang, Mudan; Zeng, Xianchun; Huang, Chencui; Liu, Jun; Liu, Xinfeng; Xie, Xingzhi; Wang, Rongpin
• Is Part Of: International journal of medical informatics. Volume 154(2021)
• Publication Date: 2021-10
• Subjects: COVID-19 pneumonia1 -- Radiomics2 -- Nomogram3 -- AI4 -- CT5; WHO World Health Organization -- CDC Centers for Disease Control and Prevention -- ARDS Acute Respiratory Distress Syndrome -- COVID-19 coronavirus disease 2019 -- CT computed tomography -- AI artificial intelligence -- ROC receiver operating characteristic -- LASSO least absolute shrinkage and selection operator -- LR logistic regression -- PCR multiplexed polymerase chain reaction -- RT-PCR reverse transcription-PCR -- ROI region of interest -- AUC area under the curve -- DC dice coefficient -- GLCM gray-level co-occurrence matrix -- GLRLM gray-level run length matrix -- GLSZM gray-level size zone matrix -- GLDM gray-level dependence matrix -- ACC accuracy -- SEN sensitivity -- SPE specificity -- DCA decision curve analysis -- CLD chronic lung disease or asthma -- CRP C-reactive protein -- WBC white blood cells count -- N neutrophil count -- L lymphocyte count -- Cr creatinine -- CK creatine kinase -- LDH lactate dehydrogenase -- VIF variance inflation factor -- EHR selectronic health records; Medical informatics -- Periodicals; Information science -- Periodicals; Computers -- Periodicals; Medical technology -- Periodicals; Medical Informatics -- Periodicals; Technology, Medical -- Periodicals; Computers; Information science; Medical informatics; Medical technology; Electronic journals; Periodicals; Electronic journals; 610.285
• DOI: 10.1016/j.ijmedinf.2021.104545
• Languages: English
• ISSNs: 1386-5056

Abstract

Highlights: A radiomics model based on non-contrast chest CT images can assess the prognosis of COVID-19 pneumonia. Clinical indicators including age and chronic lung disease or asthma (CLD) which correlated with the severity of COVID-19 pneumonia, were included in the radiomics nomogram. The radiomics nomogram that integrated clinical indicators and radiomic signatures yielded an AUC of 0.88 in the training set, 0.85 in internal validation set and 0.84 in independent external validation set using initial CT images and clinical indicators. Abstract: Background: This study utilized a comprehensive nomogram to evaluate the prognosis of patients with COVID-19 pneumonia. Methods: COVID-19 pneumonia data was divided into training set (256 of 321, 80%), internal validation set (65 of 321, 20%) and independent external validation set (n = 188). After image processing, lesion segmentation, feature extraction and feature selection, radiomics signatures and clinical indicators were used to develop a radiomics model and a clinical model respectively. Combining radiomics signatures and clinical indicators, a radiomics nomogram was built. The performance of proposed models was evaluated by the receiver operating characteristic curve (AUC). Calibration curves and decision curve analysis were used to assess the performance of the radiomics nomogram. Results: Two clinical indicators that were age and chronic lung disease or asthma and 21 radiomics features were selected to build the radiomics nomogram. The radiomics nomogram yielded an Area Under The Curve 1 (AUC) of 0.88 and accuracy of 0.80 in the training set, an AUC of 0.85 and accuracy of 0.77 in internal testing validation set and an AUC of 0.84 and accuracy of 0.75 in independent external validation set. The performance of radiomics nomogram was better than clinical model (AUC = 0.77, p < 0.001) and radiomics model (AUC = 0.72, p = 0.025) in independent external validation set. Conclusions: The radiomics nomogram may be used to assess the deterioration of COVID-19 pneumonia.