A CT-based radiomics nomogram for predicting prognosis of coronavirus disease 2019 (COVID-19) radiomics nomogram predicting COVID-19.

OBJECTIVES: To identify the value of radiomics method derived from CT images to predict prognosis in patients with COVID-19. METHODS: A total of 40 patients with COVID-19 were enrolled in the study. Baseline clinical data, CT images, and laboratory testing results were collected from all patients. We defined that ROIs in the absorption group decreased in the density and scope in GGO, and ROIs in the progress group progressed to consolidation. A total of 180 ROIs from absorption group (n = 118) and consolidation group (n = 62) were randomly divided into a training set (n = 145) and a validation set (n = 35) (8:2). Radiomics features were extracted from CT images, and the radiomics-based models were built with three classifiers. A radiomics score (Rad-score) was calculated by a linear combination of selected features. The Rad-score and clinical factors were incorporated into the radiomics nomogram construction. The prediction performance of the clinical factors model and the radiomics nomogram for prognosis was estimated. RESULTS: A total of 15 radiomics features with respective coefficients were calculated. The AUC values of radiomics models (kNN, SVM, and LR) were 0.88, 0.88, and 0.84, respectively, showing a good performance. The C-index of the clinical factors model was 0.82 [95% CI (0.75-0.88)] in the training set and 0.77 [95% CI (0.59-0.90)] in the validation set. The radiomics nomogram showed optimal prediction performance. In the training set, the C-index was 0.91 [95% CI (0.85-0.95)], and in the validation set, the C-index was 0.85 [95% CI (0.69-0.95)]. For the training set, the C-index of the radiomics nomogram was significantly higher than the clinical factors model (p = 0.0021). Decision curve analysis showed that radiomics nomogram outperformed the clinical model in terms of clinical usefulness. CONCLUSIONS: The radiomics nomogram based on CT images showed favorable prediction performance in the prognosis of COVID-19. The radiomics nomogram could be used as a potential biomarker for more accurate categorization of patients into different stages for clinical decision-making process. ADVANCES IN KNOWLEDGE: Radiomics features based on chest CT images help clinicians to categorize the patients of COVID-19 into different stages. Radiomics nomogram based on CT images has favorable predictive performance in the prognosis of COVID-19. Radiomics act as a potential modality to supplement conventional medical examinations.

High resolution computed tomography for the diagnosis of 2019 novel coronavirus (2019-nCoV) pneumonia: a study from multiple medical centers in western China.

BACKGROUND: To evaluate the role of high-resolution computed tomography (HRCT) in the diagnosis of 2019 novel coronavirus (2019-nCoV) pneumonia and to provide experience in the early detection and diagnosis of 2019-nCoV pneumonia. METHODS: Seventy-two patients confirmed to be infected with 2019-nCoV from multiple medical centers in western China were retrospectively analyzed, including epidemiologic characteristics, clinical manifestations, laboratory findings and HRCT chest features. RESULTS: All patients had lung parenchymal abnormalities on HRCT scans, which were mostly multifocal in both lungs and asymmetric in all patients, and were mostly in the peripheral or subpleural lung regions in 52 patients (72.22%), in the central lung regions in 16 patients (22.22%), and in both lungs with "white lung" manifestations in 4 patients (5.56%). Subpleural multifocal consolidation was a predominant abnormality in 38 patients (52.78%). Ground-glass opacity was seen in 34 patients (47.22%). Interlobular septal thickening was found in 18 patients, 8 of whom had only generally mild thickening with no zonal predominance. Reticulation was seen in 8 patients (11.11%), and was mild and randomly distributed. In addition, both lungs of 28 patients had 2 or 3 CT imaging features. Out of these 72 patients, 36 were diagnosed as early stage, 32 patients as progressive stage, and 4 patient as severe stage pneumonia. Moreover, the diagnostic accuracy of HRCT features combined with epidemiological history was not significantly different from the detection of viral nucleic acid (all P >0.05). CONCLUSIONS: The HRCT features of 2019-nCoV pneumonia are characteristic to a certain degree, which when combined with epidemiological history yield high clinical value in the early detection and diagnosis of 2019-nCoV pneumonia.