Study population
This retrospective study was waived by the ethics committees of the Hainan General hospital. In total, 74 patients confirmed with COVID-19 infection from January 20 to February 8, 2020, and 73 patients with other viral pneumonia of the corresponding period were collected. In the COVID-19 dataset, 66 patients were finally included who met the following inclusion criteria: (i) RT-PCR confirmed COVID-19; (ii) non-contrast CT at diagnosis time; (iii) positive CT findings. Other viral pneumonias were included who met the following inclusion criteria: (i) RT-PCR excluded COVID-19; (ii) non-contrast CT at diagnosis time; (iii) viral pneumonia highly suspected with COVID-19 at CT. The exclusion criteria were as follows: 1) contrast CT exam; 2) exams without slice thickness of 1mm; 3) negative CT findings. Finally, 326 chest CT exams from 137 patients were included in this study (Fig. 1). The average age is 47.0±15.4 years. Finally, we included 244 (75%) exams for COVID-19 and 82(25%) for other viral pneumonia in the study.
All the COVID-19 were confirmed as positive by RT PCR and were acquired from January 21, 2020, to Feb 8, 2020. The most common symptoms were fever (82%) and cough (77%). Each patient had one or multiple CT scans during the progression of the disease. The follow-up study was performed until February 19,2020.
Other viral-pneumonia patients were selected from the hospital of the corresponding period between January 23 to March 16, 2020. The admission distribution of the patients with other viral-pneumonia was: outpatient (86%, 61 of 71), inpatient (14%, 10 of 71). No one received laboratory confirmation of the etiology because of limited medical resources.
All the CT scans were split with a ratio of 85:15 into a training cohort and a testing cohort at the patient level according to the visiting time of the hospital. The features selection and model building were performed on the training cohort, and the testing cohort was not used for the training procedure. The patient demographic statistics are summarized in Table 1.
Imaging Protocol
CT examinations were performed on the NeuViz 128 CT (Neusoft, China) with automatic tube current (300 mA-496 mA). The pitch was set at 1.5 and breath-hold at full inspiration. The slice-thickness of each CT scan was 1mm. The reconstruction matrix was 512x512 pixels. The flow chart of data collection, ROI and features annotation, radiomics and quantity feature extraction, model building and evaluation are shown in Fig. 2.
Lesion segmentation and radiological evaluation
The anonymized thin-slice DICOM format non-enhanced CT images were imported into the Dr. Wise research platform, on which the pneumonia lesions were automatically delineated by deep-learning segmentation algorithms. Fifteen radiologists with more than 5 years of experience in chest imaging, blind to the knowledge of the pathological report and other clinical information, confirmed the segmentation result (Region of Interest, ROI) and evaluated the radiological characteristics. The segmentations and radiological characteristics were confirmed by two radiologists (F. C and Y.C) with 16 and more than 30 years of experience.
The 7 radiological characteristics included ground grass opacity, crazy paving pattern, halo sign, reversed halo sign, vascular perforating in the lesion, subpleural line and lesion locations (Fig. 3). For each series, the frequency of the radiological characteristics occurring was used for modeling.
Quantitative CT characteristics and radiomics features
The segmentation result was used to extract quantitative CT characteristics and radiomics features. The delineation of the pulmonary lobes was segmented by the Dr. Wise research platform and the lesion and lobe information were extracted to construct quantitative characteristics of pneumonia.
There were a total of 33 quantitative characteristics, including the volume of the lesions, mean and standard deviation of the CT values in lesions, consolidation lesions and ground glass lesions respectively, the number of the consolidation lesions and the number of the ground glass lesions, the ratio of the volume of these lesions in the entire pulmonary and the five pulmonary lobes respectively, and the number of lesions that suffered bulla, emphysema, pleural thickening, reticular, and stripe.
Before the radiomics features were extracted, the pixel spacing of images was resampled to 1.0 mm per pixel by the BSpline algorithm. Besides the original images, the wavelet filters or Laplacian of Gaussian filters were performed to generate several filtered images. A total of 1218 radiomics features were extracted from the manual confirmed ROIs of the original images and the filtered images by PyRadiomics V2.1.0, including (1) 252 First-order features; (2) 14 Shape-based features; (3) 308 Gray Level Co-occurrence Matrix Features (GLCM); (4) 224 Gray Level Size Zone Matrix Features (GLSZM); (5) 224 Gray Level Run Length Matrix Features (GLRLM); (6) 196 Gray Level Dependence Matrix Features (GLD-ZM). The pre-processing methods and radiomic feature descriptions are detailed in Supplementary Information 1.1. and 1.2.
Development of predictive models
The features were stacked in the order of radiomics features, radiological features,quantity features and clinical features, and 4 SVM models with radial basis function kernel were built. All numerical features were normalized to [0,1], and the categorical features were encoded by one-hot encoder. To avoid overfitting, the feature selection methods were used to reduce the dimension of the features. The optimal parameters of the combination of the feature selection method and the model were found by grid search using a ten-run 5-fold cross validation procedure on the training cohort. After the optimal params were determined, the entire training cohort was used to build the model and the performance on the testing cohort was evaluated. After the cross-validation procedure, the threshold that maximum the Youden Index was used to cut-off the discriminative score to differentiate the COVID-19 from other virus pneumonia. The evaluation indicators include the area under the receiver operator characteristic curve (AUC), accuracy (ACC), sensitivity and specificity.
The feature selection method we used included the f-test based method and the L1 based method. The f-test based feature selection method with different preserve ratio (1%, 5%, 10%, 30%, 50% and 100%) were tested, while the L1 based method that used linear C-support vector classification model with different C value (0.01, 0.1, 1, and 10) as a base model was tested.