key: cord-0855554-zvmyb36s
authors: Cai, Wenli; Liu, Tianyu; Xue, Xing; Luo, Guibo; Wang, Xiaoli; Shen, Yihong; Fang, Qiang; Sheng, Jifang; Chen, Feng; Liang, Tingbo
title: CT Quantification and Machine-learning Models for Assessment of Disease Severity and Prognosis of COVID-19 Patients
date: 2020-09-21
journal: Acad Radiol
DOI: 10.1016/j.acra.2020.09.004
sha: a177a66c3b24f07266aac9b3c85c3038f2fd1303
doc_id: 855554
cord_uid: zvmyb36s

OBJECTIVE: This study was to investigate the CT quantification of COVID-19 pneumonia and its impacts on the assessment of disease severity and the prediction of clinical outcomes in the management of COVID-19 patients. MATERIALS AND METHODS: Ninety-nine COVID-19 patients who were confirmed by positive nucleic acid test (NAT) of RT-PCR and hospitalized from January 19, 2020 to February 19, 2020 were collected for this retrospective study. All patients underwent arterial blood gas test, routine blood test, chest CT examination, and physical examination on admission. In addition, fellow-up clinical data including the disease severity, clinical treatment, and clinical outcomes were collected for each patient. Lung volume, lesion volume, non-lesion lung volume (NLLV) (lung volume – lesion volume), and fraction of non-lesion lung volume (%NLLV) (non-lesion lung volume / lung volume) were quantified in CT images by using two U-Net models trained for segmentation of lung and COVID-19 lesions in CT images. Furthermore, we calculated 20 histogram textures for lesions volume and non-lesion lung volumes, respectively. To investigate the validity of CT quantification in the management of COVID-19, we built Random Forest (RF) models for the purpose of classification and regression to assess the disease severity (Moderate, Severe, and Critical) and to predict the need and length of ICU stay, the duration of oxygen inhalation, hospitalization, sputum NAT-positive, and patient prognosis. The performance of RF classifiers was evaluated using the area under the receiver operating characteristic (ROC) curves (AUC) and that of RF regressors using the root-mean-square error (RMSE). RESULTS: Patients were classified into three groups of disease severity: moderate (n=25), severe (n=47) and critical (n=27), according to the clinical staging. Of which, a total of 32 patients, 1 (1/25) moderate, 6 (6/47) severe and 25 critical (25/27), respectively, were admitted to ICU. The median values of ICU stay were 0, 0, 12 days, the duration of oxygen inhalation 10, 15, and 28 days, the hospitalization 12, 16, and 28 days, and the sputum NAT-positive 8, 9, 13 days, in three severity groups, respectively. The clinical outcomes were complete recovery (n=3), partial recovery with residual pulmonary damage (n=80), prolonged recovery (n=15), and death (n=1). The %NLLV in three severity groups were 92.18±9.89%, 82.94±16.49%, and 66.19±24.15% with p-value <0.05 among each two groups. The AUCs of RF classifiers were 0.927 and 0.929 in classification of moderate vs (severe + critical), and severe vs critical, respectively, which were significantly higher than both radiomics models and clinical models (p<0.05). The RMSEs of RF regressors were 0.88 weeks for prediction of duration of hospitalization (mean: 2.60 ± 1.01 weeks), 0.92 weeks for duration of oxygen inhalation (mean: 2.44 ± 1.08 weeks), 0.90 weeks for duration of sputum NAT-positive (mean: 1.59 ± 0.98 weeks), and 0.69 weeks for stay of ICU (mean: 1.32 ± 0.67 weeks), respectively. The AUCs for prediction of ICU treatment and prognosis (partial recovery vs prolonged recovery) were 0.945 and 0.960, respectively. CONCLUSION: CT quantification and machine-learning models shows great potentials for assisting decision-making in the management of COVID-19 patients by assessing disease severity and predicting clinical outcomes.

The outbreak of viral pneumonia caused by the 2019 novel coronavirus originally identified in China 1,2 , named COVID-19 3 and officially labelled as a pandemic by world health organization (WHO) 4 , is spreading rapidly over 200 countries and more than 5 million confirmed cases worldwide as of May 2020 5 . At present, nucleic acid test (NAT) of RT-PCR (reverse transcription polymerase chain reaction) remains the gold standard for diagnosis of COVID-19 infection 6, 7 . CT findings of viral pneumonia is listed as one of the three clinical evidences (the other two are respiratory symptoms and blood test) to identify suspected cases in the guideline of Chinese Health Commission (CHC) 8 . Typical CT signs of COVID-19 infection include groundglass opacities (GGO), GGO with lung consolidation, bronchial dilation, bilateral involvement and peripheral distribution [9] [10] [11] . These abnormal CT findings play an important role in assisting the diagnosis of COVID-19 patients. However, some studies observed that asymptomatic or mild symptomatic patients might have atypical or normal CT findings 12, 13 . Thereby, the primary role of CT imaging is limited to identify the suspected patients if a patient has clinical symptoms suggestive of COVID-19 infection. As opposed to China, chest radiography (CXR), has been deemed as the modality of choice for assessing and monitoring COVID-19 by ACR (American College of Radiology) and STR (Society of Thoracic Radiology) in the US 14 .

Chest CT plays a vital role in the diagnosis and management of various lung diseases 15 . The advent of high-resolution CT images has led to its increasing use in the management of chronic and acute pulmonary diseases such as chronic obstructive pulmonary disease (COPD) 16 , hypersensitivity pneumonitis 15 , and interstitial lung diseases 17 . Chest CT scans can assist diagnosis and guide treatment decision for community-acquired pneumonia 18, 19 . Quantitative imaging provided reliable and objective biomarkers in the management of severe acute respiratory syndrome (SARS) and middle-east respiratory syndrome (MERS) 20, 21 . Recent studies reported that AI-powered CT diagnosis may outperform lab testing for screening of COVID-19 with sensitivity of 97% 22 , and CT quantification as indictors may assess disease severity and predict prognosis in the management of COVID-19 23, 24 .

According to the clinical staging and disease progression, COVID-19 patients are classified into four groups, mild, moderate, severe, and critical groups, in terms of CHC guideline 8 . Mild patients tend to have mild clinical symptoms and scarcely identified pneumonia lesions on CT.

Studies observed that disease progression is generally associated with the increasing of numbers and sizes of GGO lesions in CT 25 . Thus, CT quantification as an advanced imaging technique is more effective for advanced progressive cases or those with complications. However, due to the short outbreak of COVID-19, little is known regarding CT imaging characteristics specific to the disease 26 , and the validity of CT quantification in the management of COVID-19. The clinical roles of CT quantification have not been explored for COVID-19 yet, in particular, in assisting decision-making in the management of COVID-19. It is expected that quantitative imaging analysis combined with AI and deep-learning technology will play an important role in the management of COVID-19 patients such as assessment of disease severity and the prediction of prognosis 27 .

The objectives of this study were (1) to investigate quantitative imaging analysis techniques by using deep-learning for quantification of COVID-19 pneumonia in CT images, (2) to correlate quantitative image biomarkers to the clinical manifestations of disease severity and clinical outcomes, and (3) to build up machine-learning (ML) models with quantitative imaging biomarkers for stratification of disease severity and prediction of clinical outcomes in the management of COVID-19 patients.

The Ethics Committees of both institutions have approved this retrospective study, in which informed consent was waived, but patient confidentiality was protected. The working procedure of the study is illustrated in Figure 1 . A total of 99 patients were selected from initial identification of 105 hospitalized patients after exclusion of patients who were classified into mild group (n=2), those had no CT scanning or underwent CT examination within one day of admission (n=3), and those with incomplete clinical data (n=1).

The patient's clinical data including demographic data, clinical symptoms, arterial blood gas test, routine blood test, treatment and outcomes were retrieved from the hospital's medical record system. A total of 40 clinical parameters that are associated with the COVID-19 patient management were collected, including  Demographic data (9 parameters): gender, age, days from illness to clinical visit, Wuhan contact history, and other medical history (hypertension, diabetes, history of surgery, coronary heart disease, hepatitis B);

 Clinical symptoms (9 parameters): fever, chills, cough, sputum, dizziness, headache, fatigue, body ache, chest tightness, diarrhea;

 Routine blood test (7 parameters): white blood cells, lymphocytes, eosinophils, neutrophils, lymphocyte counts, eosinophil count, C-reactive protein;

 Arterial blood gas test (15 parameters): blood oxygen saturation, partial pressure of arterial blood oxygen (PO2), partial pressure of arterial carbon dioxide (PCO2), PH value, activated partial prothrombin time, prothrombin time, D-dimer, lactate dehydrogenase, phosphomuscular acid kinase, creatine kinase isoenzyme, Alanine Aminotransferease (ALT), aspartate aminotransferase (AST), blood creatinine, blood urea nitrogen, procalcitonin.

For clinical treatment and outcomes, these patients were followed and outcome data were collected including the ICU support and length of ICU stay, the duration of oxygen inhalation, the duration of sputum NAT-positive, the duration of hospital stay, and the final outcome in terms of complete recovery, partial recovery with residual pulmonary damage, prolonged recovery, and death. The duration of sputum NAT-positive refers to the period from the hospitalization to the clear of infection for patients who were tested negative for COVID-19 for two consecutive days in respiratory samples, which is an importance clinical outcome indicating the clearing of the virus infection in the patients. In our study, viral RNA was extracted using the MagNA Pure 96 (Roche, Basel, Switzerland), and RT-PCR was performed using a commercial kit specific for COVID-19 virus detection (BioGerm, Shanghai, China).

All subjects underwent chest CT examinations on a multi-detector CT (MDCT) scanner (GE Revolution EVO 64-slice CT scanner, GE Healthcare, Milwaukee, Wisconsin) within one day on admission with the following CT scanning parameters: supine position, 120kVp tube voltage, automatic tube current modulation, 0.725mm collimation, 1mm and 5mm reconstruction intervals. The scanning range was from thoracic inlet to upper abdominal. This pipeline is illustrated in the right part of Figure 1 .

The segmentation of lung and lesions was carried out by using the U-Net model 28 , which represents one of the most well-known convolutional neural network (CNN) architectures for medical image segmentation. U-Net consists of convolutions path for feature extraction followed by up-convolutions path for resolution restoration and an identity skip connection. A schematic workflow of the U-Net architecture is shown in Figure 2 .

In this study, we trained two end-to-end 2D cascading U-Net segmentation models of lung and lesions. As a preprocessing step of the segmentation, the 12 bits CT images were first mapped to 8 bit images by using a lung CT window-level setting (WW:1000HU, WL:-500HU). All convolutions and up-convolutions were performed with two 3×3 convolutions layers with padding to keep the size unchanged followed by batch normalization, activation with the rectified linear unit and 2×2 max pooling. The bottommost layer mediates between the convolutions path and the up-convolution path. After that, the resultant mapping passes through another 3×3 CNN layer with the number of feature maps equal to the number of segments desired. Data augmentation was performed by adding noise and random elastic deformation including random rotation, random shift, random shear, and random zoom. The weighted crossentropy was used as the loss function. We applied mini-batch Adam optimizer to train the model.

The learning rate was set to 1e-4 and the batch size was set to 8. The training process stops when the training loss did not improve in 10 epochs. The dice similarity coefficient (DSC) was used as our evaluation metrics.

The U-Net models were trained on 250 chest CT scans randomly selected from the fellow-up CT examinations of the COVID-19 patients, of which lung regions and pneumonia regions were annotated by the consensus of a junior and a senior radiologists using the semi-automated contouring tools on 3DQI platform. We applied 10-fold cross-validation in the training procedure. The extracted lung regions were fed into the U-Net model for lesion segmentation which was trained by the same datasets with the annotation of COVID-19 pneumonia lesions.

After the automated segmentation of the U-Net models, the resulting images were reviewed by the consensus of a senior radiologist and an image analyst, who were blinded to the patient clinical data including disease severity and clinical outcomes. The CT quantification of disease was determined by calculating the lung volume, the lesion volume, the non-lesion lung volume (NLLV) (lung volumelesion volume), and fraction of NLLV (%NLLV) (non-lesion lung volume/ lung volume) by using the segmentation results.

After completion of segmentation and quantification, clinical data became visible to the image analyst, who calculated the imaging texture features of lung and lesion regions and correlated to the clinical data and prognosis for the further data analysis.

For the analysis of additional quantitative image features of the diseased and non-diseased lung, we calculated the histogram features for both lesion volume and NLLV. The histogram was constructed with bin size of 10 HU in the range between -1000HU to 200HU, resulting 120 bins.

The histogram was normalized in terms of the size of the lesion volume and NLLV (the number of voxels), respectively. A set of 20 histogram statistics features such as mean, standard deviation, skewness, kurtosis, energy, and entropy 29 , was calculated for both lesions and NLLV.

(The list of histogram textures refers to Appendix 1)

Data analysis was focused on two aspects: firstly, the role of CT quantification in the assessment of disease severity and prediction of clinical outcome, and secondly, the predictive power of machine-learning (ML) models which combine clinical data, CT quantification and image textures to predict the need and duration of ICU, duration of oxygen inhalation, duration of hospitalization, duration of sputum NAT-positive, and clinical prognosis.

To assess whether CT quantification is a significant parameter in the assessment of disease severity and prognosis, we calculated the significance of the quantitative imaging biomarkers of the disease (lesion volume, NLLV, and %NLLV) for three disease severity groups (moderate, severe, and critical), and four prognosis groups (complete recovery, partial recovery with residual pulmonary damage, prolonged recovery, and death). A Chi-squared (χ 2 ) test was performed to evaluate whether a biomarker is significantly different with the null hypothesis (H0) that there is no difference for this biomarker among groups of different diseases severity and outcomes. This null hypothesis will be rejected if p-value < 0.05 whereby this biomarker is considered significantly different among different disease severity groups and different clinical outcome groups, which is an indicator that the biomarker is a significant parameter of disease severity and clinical outcomes.

To assess significant parameters in the prediction of clinical outcomes, we performed Boruta algorithm to select significant features related to disease severity and prognosis. In Boruta, each The performance of the RF classifiers and regressors trained in this study were evaluated using repeated cross-validation (100 repetitions, 10-fold partition for each repetition). The RF model performance was compared using the area under the receiver operating characteristic (ROC) curves (AUC) values with 95% CI and that of RF regressors using the root-mean-square error (RMSE).

All statistical analyses were performed using our 3DQI radiomics tool, whose core components are based on the statistical programming language R (V3. 6 

Anonymized data will be shared by request from any qualified investigators: (1) 

The chest CT images of 99 patients were subjected to the process of volumetric image analysis of COVID-19 pneumonia. Subsequently, the significance of CT quantification and the performance of prediction models were evaluated. The clinical prognoses were complete recovery (n=3), partial recovery with residual pulmonary damage (n=80), prolonged recovery (n=15), and death (n=1), respectively. The differences between partial recovery and prolonged recovery groups were statistically significant including the ICU days (p<0.0001), the oxygen inhalation days (p<0.0001), the hospitalization days (p<0.0001), and the days of sputum NAT-positive (p=0.0008). The numbers of patients in the complete recovery (n=3) and death (n=1) were too small to calculate the statistical significance.

We evaluated the performance of our two trained U-Net models for segmentation of lung and lesions by using 99 chest CT scans in this study. The mean DSCs (dice similarity coefficient) were 0.981 for the lung segmentation, and 0.778 for the lesion segmentation. In the moderate, sever, and critical groups, the average DSCs were 0.990, 0.987, 0.961 for segmentation of lungs, and 0.746, 0.790, 0.826 for segmentation of lesions, respectively. Table 3 lists the performance of lung and lesion segmentation. In general, lung segmentation performed better in less severely diseased groups, whereas lesion segmentation performed better in more severely diseased group in term of DSC and Jaccard index. With the disease progression, a decrease in GGO and an increase in consolidation were observed. In terms of relative volume difference (RVD), these differences changed from over-segmentation (positive RVD) to under-segmentation (negative RVD). On average, the segmentation process of each CT case took 0.8s.

The lung volume, lesion volume, NLLV and %NLLV were calculated based on the segmentation results. Figure 3 shows three example cases, one for each severity group. The quantification of disease in terms of disease severity and prognosis are listed in Table 4 

We applied the feature selection method to build the RF models for classification of disease severity. Table 5 lists the top 5 selected features, if any, and the performances of the radiomics models, clinical data models, and the hybrid models. Figure 4 shows the ROC curves of three models for classification of disease severity. respectively. This performance was significantly higher than either the radiomic models or clinical models with p<0.01.

We applied the RF classification and regression models for the prediction of various clinical outcomes: need and duration of ICU, duration of hospitalization, duration of oxygen inhalation, duration of sputum NAT-positive, and the prediction of prognosis. Table 6 lists the top 5 selected features and the prediction performance of four RF regression models and two RF classification models. We observed that %NLLV was among the top 5 features in four out of six RF models, which was ranked second after Age. %NLLV was the only selected quantification biomarker related to the disease size. In addition, we observed that some histogram features such as variance, MAD (mean absolute deviation) of NNLV, has significant contributes to the prediction of duration of ICU and recovery. Although the RMSEs of our RF models are not found to be considerably smaller than the standard deviation, they are, however, consistently better than the values obtained by other machine learning methods such as LASSO (Least Absolute Shrinkage and Selection Operator)

Due to the limited number of patients in the prognosis of complete recovery (n=3) and death (n=1) group, we only performed the prediction of partial recovery and prolonged recovery. The RF model achieved a high AUC of 0.960 for prediction of patient prognosis.

In this study, we investigated the AI-assisted CT quantitation and ML models demonstrated potentials for prediction of disease severity or clinical outcomes. Our study achieved a better performance to stratify the severity into moderate, sever, and critical groups with AUC more than 0.925. According to the CHC infection 8 Although studies reported that 97% of COVID-19 patients showed positive in CT by using an AI-based detection model 22 ; however, some studies also found that the typical CT signs such as GGO might not observed in some mild or asymptomatic patients that were confirmed by positive NAT of RT-PCR 12 . Thus, NAT is still the gold standard for clinical diagnosis of COVID-19.

Considering the fact that CT has high sensitivity in identifying GGO lesions, we believe that CT is being and will be used in the management of severely ill patients in particular for advanced progressive cases or those with complications for the decision-making of ICU treatment, and the prediction of ICU stay. This will directly assist physicians in the management of COVID-19

patients. This is also the primary aim of our study, to demonstrate the validity of CT quantification in the decision-making and prediction of clinical outcomes in the management of COVID-19 patients.

We investigated the ML models combining CT quantification and image textures in classifying disease severity with AUC > 0.925, prediction of clinical outcomes such as the need of ICU treatment on admission with AUC of 0.945, and the prediction of prognosis of partial recovery vs prolonged recovery with AUC of 0.960. In addition, we demonstrated that neither radiomics models nor clinical models could achieve this high performance as hybrid models. This may indicate that some of the disease characteristics might not be captured by CT imaging alone. For instance age has been selected by both classification models in Table 5 , and 5 out of 6 models in Table 6 . For better understanding the importance of the CT features in the predicting outcomes of the disease, we compared the performance between the performance with the CT features and without CT features in Table 6 . CT features were found to offer consistent performance improvement for ML models. For the three regression models, the RMSE (lower value indicates better performance) became higher if the models were trained without the CT features. In particular, the model to predict the duration of ICU was degraded in performance by as much as 16%. For the two binary classification problems, the AUC and accuracy (higher value indicates better performance) were both negatively affected by removal of CT features. Whereas the model to predict the need of ICU became slightly worse, performance of the model to predict prognosis dropped precipitously. We will explore comprehensive textures instead of only histogram features in future.

Considering the imbalanced number of patients in each group, which may bias our observations due to "within-patient clustering" artifact, we used the SMOTE resampling method 31 This study had several limitations. The first limitation was the relatively small number of cases for sufficiently training of our U-Net models for lesion segmentation. This caused some of the interaction efforts for modifying the results of the automated segmentation. We will collect more cases to improve the accuracy of the U-Net models for lesion segmentation, as well as using other deep-learning models such as ResNet to classify COVID-19 lesions from other viral pneumonia lesions and lung tissues. In addition, since the segmentation of lung had a very high accuracy, we will also work on the imaging biomarker calculated in the lung region instead of lesions for classification and prediction. Another limitation is that our study used single-center data. We plan to collect multi-center cases to train and validate our models for segmentation and prediction. Our models need multi-center data for further external validation.

Overall, the validity of CT in the management of COVID-19 patients has been held back by its controversial specificity in the diagnosis of COVID-19 pneumonia, whereas NAT remains the gold standard of the diagnosis. Our studies imposed that AI-assisted CT quantification and ML models may be an effective tool assisting the decision-making in the management of hospitalized patients such as prediction of ICU treatment, the duration of oxygen inhalation, and prognosis, which are critical questions in the management of patients, in particular for severely or critically ill patients. We observed that %NLLV and other imaging textures are significant imaging biomarkers in the management of the COVID-19, and ML models may achieve significance high performance for prediction of clinical outcomes.

Although the findings of this study warrant validation by larger multi-center studies, it may provide a new dimension for investigating the validity of CT focusing on clinical management of hospitalized severely ill patients, including the decision-making of ICU treatment, the duration of ICU, oxygen inhalation, and hospitalization, which are essential for clinical management. Table Legends   Table 1 . Demographic, symptom, arterial blood gas test, and routine blood test on admission Table 2 . Clinical outcomes in terms of disease severity and prognosis. Table 3 . Performance of U-Net models for segmentation of lung and lesions Table 4 . CT quantification of lung volume, lesion volume, non-lesion lung volume(NLLV) (lung volume -lesion volume) and fraction of non-lesion lung volume(%NLLV) (non-lesion lung volume/ lung volume), as well as the mean CT value of lesions and NLLV in terms of disease severity and prognosis Table 8 . Clinical outcomes in terms of disease severity and prognosis. As per disease severity, 99 patients were categorized into three groups: moderate (n=25), severe (n=47), and critical (n=27), respectively. As per prognosis, patients was categorized into 1: complete recovery (n=3), 2:

partial recovery with residual pulmonary damage (n=80), 3: prolonged recovery (n=15), and 4: death (n=1).

Severe Critical Table 10 . CT quantification of lung volume, lesion volume, non-lesion lung volume (NLLV) (lung volume -lesion volume) and fraction of non-lesion lung volume (%NLLV) (non-lesion lung volume / lung volume), as well as the mean CT value of lesions and NLLV in terms of disease severity and prognosis.

Severe Critical 

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