Abstract:To explore the possibility of predicting the clinical types of Corona-Virus-Disease-2019 (COVID-19) pneumonia by analyzing the non-focus area of the lung in the first chest CT image of patients with COVID-19 by using automatic machine learning (Auto-ML). 136 moderate and 83 severe patients were selected from the patients with COVID-19 pneumonia. The clinical and laboratory data were collected for statistical analysis. The texture features of the Non-focus area of the first chest CT of patients with COVID-19 pn… Show more
“…When comparing these two feature categories, it appeared that whole lung features have higher AUCs. A recent study by Tan et al [ 58 ] demonstrated the predictive value of non-focus area of CT images to distinguish different clinical types of COVID-19 pneumonia. In our study, whole lung features provided more relevant characteristics of the disease in COVID-19 patients.…”
Objective
To develop prognostic models for survival (alive or deceased status) prediction of COVID-19 patients using clinical data (demographics and history, laboratory tests, visual scoring by radiologists) and lung/lesion radiomic features extracted from chest CT images.
Methods
Overall, 152 patients were enrolled in this study protocol. These were divided into 106 training/validation and 46 test datasets (untouched during training), respectively. Radiomic features were extracted from the segmented lungs and infectious lesions separately from chest CT images. Clinical data, including patients’ history and demographics, laboratory tests and radiological scores were also collected. Univariate analysis was first performed (q-value reported after false discovery rate (FDR) correction) to determine the most predictive features among all imaging and clinical data. Prognostic modeling of survival was performed using radiomic features and clinical data, separately or in combination. Maximum relevance minimum redundancy (MRMR) and XGBoost were used for feature selection and classification. The receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC), sensitivity, specificity, and accuracy were used to assess the prognostic performance of the models on the test datasets.
Results
For clinical data, cancer comorbidity (q-value < 0.01), consciousness level (q-value < 0.05) and radiological score involved zone (q-value < 0.02) were found to have high correlated features with outcome. Oxygen saturation (AUC = 0.73, q-value < 0.01) and Blood Urea Nitrogen (AUC = 0.72, q-value = 0.72) were identified as high clinical features. For lung radiomic features, SAHGLE (AUC = 0.70) and HGLZE (AUC = 0.67) from GLSZM were identified as most prognostic features. Amongst lesion radiomic features, RLNU from GLRLM (AUC = 0.73), HGLZE from GLSZM (AUC = 0.73) had the highest performance. In multivariate analysis, combining lung, lesion and clinical features was determined to provide the most accurate prognostic model (AUC = 0.95 ± 0.029 (95%CI: 0.95-0.96), accuracy = 0.88 ± 0.046 (95% CI: 0.88-0.89), sensitivity = 0.88 ± 0.066 (95% CI = 0.87-0.9) and specificity = 0.89 ± 0.07 (95% CI = 0.87-0.9)).
Conclusion
Combination of radiomic features and clinical data can effectively predict outcome in COVID-19 patients. The developed model has significant potential for improved management of COVID-19 patients.
“…When comparing these two feature categories, it appeared that whole lung features have higher AUCs. A recent study by Tan et al [ 58 ] demonstrated the predictive value of non-focus area of CT images to distinguish different clinical types of COVID-19 pneumonia. In our study, whole lung features provided more relevant characteristics of the disease in COVID-19 patients.…”
Objective
To develop prognostic models for survival (alive or deceased status) prediction of COVID-19 patients using clinical data (demographics and history, laboratory tests, visual scoring by radiologists) and lung/lesion radiomic features extracted from chest CT images.
Methods
Overall, 152 patients were enrolled in this study protocol. These were divided into 106 training/validation and 46 test datasets (untouched during training), respectively. Radiomic features were extracted from the segmented lungs and infectious lesions separately from chest CT images. Clinical data, including patients’ history and demographics, laboratory tests and radiological scores were also collected. Univariate analysis was first performed (q-value reported after false discovery rate (FDR) correction) to determine the most predictive features among all imaging and clinical data. Prognostic modeling of survival was performed using radiomic features and clinical data, separately or in combination. Maximum relevance minimum redundancy (MRMR) and XGBoost were used for feature selection and classification. The receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC), sensitivity, specificity, and accuracy were used to assess the prognostic performance of the models on the test datasets.
Results
For clinical data, cancer comorbidity (q-value < 0.01), consciousness level (q-value < 0.05) and radiological score involved zone (q-value < 0.02) were found to have high correlated features with outcome. Oxygen saturation (AUC = 0.73, q-value < 0.01) and Blood Urea Nitrogen (AUC = 0.72, q-value = 0.72) were identified as high clinical features. For lung radiomic features, SAHGLE (AUC = 0.70) and HGLZE (AUC = 0.67) from GLSZM were identified as most prognostic features. Amongst lesion radiomic features, RLNU from GLRLM (AUC = 0.73), HGLZE from GLSZM (AUC = 0.73) had the highest performance. In multivariate analysis, combining lung, lesion and clinical features was determined to provide the most accurate prognostic model (AUC = 0.95 ± 0.029 (95%CI: 0.95-0.96), accuracy = 0.88 ± 0.046 (95% CI: 0.88-0.89), sensitivity = 0.88 ± 0.066 (95% CI = 0.87-0.9) and specificity = 0.89 ± 0.07 (95% CI = 0.87-0.9)).
Conclusion
Combination of radiomic features and clinical data can effectively predict outcome in COVID-19 patients. The developed model has significant potential for improved management of COVID-19 patients.
“…The AI algorithm based on the neural network was established in a number of research articles [ 21 , 22 , 23 , 25 , 26 , 27 , 29 , 30 , 31 , 33 , 34 , 35 , 36 , 37 , 41 , 42 , 43 , 47 , 48 , 50 , 51 , 52 , 53 , 54 , 55 , 57 ]. Among the included studies, twenty-nine models were selected for meta-analysis on DL assisted detection for predict COVID-19 [ 21 , 22 , 25 , 26 , 27 , 30 , 33 , 34 , 35 , 36 , 37 , 40 , 41 , 42 , 46 , 47 , 50 , 51 , 52 , 53 , 54 , 56 , 57 ] and fourteen models on ML assisted detection for predict COVID-19 [ 21 , 24 , 28 , 31 , 38 , 43 , 45 , 46 , 48 , 49 ] ( Table 1 ).…”
Section: Resultsmentioning
confidence: 99%
“…Among the 37 studies [ 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 ] of image-based analysis, the pooled sensitivity was 0.90 (95% CI, 0.90 - 0.91), specificity was 0.90 (95% CI, 0.90 - 0.91), the AUC was 0.96 (95% CI, 0.91 - 0.98), and diagnostic odds ratio (DOR) was 88.98 (95% CI, 56.38 – 140.44) as shown in ( Figure 2 ) ( Supplementary Figures 2-8 ). Figure 2 The summary receiver-operating characteristic (SROC) curves of the diagnostic performance of AI and CT-Scan on detection.…”
Section: Diagnostic Test Accuracy (Dta)mentioning
confidence: 99%
“…Among the 9 studies [ 21 , 24 , 28 , 38 , 43 , 45 , 46 , 48 , 49 ] of image-based analysis, the pooled sensitivity was 0.91 (95% CI, 0.90 - 0.91), specificity was 0.95 (95% CI, 0.94 - 0.95), the AUC was 0.97 (95% CI, 0.96-0.99), and DOR was 88.27 (95% CI, 29.52 – 263.96) as shown in ( Figure 4 ) ( Supplementary Figures 4-8 ). Figure 4 The summary receiver-operating characteristic (SROC) curves of the diagnostic performance of ML and CT-Scan on detection.…”
“…Moreover, a radiomic model was demonstrated to predict the tumor invasiveness of pulmonary adenocarcinomas appearing as ground-glass nodules (18,21). Radiomics-based models were recently proposed to improve the diagnosis of COVID-19 on chest CT images (22)(23)(24). To the best of our knowledge, no studies investigated the possibility of discriminating the GGOs due to COVID-19 pneumonia from those due to non-COVID-19 acute lung disease.…”
Ground-Glass Opacities (GGOs) are a non-specific CT finding observed in the early phase of COVID-19 pneumonia. However, GGOs are also seen in other acute interstitial and alveolar lung diseases, thus making the differential diagnosis a diagnostic challenge. In this poof-of-concept study, we aimed to differentiate COVID-19 pneumonia presenting with GGOs from acute non-COVID-19 lung disease using a novel radiomic-based model in patients who underwent a high-resolution CT (HRCT) scan at hospital admission during the first pandemic peak in Italy. HRCT scans of 28 RT-PCR diagnosed COVID-19 pneumonia (COVID) and 30 acute non-COVID-lung disease (nCOVID) were retrospectively included. All patients showed GGOs as the predominant CT pattern. Two readers, blinded to the final diagnosis, independently segmented GGOs on CT scans by using a semi-automated approach, and radiomic features were extracted from segmented images. Partial least square (PLS) regression was used as the multivariate machine-learning algorithm. A leave-one-out nested cross-validation was implemented to optimize the hyperparameter of PLS and to assess the model generalization. The diagnostic performance of the radiomic model to differentiate between COVID and nCOVID lung disease was assessed through receiver operating characteristic (ROC) analysis. The radiomics-based machine learning model differentiated COVID and nCOVID with an AUC = 0.868 (p = 4.2·10− 7). After a careful prospective evaluation in larger multicentric studies, it may help radiologists to rule out COVID-19 pneumonia thus improving the COVID-19 triaging in epidemic areas.
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