Severity Assessment of COVID-19 Using a CT-Based Radiomics Model

Xu, Zhigao; Zhao, Lili; Yang, Guoqiang; Ren, Ying; Wu, Jinlong; Xia, Yuwei; Yang, Xiao; Cao, Milan; Zhang, Guojiang; Peng, Taisong; Zhao, Jingkai; Yang, Hui; Hu, Jinfeng; Du, Jiangfeng

doi:10.1155/2021/2263469

Cited by 12 publications

(15 citation statements)

References 24 publications

(22 reference statements)

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“…(20). Compared with previously published models to assess the severity of COVID-19 (21,22,43), our radiomics nomogram showed a very high predictive performance and robustness. Moreover, in addition to building the diagnostic model, this study has also found some interesting insights into CT radiomics.…”

Section: Discussionmentioning

confidence: 67%

“…In addition to detecting lesions, assessing the grade of COVID-19 pulmonary lesions is important for the hierarchical management and treatment of infected patients (19). However, few studies have focused on the use of AI in grading pulmonary lesions (20), and those that exist have only used "pure" datasets accrued from unified vendor scanners (21,22), which limits the real generalizability of their AI models (23). Thus, it is necessary to develop a more robust AI grading tool based on real-world multicenter datasets.…”

Section: Introductionmentioning

confidence: 99%

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Wavelet transformation can enhance computed tomography texture features: a multicenter radiomics study for grade assessment of COVID-19 pulmonary lesions

Jiang¹,

Jin²,

Han³

et al. 2022

Quant Imaging Med Surg

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Background: This study set out to develop a computed tomography (CT)-based wavelet transforming radiomics approach for grading pulmonary lesions caused by COVID-19 and to validate it using real-world data.Methods: This retrospective study analyzed 111 patients with 187 pulmonary lesions from 16 hospitals; all patients had confirmed COVID-19 and underwent non-contrast chest CT. Data were divided into a training cohort (72 patients with 127 lesions from nine hospitals) and an independent test cohort (39 patients with 60 lesions from seven hospitals) according to the hospital in which the CT was performed. In all, 73 texture features were extracted from manually delineated lesion volumes, and 23 three-dimensional (3D) wavelets with eight decomposition modes were implemented to compare and validate the value of wavelet transformation for grade assessment. Finally, the optimal machine learning pipeline, valuable radiomic features, and final radiomic models were determined. The area under the receiver operating characteristic (ROC) curve (AUC), calibration curve, and decision curve were used to determine the diagnostic performance and clinical utility of the models.Results: Of the 187 lesions, 108 (57.75%) were diagnosed as mild lesions and 79 (42.25%) as moderate/ severe lesions. All selected radiomic features showed significant correlations with the grade of COVID-19 pulmonary lesions (P<0.05). Biorthogonal 1.1 (bior1.1) LLL was determined as the optimal wavelet transform mode. The wavelet transforming radiomic model had an AUC of 0.910 in the test cohort, outperforming the original radiomic model (AUC =0.880; P<0.05). Decision analysis showed the radiomic model could add a net benefit at any given threshold of probability.Conclusions: Wavelet transformation can enhance CT texture features. Wavelet transforming radiomics based on CT images can be used to effectively assess the grade of pulmonary lesions caused by COVID-19, ^ ORCID: Zekun Jiang,

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Section: Discussionmentioning

confidence: 67%

Section: Introductionmentioning

confidence: 99%

Wavelet transformation can enhance computed tomography texture features: a multicenter radiomics study for grade assessment of COVID-19 pulmonary lesions

Jiang¹,

Jin²,

Han³

et al. 2022

Quant Imaging Med Surg

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“…In AI modelling for prediction, Xu et al . [30] assessed the CT-based model to the predictive ability of the support vector machine for COVID-19 severity in four groups: early, progressive, severe, and absorption. Macro-average of four class-classifications yielded AUCs of 0.97 and 0.9, respectively, for the training and test sets.…”

Section: Discussionmentioning

confidence: 99%

“…Shape features illustrate properties of the size and shape of the images, which was found to be effective in the classification/severity prediction of COVID-19 and pneumonia infections. Indication of shape feature dimensions was suggested to be investigated during each phase of COVID-19 [30, 37, 38].…”

Section: Discussionmentioning

confidence: 99%

“…High quality 3D CT images have been utilized for COVID-19 identification and patient management. Since early days of the pandemic, numerous ML-based studies were focused on different tasks including diagnosing, prognostic, severity prediction utilizing binary or multiple classifications, using CT scans as illustrated in Table 4 [21,[26][27][28][29][30][31]. Radiomics has attracted significant attention since early pandemic studies as a fully non-invasive tool for an in-depth quantitative analysis of CT images.…”

Section: Discussionmentioning

confidence: 99%

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Two-Step Machine Learning to Diagnose and Predict Involvement of Lungs in COVID-19 and Pneumonia using CT Radiomics

Khaniabadi

Bouchareb

Dhuhli

et al. 2022

Preprint

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Objective: We aimed to develop a two-step machine learning (ML) based model to diagnose and predict involvement of lungs in COVID-19 and non COVID-19 pneumonia patients using CT chest radiomic features. Methods: Three hundred CT scans (3-classes: 100 COVID-19, 100 pneumonia, and 100 healthy subjects) were enrolled in this study. Diagnostic task included 3-class classification. For severity prediction, two radiologists scored involvement of lungs in COVID-19 and pneumonia scans based on percentage of involvement in all 5 lobes. Datasets were classified into mild (0–25%), moderate (26–50%), and severe (>50%). Whole lungs were segmented utilizing deep learning-based segmentation method. Altogether, 107 features including shape, first-order histogram, second and high order texture features were extracted. For both tasks, datasets were randomly divided into 90% training sets (70% and 30% for training and validation, respectively) and 10% test sets. Pearson correlation coefficient (PCC≥90%) was performed to exclude highly correlated features. Subsequently, different feature selection algorithms (Correlation attribute evaluation, Information gain attribute, Wrapper Subset selection algorithm, Relief method, and Correlation-based feature selection) were assessed. The most pertinent features were finally selected using voting method based on the evaluation of all algorithms. Several ML-based supervised algorithms were utilized, namely Naïve Bays, Support Vector Machine, Bagging, Random Forest, K-nearest neighbors, Decision Tree and Ensemble Meta voting. The synthetic minority oversampling technique (SMOTE) was used to balance the three classes in training sets. The optimal model was first selected based on precision, recall and area-under-curve (AUC) by randomizing the training/validation sets 20 times, followed by testing using the test set. To ensure the repeatability of the results, the entire process was repeated 50 times. Results: Nine pertinent features (2 shape, 1 first-order, and 6 second-order features) were obtained after feature selection for both phases. In diagnostic task, the performance of 3-class classification using Random Forest was 0.909±0.026, 0.907±0.056, 0.902±0.044, 0.939±0.031, and 0.982±0.010 for precision, recall, F1-score, accuracy, and AUC, respectively. The severity prediction task using Random Forest achieved 0.868±0.123 precision, 0.865±0.121 recall, 0.853±0.139 F1-score, 0.934±0.024 accuracy, and 0.969±0.022 AUC. Conclusion: The two-phase ML-based model accurately classified COVID-19 and pneumonia patients using CT radiomics, and adequately predicted severity of lungs involvement. This 2-steps model showed great potential in assessing COVID-19 CT images towards improved management of patients.

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Prediction of potential severe coronavirus disease 2019 patients based on CT radiomics: A retrospective study

Xiao

Sun

et al. 2022

Medical Physics

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Purpose Coronavirus disease 2019 (COVID‐19) is a recently declared worldwide pandemic. Triaging of patients into severe and non‐severe could further help in targeted management. “Potential severe patients” is a category of patients who did not have severe symptoms at their initial diagnosis, but eventually progressed to be severe patients and are easily overlooked in the early stage. This work aimed to develop and evaluate a CT‐based radiomics signature for the prediction of these potential severe COVID‐19 patients. Methods One hundred fifty COVID‐19 patients were enrolled and randomly divided into cross‐validation and independent test sets. First, their clinical characteristics were screened using the univariate and multivariate logistic regression step by step. Then, radiomics features were extracted from the lesions on their chest CT images. Subsequently, the inter‐ and intra‐class correlation coefficients (ICC) analysis, minimum‐redundancy maximum‐relevance (mRMR) selection, and the least absolute shrinkage and selection operator (LASSO) algorithm were used step by step for feature selection and construction of a radiomics signature. Finally, the screened clinical risk factors and constructed radiomics signature were combined for the combined model and Radiomics+Clinics nomogram construction. The predictive performance of the Radiomics and Combined models were evaluated and compared using receiver operating characteristic curve (ROC) analysis, Hosmer–Lemeshow test and Delong test. Results Clinical characteristics analysis resulted in the screening of five clinical risk factors. The combination of ICC, mRMR, and LASSO methods resulted in the selection of ten radiomics features, which made up of the radiomics signature. The differences in the radiomics signature between the potential severe and non‐severe groups in cross‐validation set and test sets were both p < 0.001. All Radiomics and Combined models showed a very good predictive performance with the accuracy and AUC of nearly or above 0.9. Additionally, we found no significant difference in the predictive performance between these two models. Conclusions A CT‐based radiomics signature for the prediction of potential severe COVID‐19 patients was constructed and evaluated. Constructed Radiomics and Combined model showed good feasibility and accuracy. The Radiomics+Clinical nomogram, acted as a useful tool, may assist clinicians to better identify potential severe cases to target their management in the COVID‐19 pandemic prevention and control.

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Severity Assessment of COVID-19 Using a CT-Based Radiomics Model

Cited by 12 publications

References 24 publications

Wavelet transformation can enhance computed tomography texture features: a multicenter radiomics study for grade assessment of COVID-19 pulmonary lesions

Wavelet transformation can enhance computed tomography texture features: a multicenter radiomics study for grade assessment of COVID-19 pulmonary lesions

Two-Step Machine Learning to Diagnose and Predict Involvement of Lungs in COVID-19 and Pneumonia using CT Radiomics

Prediction of potential severe coronavirus disease 2019 patients based on CT radiomics: A retrospective study

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