Multi-classifier-based identification of COVID-19 from chest computed tomography using generalizable and interpretable radiomics features

Wang, Lu; Kelly, Brendan; Lee, Edward H.; Wang, Hongmei; Zheng, Jimmy; Zhang, Wei; Halabi, Safwan; Liu, Jining; Tian, Yuan; Han, Baoqin; Huang, Chuanbin; Yeom, Kristen W.; Deng, Kan; Song, Jiangdian

doi:10.1016/j.ejrad.2021.109552

Cited by 27 publications

(16 citation statements)

References 41 publications

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“…After assessment of the title and abstracts, ten publications were selected, and their full texts were retrieved. One observational study [30] without radiomics application in the research and two observational studies [31,32] with a repetitive patient population were excluded. After the article selection process, seven articles were used in the qualitative analysis [33][34][35][36][37][38][39], and six articles were further used in the meta-analysis.…”

Section: Literature Collectionmentioning

confidence: 99%

A Meta-Analysis of Computerized Tomography-Based Radiomics for the Diagnosis of COVID-19 and Viral Pneumonia

Kao

Lin

2021

Diagnostics

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Introduction: Coronavirus disease 2019 (COVID-19) led to a global pandemic. Although reverse transcription polymerase chain reaction (RT-PCR) of viral nucleic acid is the gold standard for COVID-19 diagnosis, its sensitivity was found to not be high enough in many reports. As radiomics-based diagnosis research has recently emerged, we aimed to use computerized tomography (CT)-based radiomics models to differentiate COVID-19 pneumonia from other viral pneumonia infections. Materials and methods: This study was performed according to the preferred reporting items for systematic review and meta-analysis diagnostic test accuracy studies (PRISMA-DTA) guidelines. The Pubmed, Cochrane, and Embase databases were searched. The pooled sensitivity and pooled specificity were calculated. A summary receiver operating characteristic (sROC) curve was constructed. The study quality was evaluated based on the radiomics quality score. Results: A total of 10,300 patients were involved in this meta-analysis. The radiomics quality score ranged from 13 to 16 (maximum score: 36). The pooled sensitivity was 0.885 (95% CI: 0.818-0.929), and the pooled specificity was 0.811 (95% CI: 0.667-0.902). The pooled AUC was 906. Conclusion: Our meta-analysis showed that CT-based radiomics feature models can successfully differentiate COVID-19 from other viral pneumonias.

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Section: Literature Collectionmentioning

confidence: 99%

A Meta-Analysis of Computerized Tomography-Based Radiomics for the Diagnosis of COVID-19 and Viral Pneumonia

Kao

Lin

2021

Diagnostics

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“…Although segmentation can be performed manually in radiomics modeling studies related to COVID-19 [ 18 , 40 ], this method takes considerable time due to the large number of lesions per patient, and the reproducibility problem needs to be overcome. Methods such as the segmentation of the entire lung (healthy and diseased), rather than individual lesions, have been suggested [ 36 ].…”

Section: Discussionmentioning

confidence: 99%

Evaluation of the models generated from clinical features and deep learning-based segmentations: Can thoracic CT on admission help us to predict hospitalized COVID-19 patients who will require intensive care?

et al. 2022

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Background The aim of the study was to predict the probability of intensive care unit (ICU) care for inpatient COVID-19 cases using clinical and artificial intelligence segmentation-based volumetric and CT-radiomics parameters on admission. Methods Twenty-eight clinical/laboratory features, 21 volumetric parameters, and 74 radiomics parameters obtained by deep learning (DL)-based segmentations from CT examinations of 191 severe COVID-19 inpatients admitted between March 2020 and March 2021 were collected. Patients were divided into Group 1 (117 patients discharged from the inpatient service) and Group 2 (74 patients transferred to the ICU), and the differences between the groups were evaluated with the T-test and Mann–Whitney test. The sensitivities and specificities of significantly different parameters were evaluated by ROC analysis. Subsequently, 152 (79.5%) patients were assigned to the training/cross-validation set, and 39 (20.5%) patients were assigned to the test set. Clinical, radiological, and combined logit-fit models were generated by using the Bayesian information criterion from the training set and optimized via tenfold cross-validation. To simultaneously use all of the clinical, volumetric, and radiomics parameters, a random forest model was produced, and this model was trained by using a balanced training set created by adding synthetic data to the existing training/cross-validation set. The results of the models in predicting ICU patients were evaluated with the test set. Results No parameter individually created a reliable classifier. When the test set was evaluated with the final models, the AUC values were 0.736, 0.708, and 0.794, the specificity values were 79.17%, 79.17%, and 87.50%, the sensitivity values were 66.67%, 60%, and 73.33%, and the F1 values were 0.67, 0.62, and 0.76 for the clinical, radiological, and combined logit-fit models, respectively. The random forest model that was trained with the balanced training/cross-validation set was the most successful model, achieving an AUC of 0.837, specificity of 87.50%, sensitivity of 80%, and F1 value of 0.80 in the test set. Conclusion By using a machine learning algorithm that was composed of clinical and DL-segmentation-based radiological parameters and that was trained with a balanced data set, COVID-19 patients who may require intensive care could be successfully predicted.

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“…A radiomics model combining 8 radiomics features and 5 selected clinical variables was constructed and used for the diagnosis of COVID-19 pneumonia [ 116 ] The combined radiomics model achieved a better diagnostic accuracy, compared to CO-RADS used by radiologists, with a 85% sensitivity and 90% specificity. Wang et al [ 125 ] developed a radiomics-feature-based model that was significantly associated with the classification of COVID-19 pneumonia using a multi-classifier approach. The findings of this study extend the understanding of imaging characteristics of COVID-19 pneumonia.…”

Section: Clinical Impact Of Ai-based Covid-19 Studiesmentioning

confidence: 99%

Artificial intelligence-driven assessment of radiological images for COVID-19

Bouchareb

Khaniabadi

Kindi

et al. 2021

Computers in Biology and Medicine

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Artificial Intelligence (AI) methods have significant potential for diagnosis and prognosis of COVID-19 infections. Rapid identification of COVID-19 and its severity in individual patients is expected to enable better control of the disease individually and at-large. There has been remarkable interest by the scientific community in using imaging biomarkers to improve detection and management of COVID-19. Exploratory tools such as AI-based models may help explain the complex biological mechanisms and provide better understanding of the underlying pathophysiological processes. The present review focuses on AI-based COVID-19 studies as applies to chest x-ray (CXR) and computed tomography (CT) imaging modalities, and the associated challenges. Explicit radiomics, deep learning methods, and hybrid methods that combine both deep learning and explicit radiomics have the potential to enhance the ability and usefulness of radiological images to assist clinicians in the current COVID-19 pandemic. The aims of this review are: first, to outline COVID-19 AI-analysis workflows, including acquisition of data, feature selection, segmentation methods, feature extraction, and multi-variate model development and validation as appropriate for AI-based COVID-19 studies. Secondly, existing limitations of AI-based COVID-19 analyses are discussed, highlighting potential improvements that can be made. Finally, the impact of AI and radiomics methods and the associated clinical outcomes are summarized. In this review, pipelines that include the key steps for AI-based COVID-19 signatures identification are elaborated. Sample size, non-standard imaging protocols, segmentation, availability of public COVID-19 databases, combination of imaging and clinical information and full clinical validation remain major limitations and challenges. We conclude that AI-based assessment of CXR and CT images has significant potential as a viable pathway for the diagnosis, follow-up and prognosis of COVID-19.

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Multi-classifier-based identification of COVID-19 from chest computed tomography using generalizable and interpretable radiomics features

Cited by 27 publications

References 41 publications

A Meta-Analysis of Computerized Tomography-Based Radiomics for the Diagnosis of COVID-19 and Viral Pneumonia

A Meta-Analysis of Computerized Tomography-Based Radiomics for the Diagnosis of COVID-19 and Viral Pneumonia

Evaluation of the models generated from clinical features and deep learning-based segmentations: Can thoracic CT on admission help us to predict hospitalized COVID-19 patients who will require intensive care?

Artificial intelligence-driven assessment of radiological images for COVID-19

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