CT radiomics can help screen the Coronavirus disease 2019 (COVID-19): a preliminary study

Fang, Minghao; He, Bing; Li, Li; Dong, Di; Yang, Xin; Li, Cong; Meng, Lingwei; Zhong, Lianzhen; Li, Hailin; Liu, Hongjun; Tian, Jie

doi:10.1007/s11432-020-2849-3

Cited by 63 publications

(61 citation statements)

References 26 publications

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“…Despite promising results in recent studies [74,84], many AI models were tested in small datasets. The studies using small dataset (e.g., <300 patients) often showed high AUC or accuracy.…”

Section: Discussionmentioning

confidence: 99%

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The Role of Imaging in the Detection and Management of COVID-19: A Review

Dong

Tang

Wang

et al. 2021

IEEE Rev. Biomed. Eng.

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345

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Coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is spreading rapidly around the world, resulting in a massive death toll. Lung infection or pneumonia is the common complication of COVID-19, and imaging techniques, especially computed tomography (CT), have played an important role in diagnoses and treatment assessment of the disease. Herein, we review the use of imaging characteristics and computing models that have been applied for the management of COVID-19. CT, positron emission tomography -CT (PET/CT), lung ultrasound, and magnetic resonance imaging (MRI) have been used for detection, treatment, and follow-up. The quantitative analysis of imaging data using artificial intelligence (AI) is also explored. Our findings indicate that typical imaging characteristics and their changes can play an important role in the detection and management of COVID-19. In addition, AI or other quantitative image analysis methods are urgently needed to maximize the value of imaging in the management of COVID-19.

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“…Despite promising results in recent studies [74,84], many AI models were tested in small datasets. The studies using small dataset (e.g., <300 patients) often showed high AUC or accuracy.…”

Section: Discussionmentioning

confidence: 99%

“…Fang el al. [74] used radiomics analysis method to diagnose COVID-19. In their study, 77 radiomic features were extracted from manually delineated ROI, and unsupervised consensus clustering was used to select important features that showed a relationship with COVID-19.…”

Section: Ai-based Image Analysis For Covid-19mentioning

confidence: 99%

The Role of Imaging in the Detection and Management of COVID-19: A Review

Dong

Tang

Wang

et al. 2021

IEEE Rev. Biomed. Eng.

Self Cite

345

247

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“…Moreover, the UNet++ used in the segmentation of images may result in segmenting the infection areas that have small blood vessels that reduce the performance of the CAD system. The authors in Fang et al (2020) applied radiomics analysis from a manually delineated region of interest (ROI) CT images to diagnose COVID-19. Afterward, unsupervised consensus clustering was used to choose significant features.…”

Section: Introductionmentioning

confidence: 99%

“…The benefit of this study is using radiomics quantitative analysis as a feature extractor, which is considered as a powerful extractor along several medical domains (Lambin et al, 2012;Kolossváry et al, 2017). However, the main drawback of Fang et al (2020) method is that the authors used only handcrafted features and discarded the advantages of DL techniques, and therefore, they did not attain high performance. Li et al (2020) proposed a technique that depends on ResNet-50 CNN to classify COVID-19.…”

Section: Introductionmentioning

confidence: 99%

FUSI-CAD: Coronavirus (COVID-19) diagnosis based on the fusion of CNNs and handcrafted features

Ragab¹,

Attallah²

2020

PeerJ Computer Science

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The precise and rapid diagnosis of coronavirus (COVID-19) at the very primary stage helps doctors to manage patients in high workload conditions. In addition, it prevents the spread of this pandemic virus. Computer-aided diagnosis (CAD) based on artificial intelligence (AI) techniques can be used to distinguish between COVID-19 and non-COVID-19 from the computed tomography (CT) imaging. Furthermore, the CAD systems are capable of delivering an accurate faster COVID-19 diagnosis, which consequently saves time for the disease control and provides an efficient diagnosis compared to laboratory tests. In this study, a novel CAD system called FUSI-CAD based on AI techniques is proposed. Almost all the methods in the literature are based on individual convolutional neural networks (CNN). Consequently, the FUSI-CAD system is based on the fusion of multiple different CNN architectures with three handcrafted features including statistical features and textural analysis features such as discrete wavelet transform (DWT), and the grey level co-occurrence matrix (GLCM) which were not previously utilized in coronavirus diagnosis. The SARS-CoV-2 CT-scan dataset is used to test the performance of the proposed FUSI-CAD. The results show that the proposed system could accurately differentiate between COVID-19 and non-COVID-19 images, as the accuracy achieved is 99%. Additionally, the system proved to be reliable as well. This is because the sensitivity, specificity, and precision attained to 99%. In addition, the diagnostics odds ratio (DOR) is ≥ 100. Furthermore, the results are compared with recent related studies based on the same dataset. The comparison verifies the competence of the proposed FUSI-CAD over the other related CAD systems. Thus, the novel FUSI-CAD system can be employed in real diagnostic scenarios for achieving accurate testing for COVID-19 and avoiding human misdiagnosis that might exist due to human fatigue. It can also reduce the time and exertion made by the radiologists during the examination process.

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“…Radiomics was applied to many elds of cancer, such as tumor detection, preoperative prediction of lymph node metastasis and therapeutic response assessment [20,22,23]. Recently, radiomics have been proved to be helpful in COVID-19 screening, diagnosis, prediction of hospital stay, assessing the imaging characteristics and risk factors associated with adverse composite endpoints in patients with COVID-19 pneumonia [24][25][26][27]. However, these studies were limited in small sample size.…”

mentioning

confidence: 99%

Machine learning-based CT radiomics model Distinguishes COVID-19 from other viral pneumonia

Chen

et al. 2020

Preprint

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Purpose To develop a machine learning-based CT radiomics model is critical for the accurate diagnosis of the rapid spread Coronavirus disease 2019 (COVID-19).Methods In this retrospective study, a machine learning-based CT radiomics model was developed to extract features from chest CT exams for the detection of COVID-19. Other viral-pneumonia CT exams of the corresponding period were also included. The radiomics features extracted from the region of interest (ROI), the radiological features evaluated by the radiologists, the quantity features calculated by the AI segmentation and evaluation, and the clinical parameters including clinical symptoms, epidemiology history and biochemical results were enrolled in this study. The SVM model was built and the performance on the testing cohort was evaluated by the area under the receiver operating characteristic curve (AUC), sensitivity and specificity. Results For the SVM model that built on the radiomics features only, it reached an AUC of 0.688(95% CI 0.496 to 0.881) on the testing cohort. After the radiological features were enrolled, the AUC achieved 0.696(95% CI 0.501 to 0.892), then the AUC reached 0.753(95% CI 0.596 to 0.910) after the quantity features were included. Our final model employed all the features, reached the per-exam sensitivity and specificity for differentiating COVID-19 was 29 of 38 (0.763, 95% CI: 0.598 to 0.886]) and 12 of 13 (0.923, 95% CI: 0.640 to 0.998]), respectively, with an AUC of 0.968(95% CI 0.911 to 1.000). Conclusion The machine learning-based CT radiomics models may accurately detect COVID-19 and differentiate it from other viral pneumonia.

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CT radiomics can help screen the Coronavirus disease 2019 (COVID-19): a preliminary study

Cited by 63 publications

References 26 publications

The Role of Imaging in the Detection and Management of COVID-19: A Review

The Role of Imaging in the Detection and Management of COVID-19: A Review

FUSI-CAD: Coronavirus (COVID-19) diagnosis based on the fusion of CNNs and handcrafted features

Machine learning-based CT radiomics model Distinguishes COVID-19 from other viral pneumonia

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