A deep learning integrated radiomics model for identification of coronavirus disease 2019 using computed tomography

Zhang, Xiaoguo; Wang, Dawei; Shao, Jiang; Song, Tian; Tan, Wenming; Ma, Yan; Xu, Qing-nan; Ma, Xiaoman; Li, Dasheng; Chai, Jun; Wang, Dingjun; Liu, Wenwen; Lin, Lingbo; Wu, Jiangfen; Chen, Xia; Zhang, Zhongfa

doi:10.1038/s41598-021-83237-6

Cited by 26 publications

(19 citation statements)

References 37 publications

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“…Furthermore, the large data sets derived from advanced imaging analyses have promise for the application of artificial intelligence (AI)-enabled machinelearning approaches as 'agnostic' evaluation of the fundamental relationship between infection and disease [85]. These are being explored in human clinical settings [86][87][88][89][90][91] but may be of higher yield in the controlled experimental settings afforded by animal modeling, including in uncovering findings otherwise unattainable with current readouts, such as in detecting subclinical organ involvement at unexpected sites for which pathological significance would be determined subsequently. Harmonization of scoring systems, or bidirectional/multidirectional exchange of CT images among research groups, and collaborations toward this end would likely move the field forward.…”

Section: Open Accessmentioning

confidence: 99%

Medical imaging of pulmonary disease in SARS-CoV-2-exposed non-human primates

Stammes

Lee

Meijer

et al. 2022

Trends in Molecular Medicine

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Chest X-ray (CXR), computed tomography (CT), and positron emission tomography–computed tomography (PET-CT) are non-invasive imaging techniques widely used in human and veterinary pulmonary research and medicine. These techniques have recently been applied in studies of severe acute respiratory syndrome coronavirus 2-exposed (SARS-CoV-2-exposed) nonhuman primates (NHPs) to complement virologic assessments with meaningful translational readouts of lung disease. Our review of the literature indicates that medical imaging of SARS-CoV-2-exposed NHPs enables high-resolution qualitative and quantitative characterization of disease otherwise clinically invisible and potentially provides user-independent and unbiased evaluation of medical countermeasures. However, we also found high variability in image acquisition and analysis protocols among studies. These findings uncover an urgent need to improve standardization and ensure direct comparability across studies.

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Section: Open Accessmentioning

confidence: 99%

Medical imaging of pulmonary disease in SARS-CoV-2-exposed non-human primates

Stammes

Lee

Meijer

et al. 2022

Trends in Molecular Medicine

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“…Similarly, Fang et al found that the radiomics model has outperformed the clinical model in the prediction/diagnosis of COVID-19 pneumonia [ 30 ]. By using deep learning classifier multi-layer perceptron (DL-MLP), Zhang et al found that DL-MLP achieved optimal performance with AUC of 0.922 (95% CI 0.856–0.988) and 0.959 (95% CI 0.910–1.000), the same sensitivity of 0.879, and specificity of 0.900 and 0.887 on internal and external testing datasets, indicating that DL-MLP may be helpful in efficiently screening COVID-19 patients [ 29 ]. Besides, Tan et al demonstrated that automatic machine learning based on radiomics of non‑focus area in the first chest CT could be used to distinguish different clinical types of COVID‑19 [ 31 ].…”

Section: Discussionmentioning

confidence: 99%

“…Recently, radiomics has been proved to be helpful in COVID-19 screening, diagnosis, prediction the length of hospital stay, and assessment of the imaging characteristics and risk factors associated with adverse composite endpoints in patients with COVID-19 pneumonia [ 25 – 28 ]. Radiomics is also useful in the identification of COVID-19 [ 29 , 30 ], differentiating clinical types of COVID‑19 [ 31 ], and the prediction of poor prognostic outcomes in COVID-19 [ 32 ]. Recently, CT radiomics was found to perform better in the accurate diagnosis of COVID-19 pneumonia compared with the COVID-19 reporting and data system [ 33 ].…”

Section: Introductionmentioning

confidence: 99%

Machine learning-based CT radiomics model distinguishes COVID-19 from non-COVID-19 pneumonia

Chen

Mao²,

Chen

et al. 2021

BMC Infect Dis

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Background To develop a machine learning-based CT radiomics model is critical for the accurate diagnosis of the rapid spreading coronavirus disease 2019 (COVID-19). Methods In this retrospective study, a total of 326 chest CT exams from 134 patients (63 confirmed COVID-19 patients and 71 non-COVID-19 patients) were collected from January 20 to February 8, 2020. A semi-automatic segmentation procedure was used to delineate the volume of interest (VOI), and radiomic features were extracted. The Support Vector Machine (SVM) model was built on the combination of 4 groups of features, including radiomic features, traditional radiological features, quantifying features, and clinical features. By repeating cross-validation procedure, the performance on the time-independent testing cohort was evaluated by the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. Results For the SVM model built on the combination of 4 groups of features (integrated model), the per-exam AUC was 0.925 (95% CI 0.856 to 0.994) for differentiating COVID-19 on the testing cohort, and the sensitivity and specificity were 0.816 (95% CI 0.651 to 0.917) and 0.923 (95% CI 0.621 to 0.996), respectively. As for the SVM models built on radiomic features, radiological features, quantifying features, and clinical features, individually, the AUC on the testing cohort reached 0.765, 0.818, 0.607, and 0.739, respectively, significantly lower than the integrated model, except for the radiomic model. Conclusion The machine learning-based CT radiomics models may accurately classify COVID-19, helping clinicians and radiologists to identify COVID-19 positive cases.

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“…A novel weakly supervised algorithm that combined multi‐instance learning with the long and short‐term memory (LSTM) architecture (MIL‐LSTM) was designed for the discrimination between COVID‐19 and CAP. The lesion layers in 3D CT scans, instead of one randomly selected slice from averaged groups or all slices in CT scans, were selected as the input instances for this novel 3D‐MIL‐LSTM (3DMTM) algorithm using a lesion instance generator based on a pneumonia segmentation model (constructed by Infervision Medical Technology Co., Ltd.), 23 so as to reduce the annotation label force and to enhance model performance by extracting more spatial information of lesions. Meanwhile, another three dimensional convolutional neural network (3D CNN) and three classic machine learning algorithms including logistic regression (LR), k‐nearest neighbor (KNN), and support vector machine (SVM) were also developed using 3D CT data to validate the feasibility of newly proposed algorithm.…”

Section: Methodsmentioning

confidence: 99%

An original deep learning model using limited data for COVID‐19 discrimination: A multicenter study

Lou

Chen

et al. 2022

Medical Physics

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Objectives Artificial intelligence (AI) has been proved to be a highly efficient tool for COVID‐19 diagnosis, but the large data size and heavy label force required for algorithm development and the poor generalizability of AI algorithms, to some extent, limit the application of AI technology in clinical practice. The aim of this study is to develop an AI algorithm with high robustness using limited chest CT data for COVID‐19 discrimination. Methods A three dimensional algorithm that combined multi‐instance learning with the LSTM architecture (3DMTM) was developed for differentiating COVID‐19 from community acquired pneumonia (CAP) while logistic regression (LR), k‐nearest neighbor (KNN), support vector machine (SVM), and a three dimensional convolutional neural network set for comparison. Totally, 515 patients with or without COVID‐19 between December 2019 and March 2020 from five different hospitals were recruited and divided into relatively large (150 COVID‐19 and 183 CAP cases) and relatively small datasets (17 COVID‐19 and 35 CAP cases) for either training or validation and another independent dataset (37 COVID‐19 and 93 CAP cases) for external test. Area under the receiver operating characteristic curve (AUC), sensitivity, specificity, precision, accuracy, F1 score, and G‐mean were utilized for performance evaluation. Results In the external test cohort, the relatively large data‐based 3DMTM‐LD achieved an AUC of 0.956 (95% confidence interval, 95% CI, 0.929∼0.982) with 86.2% and 98.0% for its sensitivity and specificity. 3DMTM‐SD got an AUC of 0.937 (95% CI, 0.909∼0.965), while the AUC of 3DCM‐SD decreased dramatically to 0.714 (95% CI, 0.649∼0.780) with training data reduction. KNN‐MMSD, LR‐MMSD, SVM‐MMSD, and 3DCM‐MMSD benefited significantly from the inclusion of clinical information while models trained with relatively large dataset got slight performance improvement in COVID‐19 discrimination. 3DMTM, trained with either CT or multi‐modal data, presented comparably excellent performance in COVID‐19 discrimination. Conclusions The 3DMTM algorithm presented excellent robustness for COVID‐19 discrimination with limited CT data. 3DMTM based on CT data performed comparably in COVID‐19 discrimination with that trained with multi‐modal information. Clinical information could improve the performance of KNN, LR, SVM, and 3DCM in COVID‐19 discrimination, especially in the scenario with limited data for training.

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A deep learning integrated radiomics model for identification of coronavirus disease 2019 using computed tomography

Cited by 26 publications

References 37 publications

Medical imaging of pulmonary disease in SARS-CoV-2-exposed non-human primates

Medical imaging of pulmonary disease in SARS-CoV-2-exposed non-human primates

Machine learning-based CT radiomics model distinguishes COVID-19 from non-COVID-19 pneumonia

An original deep learning model using limited data for COVID‐19 discrimination: A multicenter study

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