tetano
Editor, Senior Moderator
BMC Infect Dis
. 2021 Sep 8;21(1):931.
doi: 10.1186/s12879-021-06614-6.
Machine learning-based CT radiomics model distinguishes COVID-19 from non-COVID-19 pneumonia
Hui Juan Chen[SUP] 1 [/SUP], Li Mao[SUP] 2 [/SUP], Yang Chen[SUP] 1 [/SUP], Li Yuan[SUP] 1 [/SUP], Fei Wang[SUP] 1 [/SUP], Xiuli Li[SUP] 2 [/SUP], Qinlei Cai[SUP] 1 [/SUP], Jie Qiu[SUP] 3 [/SUP], Feng Chen[SUP] 4 [/SUP]
Affiliations
Abstract
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.
Keywords: Coronavirus Disease 2019 (COVID-19); Machine learning; Non-COVID-19 pneumonia; Radiomics.
. 2021 Sep 8;21(1):931.
doi: 10.1186/s12879-021-06614-6.
Machine learning-based CT radiomics model distinguishes COVID-19 from non-COVID-19 pneumonia
Hui Juan Chen[SUP] 1 [/SUP], Li Mao[SUP] 2 [/SUP], Yang Chen[SUP] 1 [/SUP], Li Yuan[SUP] 1 [/SUP], Fei Wang[SUP] 1 [/SUP], Xiuli Li[SUP] 2 [/SUP], Qinlei Cai[SUP] 1 [/SUP], Jie Qiu[SUP] 3 [/SUP], Feng Chen[SUP] 4 [/SUP]
Affiliations
- PMID: 34496794
- DOI: 10.1186/s12879-021-06614-6
Abstract
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.
Keywords: Coronavirus Disease 2019 (COVID-19); Machine learning; Non-COVID-19 pneumonia; Radiomics.