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CT-based radiomics combined with signs: a valuable tool to help radiologist discriminate COVID-19 and influenza pneumonia.
Huang, Yilong; Zhang, Zhenguang; Liu, Siyun; Li, Xiang; Yang, Yunhui; Ma, Jiyao; Li, Zhipeng; Zhou, Jialong; Jiang, Yuanming; He, Bo.
  • Huang Y; Medical Imaging Department, First Affiliated Hospital of Kunming Medical University, Kunming, 650000, China.
  • Zhang Z; Medical Imaging Department, First Affiliated Hospital of Kunming Medical University, Kunming, 650000, China.
  • Liu S; Precision Health Institution, PDx, GE Healthcare (China), Beijing, 100176, China.
  • Li X; Department of Radiology, The 3rd Peoples' Hospital of Kunming, Kunming, 650000, China.
  • Yang Y; Department of Medical Imaging, People's Hospital of Xishuangbanna Dai Autonomous Prefecture, Xishuangbanna, 666100, China.
  • Ma J; Medical Imaging Department, First Affiliated Hospital of Kunming Medical University, Kunming, 650000, China.
  • Li Z; Medical Imaging Department, Yunnan Provincial Infectious Disease Hospital, Kunming, 650000, China.
  • Zhou J; MRI Department, The First People's Hospital of Yunnan Province, Kunming, 650000, China.
  • Jiang Y; Medical Imaging Department, First Affiliated Hospital of Kunming Medical University, Kunming, 650000, China.
  • He B; Medical Imaging Department, First Affiliated Hospital of Kunming Medical University, Kunming, 650000, China. hebo_ydyy@qq.com.
BMC Med Imaging ; 21(1): 31, 2021 02 17.
Article in English | MEDLINE | ID: covidwho-1088584
ABSTRACT

BACKGROUND:

In this COVID-19 pandemic, the differential diagnosis of viral pneumonia is still challenging. We aimed to assess the classification performance of computed tomography (CT)-based CT signs and radiomics features for discriminating COVID-19 and influenza pneumonia.

METHODS:

A total of 154 patients with confirmed viral pneumonia (COVID-19 89 cases, influenza pneumonia 65 cases) were collected retrospectively in this study. Pneumonia signs and radiomics features were extracted from the initial unenhanced chest CT images to build independent and combined models. The predictive performance of the radiomics model, CT sign model, the combined model was constructed based on the whole dataset and internally invalidated by using 1000-times bootstrap. Diagnostic performance of the models was assessed via receiver operating characteristic (ROC) analysis.

RESULTS:

The combined models consisted of 4 significant CT signs and 7 selected features and demonstrated better discrimination performance between COVID-19 and influenza pneumonia than the single radiomics model. For the radiomics model, the area under the ROC curve (AUC) was 0.888 (sensitivity, 86.5%; specificity, 78.4%; accuracy, 83.1%), and the AUC was 0.906 (sensitivity, 86.5%; specificity, 81.5%; accuracy, 84.4%) in the CT signs model. After combining CT signs and radiomics features, AUC of the combined model was 0.959 (sensitivity, 89.9%; specificity, 90.7%; accuracy, 90.3%).

CONCLUSIONS:

CT-based radiomics combined with signs might be a potential method for distinguishing COVID-19 and influenza pneumonia with satisfactory performance.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Pneumonia, Viral / Radiographic Image Interpretation, Computer-Assisted / Tomography, X-Ray Computed / Influenza, Human / COVID-19 Type of study: Diagnostic study / Observational study / Prognostic study Limits: Adult / Female / Humans / Male / Middle aged Language: English Journal: BMC Med Imaging Journal subject: Diagnostic Imaging Year: 2021 Document Type: Article Affiliation country: S12880-021-00564-w

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Pneumonia, Viral / Radiographic Image Interpretation, Computer-Assisted / Tomography, X-Ray Computed / Influenza, Human / COVID-19 Type of study: Diagnostic study / Observational study / Prognostic study Limits: Adult / Female / Humans / Male / Middle aged Language: English Journal: BMC Med Imaging Journal subject: Diagnostic Imaging Year: 2021 Document Type: Article Affiliation country: S12880-021-00564-w