Yilong Huang1, Zhenguang Zhang1, Siyun Liu2, Xiang Li3, Yunhui Yang4, Jiyao Ma1, Zhipeng Li5, Jialong Zhou6, Yuanming Jiang1, Bo He7. 1. Medical Imaging Department, First Affiliated Hospital of Kunming Medical University, Kunming, 650000, China. 2. Precision Health Institution, PDx, GE Healthcare (China), Beijing, 100176, China. 3. Department of Radiology, The 3rd Peoples' Hospital of Kunming, Kunming, 650000, China. 4. Department of Medical Imaging, People's Hospital of Xishuangbanna Dai Autonomous Prefecture, Xishuangbanna, 666100, China. 5. Medical Imaging Department, Yunnan Provincial Infectious Disease Hospital, Kunming, 650000, China. 6. MRI Department, The First People's Hospital of Yunnan Province, Kunming, 650000, China. 7. Medical Imaging Department, First Affiliated Hospital of Kunming Medical University, Kunming, 650000, China. hebo_ydyy@qq.com.
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.
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.
Authors: You Li; Rachel M Reeves; Xin Wang; Quique Bassat; W Abdullah Brooks; Cheryl Cohen; David P Moore; Marta Nunes; Barbara Rath; Harry Campbell; Harish Nair Journal: Lancet Glob Health Date: 2019-08 Impact factor: 26.763
Authors: Ashley Fowlkes; Andrea Steffens; Jon Temte; Steve Di Lonardo; Lisa McHugh; Karen Martin; Heather Rubino; Michelle Feist; Carol Davis; Christine Selzer; Jose Lojo; Oluwakemi Oni; Katie Kurkjian; Ann Thomas; Rachelle Boulton; Nicole Bryan; Ruth Lynfield; Matthew Biggerstaff; Lyn Finelli Journal: Lancet Respir Med Date: 2015-08-21 Impact factor: 30.700
Authors: Tim Fischer; Yassir El Baz; Giulia Scanferla; Nicole Graf; Frederike Waldeck; Gian-Reto Kleger; Thomas Frauenfelder; Jens Bremerich; Sabine Schmidt Kobbe; Jean-Luc Pagani; Sebastian Schindera; Anna Conen; Simon Wildermuth; Sebastian Leschka; Carol Strahm; Stephan Waelti; Tobias Johannes Dietrich; Werner C Albrich Journal: Eur J Radiol Open Date: 2022-06-24