tetano
Editor, Senior Moderator
BMC Med Imaging
. 2020 Oct 20;20(1):118.
doi: 10.1186/s12880-020-00521-z.
A model based on CT radiomic features for predicting RT-PCR becoming negative in coronavirus disease 2019 (COVID-19) patients
Quan Cai[SUP] 1 [/SUP], Si-Yao Du[SUP] 2 [/SUP], Si Gao[SUP] 2 [/SUP], Guo-Liang Huang[SUP] 2 [/SUP], Zheng Zhang[SUP] 2 [/SUP], Shu Li[SUP] 2 [/SUP], Xin Wang[SUP] 2 [/SUP], Pei-Ling Li[SUP] 2 [/SUP], Peng Lv[SUP] 2 [/SUP], Gang Hou[SUP] 3 [/SUP], Li-Na Zhang[SUP] 4 [/SUP]
Affiliations
Abstract
Background: Coronavirus disease 2019 (COVID-19) has emerged as a global pandemic. According to the diagnosis and treatment guidelines of China, negative reverse transcription-polymerase chain reaction (RT-PCR) is the key criterion for discharging COVID-19 patients. However, repeated RT-PCR tests lead to medical waste and prolonged hospital stays for COVID-19 patients during the recovery period. Our purpose is to assess a model based on chest computed tomography (CT) radiomic features and clinical characteristics to predict RT-PCR negativity during clinical treatment.
Methods: From February 10 to March 10, 2020, 203 mild COVID-19 patients in Fangcang Shelter Hospital were retrospectively included (training: n = 141; testing: n = 62), and clinical characteristics were collected. Lung abnormalities on chest CT images were segmented with a deep learning algorithm. CT quantitative features and radiomic features were automatically extracted. Clinical characteristics and CT quantitative features were compared between RT-PCR-negative and RT-PCR-positive groups. Univariate logistic regression and Spearman correlation analyses identified the strongest features associated with RT-PCR negativity, and a multivariate logistic regression model was established. The diagnostic performance was evaluated for both cohorts.
Results: The RT-PCR-negative group had a longer time interval from symptom onset to CT exams than the RT-PCR-positive group (median 23 vs. 16 days, p < 0.001). There was no significant difference in the other clinical characteristics or CT quantitative features. In addition to the time interval from symptom onset to CT exams, nine CT radiomic features were selected for the model. ROC curve analysis revealed AUCs of 0.811 and 0.812 for differentiating the RT-PCR-negative group, with sensitivity/specificity of 0.765/0.625 and 0.784/0.600 in the training and testing datasets, respectively.
Conclusion: The model combining CT radiomic features and clinical data helped predict RT-PCR negativity during clinical treatment, indicating the proper time for RT-PCR retesting.
Keywords: COVID-19; Computed tomography; Quantitative; RT-PCR; Radiomics.
. 2020 Oct 20;20(1):118.
doi: 10.1186/s12880-020-00521-z.
A model based on CT radiomic features for predicting RT-PCR becoming negative in coronavirus disease 2019 (COVID-19) patients
Quan Cai[SUP] 1 [/SUP], Si-Yao Du[SUP] 2 [/SUP], Si Gao[SUP] 2 [/SUP], Guo-Liang Huang[SUP] 2 [/SUP], Zheng Zhang[SUP] 2 [/SUP], Shu Li[SUP] 2 [/SUP], Xin Wang[SUP] 2 [/SUP], Pei-Ling Li[SUP] 2 [/SUP], Peng Lv[SUP] 2 [/SUP], Gang Hou[SUP] 3 [/SUP], Li-Na Zhang[SUP] 4 [/SUP]
Affiliations
- PMID: 33081700
- DOI: 10.1186/s12880-020-00521-z
Abstract
Background: Coronavirus disease 2019 (COVID-19) has emerged as a global pandemic. According to the diagnosis and treatment guidelines of China, negative reverse transcription-polymerase chain reaction (RT-PCR) is the key criterion for discharging COVID-19 patients. However, repeated RT-PCR tests lead to medical waste and prolonged hospital stays for COVID-19 patients during the recovery period. Our purpose is to assess a model based on chest computed tomography (CT) radiomic features and clinical characteristics to predict RT-PCR negativity during clinical treatment.
Methods: From February 10 to March 10, 2020, 203 mild COVID-19 patients in Fangcang Shelter Hospital were retrospectively included (training: n = 141; testing: n = 62), and clinical characteristics were collected. Lung abnormalities on chest CT images were segmented with a deep learning algorithm. CT quantitative features and radiomic features were automatically extracted. Clinical characteristics and CT quantitative features were compared between RT-PCR-negative and RT-PCR-positive groups. Univariate logistic regression and Spearman correlation analyses identified the strongest features associated with RT-PCR negativity, and a multivariate logistic regression model was established. The diagnostic performance was evaluated for both cohorts.
Results: The RT-PCR-negative group had a longer time interval from symptom onset to CT exams than the RT-PCR-positive group (median 23 vs. 16 days, p < 0.001). There was no significant difference in the other clinical characteristics or CT quantitative features. In addition to the time interval from symptom onset to CT exams, nine CT radiomic features were selected for the model. ROC curve analysis revealed AUCs of 0.811 and 0.812 for differentiating the RT-PCR-negative group, with sensitivity/specificity of 0.765/0.625 and 0.784/0.600 in the training and testing datasets, respectively.
Conclusion: The model combining CT radiomic features and clinical data helped predict RT-PCR negativity during clinical treatment, indicating the proper time for RT-PCR retesting.
Keywords: COVID-19; Computed tomography; Quantitative; RT-PCR; Radiomics.