Abstract
BackgroundCoronavirus 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.MethodsFrom 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.ResultsThe 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.ConclusionThe model combining CT radiomic features and clinical data helped predict RT-PCR negativity during clinical treatment, indicating the proper time for RT-PCR retesting.
Highlights
Coronavirus disease 2019 (COVID-19) has emerged as a global pandemic
For 203 patients included in our study, the average number of reverse transcription-polymerase chain reaction (RT-PCR) tests was 6 ± 3, ranging from 3 to 12 during hospitalization. 122/203 (60.1%) were categorized in the RT-PCRnegative group, and 81 (39.9%) were categorized in the RT-PCR-positive group
There was no significant difference in the other clinical characteristics
Summary
According to the diagnosis and treatment guidelines of China, negative reverse transcription-polymerase chain reaction (RT-PCR) is the key crite‐ rion for discharging COVID-19 patients. 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. According to the diagnosis and treatment guidelines proposed by the National Health Committee of the People’s Republic of China (7th Edition) [1], negative reverse transcription-polymerase chain reaction (RT-PCR) is the key criterion for. The clinical prediction of RT-PCR becoming negative is critical for the proper retesting time, preventing medical waste from repeated RT-PCR tests and unnecessary prolonged hospital stays. Based on a reliable segmentation method, the high-throughput and high-dimensional radiomic features on chest CT showed strong potential for predicting the true status of RT-PCR
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