A model based on CT radiomic features for predicting RT-PCR becoming negative in coronavirus disease 2019 (COVID-19) patients

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.

COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: Analysis of a multi-institutional dataset of 14,339 patients

“…Another study by Li et al [ 32 ] analyzed a radiomics/DL model that distinguished severe from critical COVID-19 pneumonia patients. Cai et al [ 33 ] developed a model combining CT radiomic features and clinical data to predict RT-PCR negativity during admission. Yue et al [ 34 ] conducted a multicentric radiomics study on 52 patients to differentiate whether an individual needs a short-term or long-term hospital stay.…”

Toward the determination of sensitive and reliable whole-lung computed tomography features for robust standard radiomics and delta-radiomics analysis in a nonhuman primate model of coronavirus disease 2019

“…Cai et al 27 . proposed a model based on CT radiomic features that could predict a negative reverse transcription quantitative polymerase chain reaction (RT-qPCR) test for SARS-CoV-2 and could be used to recommend early patient discharge from hospitals.…”

Prediction of COVID‐19 patients in danger of death using radiomic features of portable chest radiographs

“…14 On the other hand, radiomic research includes predictive studies related to the severity and prognosis of COVID-19, [15][16][17][18][19][20][21] the need for oxygenation support and intubation 22 and the criteria for discharge. 23,24 In previous radiomic studies of COVID-19, computed tomography (CT) images were used because of the ease of analysis, while chest radiographs have never been used. However, preventing infection during entering and exiting the CT room imposes a heavy burden on the medical staff.…”

“…CAD research includes studies related to the detection of COVID‐19 pneumonia, 5 the differentiation of COVID‐19 pneumonia from other types of pneumonia, 6,7 the concurrent detection and differential diagnosis of COVID‐19 pneumonia 8–13 and the differential diagnosis of severe respiratory failure 14 . On the other hand, radiomic research includes predictive studies related to the severity and prognosis of COVID‐19, 15–21 the need for oxygenation support and intubation 22 and the criteria for discharge 23,24 …”