tetano
Editor, Senior Moderator
Br J Radiol
. 2021 Apr 21;20201007.
doi: 10.1259/bjr.20201007. Online ahead of print.
CT-based radiomics for predicting the rapid progression of coronavirus disease 2019 (COVID-19) pneumonia lesions
Bin Zhang[SUP] 1 [/SUP], Ma-Yi-di-Li Ni-Jia-Ti[SUP] 2 [/SUP], Ruike Yan[SUP] 1 [/SUP], Nan An[SUP] 3 [/SUP], Lv Chen[SUP] 1 [/SUP], Shuyi Liu[SUP] 1 [/SUP], Luyan Chen[SUP] 1 [/SUP], Qiuying Chen[SUP] 1 [/SUP], Minmin Li[SUP] 1 [/SUP], Zhuozhi Chen[SUP] 1 [/SUP], Jingjing You[SUP] 1 [/SUP], Yuhao Dong[SUP] 4 [/SUP], Zhiyuan Xiong[SUP] 5 6 [/SUP], Shuixing Zhang[SUP] 1 [/SUP]
Affiliations
Abstract
Objectives: To develop and validate a radiomic model to predict the rapid progression (defined as volume growth of pneumonia lesions > 50% within seven days) in patients with coronavirus disease 2019 (COVID-19).
Methods: Patients with laboratory-confirmed COVID-19 who underwent longitudinal chest CT between January 01 and February 18, 2020 were included. A total of 1316 radiomic features were extracted from the lung parenchyma window for each CT. The least absolute shrinkage and selection operator (LASSO), Relief, Las Vegas Wrapper (LVW), L1-norm-Support Vector Machine (L1-norm-SVM), and recursive feature elimination (RFE) were applied to select the features that associated with rapid progression. Four machine learning classifiers were used for modeling, including Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), and Decision Tree (DT). Accordingly, 20 radiomic models were developed on the basis of 296 CT scans and validated in 74 CT scans. Model performance was determined by the receiver operating characteristic curve.
Results: A total of 107 patients (median age, 49.0 years, interquartile range, 35-54) were evaluated. The patients underwent a total of 370 chest CT scans with a median interval of 4 days (interquartile range, 3-5 days). The combination methods of L1-norm SVM and SVM with 17 radiomic features yielded the highest performance in predicting the likelihood of rapid progression of pneumonia lesions on next CT scan, with an AUC of 0.857 (95% CI: 0.766-0.947), sensitivity of 87.5%, and specificity of 70.7%.
Conclusions: Our radiomic model based on longitudinal chest CT data could predict the rapid progression of pneumonia lesions, which may facilitate the CT follow-up intervals and reduce the radiation.
Advances in knowledge: Radiomic features extracted from the current chest CT have potential in predicting the likelihood of rapid progression of pneumonia lesions on the next chest CT, which would improve clinical decision-making regarding timely treatment.
. 2021 Apr 21;20201007.
doi: 10.1259/bjr.20201007. Online ahead of print.
CT-based radiomics for predicting the rapid progression of coronavirus disease 2019 (COVID-19) pneumonia lesions
Bin Zhang[SUP] 1 [/SUP], Ma-Yi-di-Li Ni-Jia-Ti[SUP] 2 [/SUP], Ruike Yan[SUP] 1 [/SUP], Nan An[SUP] 3 [/SUP], Lv Chen[SUP] 1 [/SUP], Shuyi Liu[SUP] 1 [/SUP], Luyan Chen[SUP] 1 [/SUP], Qiuying Chen[SUP] 1 [/SUP], Minmin Li[SUP] 1 [/SUP], Zhuozhi Chen[SUP] 1 [/SUP], Jingjing You[SUP] 1 [/SUP], Yuhao Dong[SUP] 4 [/SUP], Zhiyuan Xiong[SUP] 5 6 [/SUP], Shuixing Zhang[SUP] 1 [/SUP]
Affiliations
- PMID: 33881930
- DOI: 10.1259/bjr.20201007
Abstract
Objectives: To develop and validate a radiomic model to predict the rapid progression (defined as volume growth of pneumonia lesions > 50% within seven days) in patients with coronavirus disease 2019 (COVID-19).
Methods: Patients with laboratory-confirmed COVID-19 who underwent longitudinal chest CT between January 01 and February 18, 2020 were included. A total of 1316 radiomic features were extracted from the lung parenchyma window for each CT. The least absolute shrinkage and selection operator (LASSO), Relief, Las Vegas Wrapper (LVW), L1-norm-Support Vector Machine (L1-norm-SVM), and recursive feature elimination (RFE) were applied to select the features that associated with rapid progression. Four machine learning classifiers were used for modeling, including Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), and Decision Tree (DT). Accordingly, 20 radiomic models were developed on the basis of 296 CT scans and validated in 74 CT scans. Model performance was determined by the receiver operating characteristic curve.
Results: A total of 107 patients (median age, 49.0 years, interquartile range, 35-54) were evaluated. The patients underwent a total of 370 chest CT scans with a median interval of 4 days (interquartile range, 3-5 days). The combination methods of L1-norm SVM and SVM with 17 radiomic features yielded the highest performance in predicting the likelihood of rapid progression of pneumonia lesions on next CT scan, with an AUC of 0.857 (95% CI: 0.766-0.947), sensitivity of 87.5%, and specificity of 70.7%.
Conclusions: Our radiomic model based on longitudinal chest CT data could predict the rapid progression of pneumonia lesions, which may facilitate the CT follow-up intervals and reduce the radiation.
Advances in knowledge: Radiomic features extracted from the current chest CT have potential in predicting the likelihood of rapid progression of pneumonia lesions on the next chest CT, which would improve clinical decision-making regarding timely treatment.