tetano
Editor, Senior Moderator
BMC Infect Dis
. 2024 Jun 17;24(1):595.
doi: 10.1186/s12879-024-09504-9. Prediction of short-term progression of COVID-19 pneumonia based on chest CT artificial intelligence: during the Omicron epidemic
Xinjing Lou[SUP] #[/SUP][SUP] 1 2 [/SUP], Chen Gao[SUP] #[/SUP][SUP] 1 2 [/SUP], Linyu Wu[SUP] 1 2 [/SUP], Ting Wu[SUP] 1 2 [/SUP], Linyang He[SUP] 3 [/SUP], Jiahao Shen[SUP] 1 2 [/SUP], Meiqi Hua[SUP] 1 2 [/SUP], Maosheng Xu[SUP] 4 5 [/SUP]
Affiliations
Background and purpose: The persistent progression of pneumonia is a critical determinant of adverse outcomes in patients afflicted with COVID-19. This study aimed to predict personalized COVID-19 pneumonia progression between the duration of two weeks and 1 month after admission by integrating radiological and clinical features.
Methods: A retrospective analysis, approved by the Institutional Review Board, encompassed patients diagnosed with COVID-19 pneumonia between December 2022 and February 2023. The cohort was divided into training and validation groups in a 7:3 ratio. A trained multi-task U-Net network was deployed to segment COVID-19 pneumonia and lung regions in CT images, from which quantitative features were extracted. The eXtreme Gradient Boosting (XGBoost) algorithm was employed to construct a radiological model. A clinical model was constructed by LASSO method and stepwise regression analysis, followed by the subsequent construction of the combined model. Model performance was assessed using ROC and decision curve analysis (DCA), while Shapley's Additive interpretation (SHAP) illustrated the importance of CT features.
Results: A total of 214 patients were recruited in our study. Four clinical characteristics and four CT features were identified as pivotal components for constructing the clinical and radiological models. The final four clinical characteristics were incorporated as well as the RS_radiological model to construct the combined prediction model. SHAP analysis revealed that CT score difference exerted the most significant influence on the predictive performance of the radiological model. The training group's radiological, clinical, and combined models exhibited AUC values of 0.89, 0.72, and 0.92, respectively. Correspondingly, in the validation group, these values were observed to be 0.75, 0.72, and 0.81. The DCA curve showed that the combined model exhibited greater clinical utility than the clinical or radiological models.
Conclusion: Our novel combined model, fusing quantitative CT features with clinical characteristics, demonstrated effective prediction of COVID-19 pneumonia progression from 2 weeks to 1 month after admission. This comprehensive model can potentially serve as a valuable tool for clinicians to develop personalized treatment strategies and improve patient outcomes.
Keywords: COVID-19; Computed tomography; Prognosis analysis; Risk factors; SARS-CoV-2.
. 2024 Jun 17;24(1):595.
doi: 10.1186/s12879-024-09504-9. Prediction of short-term progression of COVID-19 pneumonia based on chest CT artificial intelligence: during the Omicron epidemic
Xinjing Lou[SUP] #[/SUP][SUP] 1 2 [/SUP], Chen Gao[SUP] #[/SUP][SUP] 1 2 [/SUP], Linyu Wu[SUP] 1 2 [/SUP], Ting Wu[SUP] 1 2 [/SUP], Linyang He[SUP] 3 [/SUP], Jiahao Shen[SUP] 1 2 [/SUP], Meiqi Hua[SUP] 1 2 [/SUP], Maosheng Xu[SUP] 4 5 [/SUP]
Affiliations
- PMID: 38886649
- DOI: 10.1186/s12879-024-09504-9
Background and purpose: The persistent progression of pneumonia is a critical determinant of adverse outcomes in patients afflicted with COVID-19. This study aimed to predict personalized COVID-19 pneumonia progression between the duration of two weeks and 1 month after admission by integrating radiological and clinical features.
Methods: A retrospective analysis, approved by the Institutional Review Board, encompassed patients diagnosed with COVID-19 pneumonia between December 2022 and February 2023. The cohort was divided into training and validation groups in a 7:3 ratio. A trained multi-task U-Net network was deployed to segment COVID-19 pneumonia and lung regions in CT images, from which quantitative features were extracted. The eXtreme Gradient Boosting (XGBoost) algorithm was employed to construct a radiological model. A clinical model was constructed by LASSO method and stepwise regression analysis, followed by the subsequent construction of the combined model. Model performance was assessed using ROC and decision curve analysis (DCA), while Shapley's Additive interpretation (SHAP) illustrated the importance of CT features.
Results: A total of 214 patients were recruited in our study. Four clinical characteristics and four CT features were identified as pivotal components for constructing the clinical and radiological models. The final four clinical characteristics were incorporated as well as the RS_radiological model to construct the combined prediction model. SHAP analysis revealed that CT score difference exerted the most significant influence on the predictive performance of the radiological model. The training group's radiological, clinical, and combined models exhibited AUC values of 0.89, 0.72, and 0.92, respectively. Correspondingly, in the validation group, these values were observed to be 0.75, 0.72, and 0.81. The DCA curve showed that the combined model exhibited greater clinical utility than the clinical or radiological models.
Conclusion: Our novel combined model, fusing quantitative CT features with clinical characteristics, demonstrated effective prediction of COVID-19 pneumonia progression from 2 weeks to 1 month after admission. This comprehensive model can potentially serve as a valuable tool for clinicians to develop personalized treatment strategies and improve patient outcomes.
Keywords: COVID-19; Computed tomography; Prognosis analysis; Risk factors; SARS-CoV-2.