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Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
Published on: December 19, 2020
Multicenter study on CT-based Radiomics for predicting severity and delayed recovery in Mycoplasma pneumoniae
Qian Li1, Zi-Jun Song1, Wenjing Chen2
1Department of Critical Care Medicine, Baoding First Central Hospital, Baoding, China.
Objective:
To develop and validate model based on clinical, imaging, and Radiomics features for predicting disease severity and delayed recovery in Mycoplasma pneumoniae pneumonia (MPP).
Methods:
This multicenter retrospective study enrolled 238 patients (training cohort), 60 (testing cohort), and 278 (validation cohort). Patients were classified into non-severe MPP (NSMPP) and severe MPP (SMPP) groups based on guideline, and further stratified post-treatment into recovery or delayed recovery groups. Radiomics features were extracted from chest CT using PyRadiomics, with Least Absolute Shrinkage and Selection Operator (LASSO) regression for feature selection. Three random forest-based predictive models were developed, including Clinical-Image, Radiomics, and Integrated. Predictive performance was evaluated via by the area under the receiver operating characteristic curve (AUC), calibration, and clinical utility.
Results:
The Integrated model demonstrated superior discrimination for severity prediction (validation AUC: 0.784, 95% CI: 0.722-0.845) and delayed recovery (validation AUC: 0.865, 95% CI: 0.770-0.960), outperforming Clinical-Image (severity AUC: 0.771, 95% CI: 0.695-0.847; delayed recovery AUC: 0.807, 95% CI: 0.724-0.950) and Radiomics model (severity AUC: 0.710, 95% CI: 0.643-0.776; delayed recovery AUC: 0.837, 95% CI: 0.724-0.950). Integrated Discrimination Improvement (IDI) analysis demonstrated significant enhancements in the Integrated model compared to both the Clinical-Image and Radiomics models for predicting both disease severity and delayed recovery (all p < 0.05). Key predictors comprised D-dimer (severity OR = 1.371; delayed recovery OR = 4.061), systemic immune-inflammation index (delayed recovery OR = 6.607), and consolidation patterns (delayed recovery OR = 2.820).
Conclusion:
The Integrated model combining clinical, imaging, and Radiomics features enhances risk stratification for MPP severity and delayed recovery.
Insights
An integrated model combining clinical, imaging, and radiomics features accurately predicts Mycoplasma pneumoniae pneumonia (MPP) severity and delayed recovery, improving patient risk stratification.
Area of Science:
- Medical Imaging
- Pulmonology
- Artificial Intelligence in Medicine
Background:
- Mycoplasma pneumoniae pneumonia (MPP) poses a significant challenge in clinical practice, with disease severity and recovery time varying widely among patients.
- Accurate prediction of MPP severity and delayed recovery is crucial for timely and effective patient management.
Purpose of the Study:
- To develop and validate a predictive model integrating clinical, imaging, and radiomics features for Mycoplasma pneumoniae pneumonia (MPP).
- To assess the model's ability to predict disease severity and delayed recovery in patients with MPP.
Main Methods:
- A multicenter retrospective study involving 238 patients in the training cohort, 60 in the testing cohort, and 278 in the validation cohort.
- Radiomics features were extracted from chest CT scans, and Least Absolute Shrinkage and Selection Operator (LASSO) regression was used for feature selection.
- Three random forest-based models (Clinical-Image, Radiomics, and Integrated) were developed and evaluated using AUC, calibration, and clinical utility metrics.
Main Results:
- The Integrated model demonstrated superior performance in predicting both severity (validation AUC: 0.784) and delayed recovery (validation AUC: 0.865) compared to the Clinical-Image and Radiomics-only models.
- Integrated Discrimination Improvement (IDI) analysis confirmed significant enhancements with the Integrated model (p < 0.05).
- Key predictors identified included D-dimer, systemic immune-inflammation index, and consolidation patterns.
Conclusions:
- An integrated model combining clinical, imaging, and radiomics data offers enhanced risk stratification for MPP.
- This approach improves the prediction of disease severity and delayed recovery in patients with Mycoplasma pneumoniae pneumonia.
- The findings support the clinical utility of multimodal data integration for optimizing MPP patient management.
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