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Published on: April 13, 2013
A Nomogram Based on CT Radiomics and Clinical Risk Factors for Prediction of Prognosis of Hypertensive Intracerebral
Caiyun Fang1,2, Xiao An2, Kejian Li1,2
1Department of Radiology, The First Affiliated Hospital of Shandong First Medical University, Shandong Provincial Qianfoshan Hospital, Jinan, Shandong 250014, China.
Insights
This study developed a clinical-radiomics nomogram using CT imaging features and patient data to predict the prognosis of hypertensive intracerebral hemorrhage (HICH). The nomogram demonstrated high accuracy and clinical applicability in predicting HICH outcomes.
Area of Science:
- Radiology
- Medical Informatics
- Neurology
Background:
- Hypertensive intracerebral hemorrhage (HICH) is a severe neurological condition with significant mortality and morbidity.
- Accurate prognosis prediction is crucial for guiding clinical management and improving patient outcomes.
- Current prognostic models may not fully capture the complexity of HICH progression.
Purpose of the Study:
- To develop and validate a predictive model for HICH prognosis.
- To integrate clinical risk factors with radiomics features from CT scans.
- To create a clinical-radiomics nomogram for enhanced prognostic accuracy.
Main Methods:
- Retrospective analysis of 195 HICH patients, divided into training (n=138) and validation (n=57) cohorts.
- Extraction of 1702 CT radiomics features from intrahematomal and perihematomal regions using 3D Slicer.
- Selection of optimal features using the least absolute shrinkage and selection operator (LASSO) method to calculate a radiomics score (Rad-score).
- Development of a clinical-radiomics nomogram through logistic regression analysis incorporating Rad-score and clinical factors.
- Evaluation of nomogram performance using Area Under the Curve (AUC) and Decision Curve Analysis (DCA).
Main Results:
- Key clinical risk factors identified: age, sex, RBC, serum glucose, D-dimer, hematoma volume, and midline shift.
- The clinical-radiomics nomogram demonstrated high predictive efficiency in the training cohort (AUC=0.95) and validation cohort (AUC=0.90).
- The nomogram exhibited good calibration and high applicability in clinical practice, as confirmed by DCA.
Conclusions:
- The developed clinical-radiomics nomogram effectively integrates radiomics features and clinical risk factors.
- This nomogram shows significant potential for accurately predicting the prognosis of hypertensive intracerebral hemorrhage.
- The model offers a promising tool for improving clinical decision-making in HICH management.
Purpose:
To develop and validate a clinical-radiomics nomogram based on clinical risk factors and CT radiomics feature to predict hypertensive intracerebral hemorrhage (HICH) prognosis.
Methods:
A total of 195 patients with HICH treated in our hospital from January 2018 to January 2022 were retrospectively enrolled and randomly divided into two cohorts for training (n = 138) and validation (n = 57) according to the ratio of 7 : 3. All CT radiomics features were extracted from intrahematomal, perihematomal, and combined intra- and perihematomal regions by using free open-source software called 3D slicer. The least absolute shrinkage and selection operator method was used to select the optimal radiomics features, and the radiomics score (Rad-score) was calculated. The relationship between Rad-score, clinical risk factors, and the HICH prognosis was analyzed by univariate and multivariate logistic regression analyses, and the clinical-radiomics nomogram was built. The area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA) were used to evaluate the performance of the clinical-radiomics nomogram in predicting the prognosis of HICH.
Results:
A total of 1702 radiomics features were extracted from the CT images of each patient for analysis. By univariate and stepwise multivariate logistic regression analyses, age, sex, RBC, serum glucose, D-dimer level, hematoma volume, and midline shift were clinical risk factors for the prognosis of HICH. Rad-score and clinical risk factors developed the clinical-radiomics nomogram. The nomogram showed the highest predictive efficiency in the training cohort (AUC = 0.95, 95% confidence interval (CI), 0.92 to 0.98) and the validation cohort (AUC = 0.90, 95% CI, 0.82 to 0.98). The calibration curve indicated that the clinical-radiomics nomogram had good calibration. DCA showed that the nomogram had high applicability in clinical practice.
Conclusions:
The clinical-radiomics nomogram incorporated with the radiomics features and clinical risk factors has good potential in predicting the prognosis of HICH.
