Research Paper Volume 13, Issue 9 pp 12833—12848
Predicting intraventricular hemorrhage growth with a machine learning-based, radiomics-clinical model
- 1 Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
- 2 Department of Radiology, Lab-Yang, University of Washington, Seattle, WA 98109, USA
- 3 Department of Neurosurgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
Received: November 28, 2020 Accepted: February 17, 2021 Published: May 4, 2021https://doi.org/10.18632/aging.202954
How to Cite
Copyright: © 2021 Zhu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
We constructed a radiomics-clinical model to predict intraventricular hemorrhage (IVH) growth after spontaneous intracerebral hematoma. The model was developed using a training cohort (N=626) and validated with an independent testing cohort (N=270). Radiomics features and clinical predictors were selected using the least absolute shrinkage and selection operator (LASSO) method and multivariate analysis. The radiomics score (Rad-score) was calculated through linear combination of selected features multiplied by their respective LASSO coefficients. The support vector machine (SVM) method was used to construct the model. IVH growth was experienced by 13.4% and 13.7% of patients in the training and testing cohorts, respectively. The Rad-score was associated with severe IVH and poor outcome. Independent predictors of IVH growth included hypercholesterolemia (odds ratio [OR], 0.12 [95%CI, 0.02-0.90]; p=0.039), baseline Graeb score (OR, 1.26 [95%CI, 1.16-1.36]; p<0.001), time to initial CT (OR, 0.70 [95%CI, 0.58-0.86]; p<0.001), international normalized ratio (OR, 4.27 [95%CI, 1.40, 13.0]; p=0.011), and Rad-score (OR, 2.3 [95%CI, 1.6-3.3]; p<0.001). In the training cohort, the model achieved an AUC of 0.78, sensitivity of 0.83, and specificity of 0.66. In the testing cohort, AUC, sensitivity, and specificity were 0.71, 0.81, and 0.64, respectively. This radiomics-clinical model thus has the potential to predict IVH growth.
AUC: area under the receiver operating curve; GOS: Glasgow Outcome Scale; ICC: inter-class correlation coefficient; ICH: intracerebral hematoma; INR: international normalized ratio; IVH: Intraventricular hemorrhage; LASSO: least absolute shrinkage and selection operator; OR: odds ratio; ROC: receiver-operator curve; SVM: support vector machine.