Research Paper Volume 13, Issue 9 pp 13195—13210
Morphology-based radiomics signature: a novel determinant to identify multiple intracranial aneurysms rupture
- 1 Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- 2 Department of Interventional Neuroradiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- 3 Department of Neurosurgery, Beijing Hospital, National Center of Gerontology, Graduate School of Peking Union Medical College, Beijing, China
- 4 Department of Neurosurgery, Peking University International Hospital, Beijing, China
Received: August 3, 2020 Accepted: November 27, 2020 Published: May 10, 2021https://doi.org/10.18632/aging.203001
How to Cite
Copyright: © 2021 Tong 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 aimed to develop and validate a morphology-based radiomics signature nomogram for assessing the risk of intracranial aneurysm (IA) rupture. A total of 254 aneurysms in 105 patients with subarachnoid hemorrhage and multiple intracranial aneurysms from three centers were retrospectively reviewed and randomly divided into the derivation and validation cohorts. Radiomics morphological features were automatically extracted from digital subtraction angiography and selected by the least absolute shrinkage and selection operator algorithm to develop a radiomics signature. A radiomics signature-based nomogram was developed by incorporating the signature and traditional morphological features. The performance of calibration, discrimination, and clinical usefulness of the nomogram was assessed. Ten radiomics morphological features were selected to build the radiomics signature model, which showed better discrimination with an area under the curve (AUC) equal to 0.814 and 0.835 in the derivation and validation cohorts compared with 0.747 and 0.666 in the traditional model, which only include traditional morphological features. When radiomics signature and traditional morphological features were combined, the AUC increased to 0.842 and 0.849 in the derivation and validation cohorts, thus showing better performance in assessing aneurysm rupture risk. This novel model could be useful for decision-making and risk stratification for patients with IAs.