Erosion of the temporal bone by vestibular schwannoma: morphometrics and predictive modeling.

Details

Serval ID
serval:BIB_C04FDCE27C69
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Erosion of the temporal bone by vestibular schwannoma: morphometrics and predictive modeling.
Journal
European archives of oto-rhino-laryngology
Author(s)
Massager N., El Hadwe S., Barrit S., Al Barajraji M., Morelli D., Renier C.
ISSN
1434-4726 (Electronic)
ISSN-L
0937-4477
Publication state
In Press
Peer-reviewed
Oui
Language
english
Notes
Publication types: Journal Article
Publication Status: aheadofprint
Abstract
To perform a comprehensive morphometric analysis of vestibular schwannomas (VS) using multimodal imaging, focusing on the relationship between tumor characteristics and internal acoustic canal (IAC) changes.
We analyzed a cohort of patients undergoing radiosurgery for VS, utilizing high-definition MRI and bone CT for detailed anatomical assessment. Image co-registration and fusion techniques were employed to examine VS and IAC dimensions. Advanced statistical methods, including logistic regression, were applied to identify patterns of IAC dilation and establish predictive indicators for these morphological changes.
The study included 659 patients (51.1% female, mean age 56.37 years) with evenly distributed tumor lateralization. Koos grades were I (22.9%), II (29.9%), III (38.2%), IVa (8.9%), and IVb (0.3%). Most tumors (90.9%) extended both inside and outside the IAC. Ipsilateral IAC (IIAC) dimensions were significantly larger than contralateral, with IIAC volume 49% greater (p < .0001). Higher Koos grades correlated with increased intra-canalicular lesion volume (icLV), which was strongly associated with IIAC size. Logistic regression identified icLV as the strongest predictor of IIAC dilation (AUC = 0.7674, threshold = 137.52 mm3).
The icLV appears central to the pathophysiological development of VS and its impact on IAC anatomy. While limited by a selective patient base and static imaging data, these findings enhance the understanding of VS-IAC interactions, offering insights for improved clinical management and further research.
Keywords
Artificial intelligence, Bone erosion, Internal auditory canal, Machine learning, Morphometrics, Vestibular schwannoma
Pubmed
Web of science
Create date
28/10/2024 14:19
Last modification date
31/10/2024 7:13
Usage data