Dynamic contrast-enhanced and diffusion-weighted MR imaging for predicting tumor growth of sporadic vestibular schwannomas: A prospective study
Year: 2024
Type: article
Study design: Observational Study
Abstract: BACKGROUND: Advanced MR imaging, such as diffusion-weighted (DWI) and dynamic contrast-enhanced (DCE) imaging, may provide valuable noninvasive information on intrinsic tumor biology. This study aims to evaluate apparent diffusion coefficient (ADC) and DCE-MRI-derived microvascular parameter values (Ktrans, ve, and vp) as potential imaging predictors for future sporadic vestibular schwannoma (VS) growth. METHODS: In this prospective cohort study, patients with newly diagnosed unilateral sporadic VS and an initial wait-and-scan strategy were enrolled between January 2021 and January 2023. Patients underwent a single timepoint comprehensive MRI protocol, including DWI and DCE-MRI sequences. The estimated values of ADC, Ktrans, ve, and vp were calculated using established pipelines on a voxelwise basis within the delineated tumor region of interest. Associations of the estimated parameter values with volumetric growth were evaluated in uni- and multivariable logistic regression and survival analyses. RESULTS: Of the 110 analyzed patients, 70 (64%) exhibited growth during follow-up. A significant correlation was primarily observed between the DCE-MRI-derived parameters and VS growth. The combination of mean Ktrans (P < .001) and ve (P < .001) tumor values provided an internally validated model with an AUC of 0.85 for growth, yielding a sensitivity of 89% and specificity of 73% at the optimized cutoff value. Only the mean ADC values were found to be significantly higher in shrinking tumors (P = .04). CONCLUSIONS: The strongly significant correlation observed between VS growth and Ktrans and ve tumor values indicate the great potential of the noninvasive DCE-MRI for individualized VS management in clinical practice. External validation is needed to further substantiate these findings.
Source: Neuro-Oncology
Authors: Sammy M Schouten, Daniel Lewis, Stefan Cornelissen, Ka-Loh Li, Xiaoping Zhu, Marnix C Maas, Sjoert Pegge, Thijs T G Jansen, Jef J S Mulder, Jérôme J Waterval, Alida A Postma, Omar Pathmanaban, David J Coope, Jolanda M M Derks, Patrick P J H Langenhuizen, Andrew T King, Jeroen B Verheul, Henricus P M Kunst
Language: English
Topic: MRI in cancer diagnosis
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $5,389
Award: G6896985708
OpenAlex ID: W4404646252
