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Updated: May 28, 2026
In Vivo Morphometric Analysis of Human Cranial Nerves Using Magnetic Resonance Imaging in Menière's Disease Ears and Normal Hearing Ears
Published on: February 21, 2018
Computerized morphometry as an aid in distinguishing recurrent versus nonrecurrent meningiomas
Shawna Noy1, Euvgeni Vlodavsky, Geula Klorin
1Department of Pathology and Oncology, Rambam Health Care Campus, Legacy Heritage Clinical Research Institute at Ram-bam, and Ruth and Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel.
Novel digital and morphometric methods can predict intracranial meningioma recurrence. These techniques analyze tumor growth patterns and nuclear textures, offering valuable insights for patient management.
Area of Science:
- Neuropathology
- Computational Pathology
- Oncology
Background:
- Intracranial meningiomas are common primary brain tumors.
- Predicting recurrence is crucial for effective patient management.
- Current prediction methods may benefit from advanced analytical tools.
Purpose of the Study:
- To introduce novel digital and morphometric methods for predicting intracranial meningioma recurrence.
- To identify specific image-derived variables associated with tumor recurrence.
- To enhance prognostic accuracy in meningioma patients.
Main Methods:
- Histologic images from 30 recurrent meningiomas were analyzed.
- Digital pattern recognition, Fourier transformation, and fractal analysis were employed.
- Nuclear texture and chromatin patterns were quantitatively assessed.
Main Results:
- Significant associations were found between morphometric parameters and recurrence times.
- Tumors with less nuclear orientation and higher fractal dimensions recurred faster.
- Increased nuclear density and irregular chromatin textures correlated with faster recurrence.
Conclusions:
- Digital morphometric analysis offers a novel approach to predict meningioma recurrence.
- These methods provide valuable prognostic information for clinicians.
- This technique may aid in optimizing the management of intracranial meningiomas.
