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Risk estimation for idiopathic normal-pressure hydrocephalus: development and validation of a brain morphometry-based
Su Young Yun1,2, Kyu Sung Choi3, Chong Hyun Suh4
1Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
European Radiology
|April 14, 2023
Summary
A new nomogram using MRI features accurately predicts idiopathic normal-pressure hydrocephalus (iNPH). This tool aids in early diagnosis and may prevent invasive procedures for iNPH patients.
Area of Science:
- Neurology
- Radiology
- Medical Imaging
Background:
- Idiopathic normal-pressure hydrocephalus (iNPH) is a neurological condition affecting gait, cognition, and bladder control.
- Accurate diagnosis of iNPH is challenging and often requires invasive tests.
- Developing non-invasive predictive tools is crucial for timely and appropriate patient management.
Purpose of the Study:
- To develop and validate a predictive nomogram for idiopathic normal-pressure hydrocephalus (iNPH) using magnetic resonance imaging (MRI) features.
- To assess the diagnostic performance of the nomogram in distinguishing iNPH from other neurological conditions.
Main Methods:
- Retrospective analysis of MRI data from patients aged 60 years and older with suspected iNPH, Parkinson's disease, Alzheimer's disease, or healthy controls.
- Utilized deep learning-based brain segmentation for 3D volumetry and logistic regression to build a nomogram based on high-convexity tightness, callosal angle, and normalized lateral ventricle volume.
- Internal and external validation of the nomogram's predictive accuracy and calibration.
Main Results:
- The nomogram incorporated high-convexity tightness, callosal angle < 90°, and normalized lateral ventricle volume as key predictors of iNPH.
- The nomogram demonstrated high predictive performance with an area under the curve (AUC) of 0.995 in the study sample, 0.994 in internal validation, and 0.969 in external validation.
- These MRI-derived features were significantly associated with iNPH in multivariable analysis.
Conclusions:
- A brain morphometry-based nomogram utilizing specific MRI findings can accurately estimate the probability of iNPH.
- This non-invasive tool may aid in the early prediction of iNPH, potentially reducing the need for invasive diagnostic procedures.
- The nomogram offers a valuable tool for clinicians in the diagnostic workup of suspected iNPH.
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