Diagnosis of Normal-Pressure Hydrocephalus: Use of Traditional Measures in the Era of Volumetric MR Imaging

Abstract

Purpose To assess the diagnostic performance of the callosal angle (CA) and Evans index (EI) measures and to determine their role versus automated volumetric methods in clinical radiology. Materials and Methods Magnetic resonance (MR) examinations performed before surgery (within 1-5 months of the MR examination) in 36 shunt-responsive patients with normal-pressure hydrocephalus (NPH; mean age, 75 years; age range, 58-87 years; 26 men, 10 women) and MR examinations of age- and sex-matched patients with Alzheimer disease (n = 34) and healthy control volunteers (n = 36) were studied. Three blinded observers independently measured EI and CA for each patient. Volumetric segmentation of global gray matter, white matter, ventricles, and hippocampi was performed by using software. These measures were tested by using multivariable logistic regression models to determine which combination of metrics is most accurate in diagnosis. Results The model that used CA and EI demonstrated 89.6%-93.4% accuracy and average area under the curve of 0.96 in differentiating patients with NPH from patients without NPH (ie, Alzheimer disease and healthy control). The regression model that used volumetric predictors of gray matter and white matter was 94.3% accurate. Conclusion CA and EI may serve as a screening tool to help the radiologist differentiate patients with NPH from patients without NPH, which would allow for designation of patients for further volumetric assessment. RSNA, 2017.

A Novel Deep Learning Approach with a 3D Convolutional Ladder Network for Differential Diagnosis of Idiopathic Normal Pressure Hydrocephalus and Alzheimer’s Disease

“…In previous studies, comparison of the volumes of brain gray matter and white matter by automated segmentation was able to differentiate between NPH and AD with accuracy of 94-96%, 11,12 and comparison of the callosal angle and Evans index demonstrated the accuracy of 90-93%. 12 Although the result of the present study was slightly inferior to those of the previous reports, our model based on the deep learning has the advantage that post-processing is unnecessary and the diagnosis can be obtained in 1-2 s per case by simply inputting 3D T 1 -weighted images.…”

“…In previous studies, comparison of the volumes of brain gray matter and white matter by automated segmentation was able to differentiate between NPH and AD with accuracy of 94-96%, 11,12 and comparison of the callosal angle and Evans index demonstrated the accuracy of 90-93%. 12 Although the result of the present study was slightly inferior to those of the previous reports, our model based on the deep learning has the advantage that post-processing is unnecessary and the diagnosis can be obtained in 1-2 s per case by simply inputting 3D T 1 -weighted images. Although there was no statistically significant difference between the results of our model and the interpretation of the radiologists, the diagnostic accuracy of our model was higher than those of the radiologists who conducted the reading study: the radiologists tended to be more accurate for iNPH diagnosis, whereas the deep learning-based model tended to be more accurate for AD.…”

“…There have been some reports that aimed to distinguish between iNPH and AD using three-dimensional (3D) T 1 -weighted images by measuring the hippocampal volume, lateral ventricle size, the corpus callosum angle, or the whole gray matter and white matter volumes. [10][11][12] Even though segmental volume measurements can now be performed automatically, these techniques still require manual steps and some expertise. Brain deformation is severe in iNPH patients, so standardization or segmentation failure may frequently occur.…”

Diagnostic accuracy of the iNPH Radscale in idiopathic normal pressure hydrocephalus

“…One such example is the study by Ishii et al [18], who showed that the combination of a callosal angle of less than 90˚and an Evans' index over 0.30 could separate probable iNPH from Alzheimer disease. Likewise, Miskin et al [24] demonstrated that Evans' index combined with callosal angle could separate definite iNPH from healthy controls…”

“…In the intermediate group with 5-7 scores on the iNPH Radscale, the patients should be further evaluated for iNPH depending on symptoms, comorbidity and results of additional investigations. Several previous studies have described the prognostic value of imaging features in predicting shunt response in iNPH patients [14,[19][20][21][22], but only a few studies have focused on the diagnostic value [18,23,24]. One such example is the study by Ishii et al [18], who showed that the combination of a callosal angle of less than 90˚and an Evans' index over 0.30 could separate probable iNPH from Alzheimer disease.…”