Neuroradiology · 2025
Data-Driven differentiation of idiopathic Normal-Pressure hydrocephalus and progressive supranuclear palsy via automated volumetric analysis.
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Yun Suyoung, Song Yujee, Suh Chong Hyun, Jung Wooseok, Lee Seung Hyun, Kim Saehyun, Choi Kyu Sung, Heo Hwon, Shim Woo Hyun, Jo Sungyang, Chung Sun Ju, Lim Jae-Sung, Choi Yangsean, Kim Ho Sung, Kim Sang Joon, Lee Jae-Hong, Kim Eung Yeop
Аннотация
PURPOSE: This study aims to develop automated machine learning methods to differentiate idiopathic normal pressure hydrocephalus (iNPH) from progressive supranuclear palsy (PSP) using advanced imaging features and volumetric analysis.
METHODS: We analyzed T1-weighted 3D brain MRI scans of iNPH and PSP patients using automated component measurement acquisition and deep learning-based automated volumetric analysis. We grouped the MRI features as brainstem subgroup, volumetrics subgroup, midbrain to pons (MP) ratio subgroup (included the midbrain to pons area ratio and volume ratio), and disproportionately enlarged subarachnoid space hydrocephalus (DESH) subgroup (included the callosal angle, Sylvian fissure empty ratio, vertex region crowding ratio, and Evans' index). Key imaging features were quantified, and statistical comparisons were conducted to identify distinguishing characteristics. Machine learning models were applied to evaluate feature effectiveness and improve diagnostic classification accuracy.
RESULTS: This study analyzed 192 patients (132 iNPH, 60 PSP) and found significant differences in midbrain volume, midbrain to pons volume ratio, callosal angle, Sylvian fissure empty ratio, vertex region crowding ratio, and Evans' index. Machine learning models, particularly the linear Support Vector Machine (SVM), achieved high diagnostic accuracy (AUROC = 0.98) in distinguishing iNPH from PSP. Volumetric analysis outperformed other feature subgroups.
CONCLUSION: The deep learning-based automated brain volumetric analysis achieved high diagnostic accuracy in distinguishing iNPH from PSP using T1-weighted brain MR images.
METHODS: We analyzed T1-weighted 3D brain MRI scans of iNPH and PSP patients using automated component measurement acquisition and deep learning-based automated volumetric analysis. We grouped the MRI features as brainstem subgroup, volumetrics subgroup, midbrain to pons (MP) ratio subgroup (included the midbrain to pons area ratio and volume ratio), and disproportionately enlarged subarachnoid space hydrocephalus (DESH) subgroup (included the callosal angle, Sylvian fissure empty ratio, vertex region crowding ratio, and Evans' index). Key imaging features were quantified, and statistical comparisons were conducted to identify distinguishing characteristics. Machine learning models were applied to evaluate feature effectiveness and improve diagnostic classification accuracy.
RESULTS: This study analyzed 192 patients (132 iNPH, 60 PSP) and found significant differences in midbrain volume, midbrain to pons volume ratio, callosal angle, Sylvian fissure empty ratio, vertex region crowding ratio, and Evans' index. Machine learning models, particularly the linear Support Vector Machine (SVM), achieved high diagnostic accuracy (AUROC = 0.98) in distinguishing iNPH from PSP. Volumetric analysis outperformed other feature subgroups.
CONCLUSION: The deep learning-based automated brain volumetric analysis achieved high diagnostic accuracy in distinguishing iNPH from PSP using T1-weighted brain MR images.