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Karki P, Murphy MC, Cogswell PM, Senjem ML, Graff-Radford J, Elder BD, Perry A, Graffeo CS, Meyer FB, Jack CR, Ehman RL, Huston J. Prediction of Surgical Outcomes in Normal Pressure Hydrocephalus by MR Elastography. AJNR Am J Neuroradiol 2024; 45:328-334. [PMID: 38272572 DOI: 10.3174/ajnr.a8108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 11/21/2023] [Indexed: 01/27/2024]
Abstract
BACKGROUND AND PURPOSE Normal pressure hydrocephalus is a treatable cause of dementia associated with distinct mechanical property signatures in the brain as measured by MR elastography. In this study, we tested the hypothesis that specific anatomic features of normal pressure hydrocephalus are associated with unique mechanical property alterations. Then, we tested the hypothesis that summary measures of these mechanical signatures can be used to predict clinical outcomes. MATERIALS AND METHODS MR elastography and structural imaging were performed in 128 patients with suspected normal pressure hydrocephalus and 44 control participants. Patients were categorized into 4 subgroups based on their anatomic features. Surgery outcome was acquired for 68 patients. Voxelwise modeling was performed to detect regions with significantly different mechanical properties between each group. Mechanical signatures were summarized using pattern analysis and were used as features to train classification models and predict shunt outcomes for 2 sets of feature spaces: a limited 2D feature space that included the most common features found in normal pressure hydrocephalus and an expanded 20-dimensional (20D) feature space that included features from all 4 morphologic subgroups. RESULTS Both the 2D and 20D classifiers performed significantly better than chance for predicting clinical outcomes with estimated areas under the receiver operating characteristic curve of 0.66 and 0.77, respectively (P < .05, permutation test). The 20D classifier significantly improved the diagnostic OR and positive predictive value compared with the 2D classifier (P < .05, permutation test). CONCLUSIONS MR elastography provides further insight into mechanical alterations in the normal pressure hydrocephalus brain and is a promising, noninvasive method for predicting surgical outcomes in patients with normal pressure hydrocephalus.
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Affiliation(s)
- Pragalv Karki
- From the Department of Radiology (P.K., M.C.M., P.M.C., M.L.S., J.G.-R., C.R.J., R.L.E., J.H.), Mayo Clinic College of Medicine, Rochester, Minnesota
| | - Matthew C Murphy
- From the Department of Radiology (P.K., M.C.M., P.M.C., M.L.S., J.G.-R., C.R.J., R.L.E., J.H.), Mayo Clinic College of Medicine, Rochester, Minnesota
| | - Petrice M Cogswell
- From the Department of Radiology (P.K., M.C.M., P.M.C., M.L.S., J.G.-R., C.R.J., R.L.E., J.H.), Mayo Clinic College of Medicine, Rochester, Minnesota
| | - Matthew L Senjem
- From the Department of Radiology (P.K., M.C.M., P.M.C., M.L.S., J.G.-R., C.R.J., R.L.E., J.H.), Mayo Clinic College of Medicine, Rochester, Minnesota
| | - Jonathan Graff-Radford
- Department of Neurology (J.G.-R.), Mayo Clinic College of Medicine, Rochester, Minnesota
| | - Benjamin D Elder
- Department of Neurologic Surgery (B.D.E., C.S.G., F.B.M.), Mayo Clinic College of Medicine, Rochester, Minnesota
| | - Avital Perry
- Department of Neurosurgery (A.P.), Sheba Medical Center, Tel Hashomer, Ramat Gan, Israel
| | - Christopher S Graffeo
- Department of Neurologic Surgery (B.D.E., C.S.G., F.B.M.), Mayo Clinic College of Medicine, Rochester, Minnesota
- Department of Neurosurgery (C.S.G.), University of Oklahoma, Oklahoma City, Oklahoma
| | - Fredric B Meyer
- Department of Neurologic Surgery (B.D.E., C.S.G., F.B.M.), Mayo Clinic College of Medicine, Rochester, Minnesota
| | - Clifford R Jack
- From the Department of Radiology (P.K., M.C.M., P.M.C., M.L.S., J.G.-R., C.R.J., R.L.E., J.H.), Mayo Clinic College of Medicine, Rochester, Minnesota
| | - Richard L Ehman
- From the Department of Radiology (P.K., M.C.M., P.M.C., M.L.S., J.G.-R., C.R.J., R.L.E., J.H.), Mayo Clinic College of Medicine, Rochester, Minnesota
| | - John Huston
- From the Department of Radiology (P.K., M.C.M., P.M.C., M.L.S., J.G.-R., C.R.J., R.L.E., J.H.), Mayo Clinic College of Medicine, Rochester, Minnesota
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Kawazoe M, Koga S, Dickson DW. Progressive supranuclear palsy can mimic idiopathic normal pressure hydrocephalus: A case series. J Neuropathol Exp Neurol 2023; 82:1033-1036. [PMID: 37944016 PMCID: PMC10658350 DOI: 10.1093/jnen/nlad090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2023] Open
Affiliation(s)
- Miki Kawazoe
- Department of Neuroscience, Mayo Clinic, Jacksonville, Florida, USA
| | - Shunsuke Koga
- Department of Neuroscience, Mayo Clinic, Jacksonville, Florida, USA
| | - Dennis W Dickson
- Department of Neuroscience, Mayo Clinic, Jacksonville, Florida, USA
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Cortical atrophy distinguishes idiopathic normal-pressure hydrocephalus from progressive supranuclear palsy: A machine learning approach. Parkinsonism Relat Disord 2022; 103:7-14. [PMID: 35988437 DOI: 10.1016/j.parkreldis.2022.08.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 07/25/2022] [Accepted: 08/07/2022] [Indexed: 11/22/2022]
Abstract
INTRODUCTION Progressive supranuclear palsy (PSP) and idiopathic normal pressure hydrocephalus (iNPH) share several clinical and radiological features, making the differential diagnosis challenging. In this study, we aimed to differentiate between these two diseases using a machine learning approach based on cortical thickness and volumetric data. METHODS Twenty-three iNPH patients, 50 PSP patients and 55 control subjects were enrolled. All participants underwent a brain 3T-MRI, and cortical thickness and volumes were extracted using Freesurfer 6 on T1-weighted images and compared among groups. Finally, the performance of a machine learning approach with random forest using the extracted cortical features was investigated to differentiate between iNPH and PSP patients. RESULTS iNPH patients showed cortical thinning and volume loss in the frontal lobe, temporal lobe and cingulate cortex, and thickening in the superior parietal gyrus in comparison with controls and PSP patients. PSP patients only showed mild thickness and volume reduction in the frontal lobe, compared to control subjects. Random Forest algorithm distinguished iNPH patients from controls with AUC of 0.96 and from PSP patients with AUC of 0.95, while a lower performance (AUC 0.76) was reached in distinguishing PSP from controls. CONCLUSION This study demonstrated a more severe and widespread cortical involvement in iNPH than in PSP, possibly due to the marked lateral ventricular enlargement which characterizes iNPH. A machine learning model using thickness and volumetric data led to accurate differentiation between iNPH and PSP patients, which may help clinicians in the differential diagnosis and in the selection of patients for shunt procedures.
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