Idiopathic Normal-Pressure Hydrocephalus: Diagnostic Accuracy of Automated Sulcal Morphometry in Patients With Ventriculomegaly

Abstract

BACKGROUND Idiopathic normal-pressure hydrocephalus (iNPH) is a treatable cause of gait and cognitive impairment. iNPH should be differentiated from ventriculomegaly secondary to brain atrophy to choose the best therapeutic option (ventriculoperitoneal shunt vs medical management). OBJECTIVE To determine the diagnostic accuracy of automated sulcal morphometry to differentiate patients with iNPH from patients with ventriculomegaly of neurodegenerative origin. METHODS Thirty-eight consecutive patients with iNPH (shunt responsive n = 31, nonresponsive n = 7), 35 with vascular cognitive disorder, and 25 age- and sex-matched healthy controls were prospectively included and underwent cognitive evaluation and 3T brain magnetic resonance imaging. Sulcal opening of 10 sulci of interest was retrospectively measured using an automated surface-based approach from the 3-dimensional T1-weighted images. Receiver-operating characteristic curve analyses determined the best parameter to identify iNPH patients. RESULTS The best parameter to discriminate shunt-responsive iNPH from patients with vascular cognitive disorder and healthy controls was the ratio between calcarine sulcus and cingulate sulcus opening with an area under the curve of 0.94 (95% CI: 0.89, 0.99). A cut-off value of 0.95 provided the highest sensitivity (96.8%) and specificity (83.3%). CONCLUSION This preliminary study showed that automated sulcal morphometry may help the neurosurgeon to identify iNPH patients and to exclude other causes of ventriculomegaly.

CSF tap test in idiopathic normal pressure hydrocephalus: still a necessary prognostic test?

“…Changes of the subarachnoid space may also represent a stressor onto brain tissues and have treatment implications [ 27 ]. In our sample, the posterior cingulate and calcarine fissures were, respectively, constrained and enlarged in iNPH compared to HCs, consistently with previous findings [ 28 ]. In the multivariable prediction analyses, less constrained posterior cingulate fissure (i.e., more pronounced high-convexity tightness), together with stronger hyper-alignment of cingular WM fibers, were associated with poor CSFTT outcome.…”

“…1 a). The volume of brain sulci (bilateral posterior callosal marginal fissure (PCMF) and calcarine fissure (CF), previously implicated in iNPH differential diagnosis [ 27 ] and prognosis [ 28 ]) and lateral ventricles was quantified based on the Brainvisa atlas v201, similarly to previous work [ 27 ] (Fig. 1 c).…”

Radiological predictors of shunt response in the diagnosis and treatment of idiopathic normal pressure hydrocephalus: a systematic review and meta-analysis

“…1). Nine studies [8,14,14,23,26,27,31,41,59] cored a low risk of bias overall using the ROBINS-I [53] while 18 scored moderate [2][3][4][5]20,25,34,40,41,46,47,49,50, risk and 1 study was rated as serious risk [34] (Fig. 2, Supplementary Material: Table 4).…”

SVM-Based Normal Pressure Hydrocephalus Detection

“…Both junior raters assessed "no NPH pattern", both senior raters a "probable NPH pattern" Fig. 3 The receiver operating characteristic (ROC) curve displays the performance of the support vector machine (SVM) in terms of identifying normal pressure hydrocephalus (NPH) patients ings in terms of how reliably NPH patients can be identified in MRI and surpassed the accuracy of others who differentiated NPH, vascular cognitive disease and HC using sulcal patterns [23] or a 3D convolutional ladder network in the differentiation of NPH, Alzheimer's dementia and HC [24]. Zhang et al described a similar approach on CT imaging with a comparable group (27 NPH patients and 34 HC) and reached acceptable sensitivity, too [25].…”

“…Unspecific ventricular enlargement was successfully detected in computed tomography (CT) imaging [ 22 ] and machine learning was used to determine most discriminate regions in patients who had been identified by radiologists as having a NPH pattern [ 12 ]. In contrast, we compared the performance of an SVM against human readings in terms of how reliably NPH patients can be identified in MRI and surpassed the accuracy of others who differentiated NPH, vascular cognitive disease and HC using sulcal patterns [ 23 ] or a 3D convolutional ladder network in the differentiation of NPH, Alzheimer’s dementia and HC [ 24 ]. Zhang et al described a similar approach on CT imaging with a comparable group (27 NPH patients and 34 HC) and reached acceptable sensitivity, too [ 25 ].…”