Abstract
BACKGROUND: Disproportionately enlarged subarachnoid-space hydrocephalus (DESH) is a key feature of Hakim's disease (synonymous with idiopathic normal pressure hydrocephalus; iNPH). However, it previously had been only subjectively evaluated. PURPOSE: This study aims to evaluate the usefulness of MRI indices, derived from deep learning segmentation of cerebrospinal fluid (CSF) spaces, for DESH detection and to establish their optimal thresholds. MATERIALS AND METHODS: This study retrospectively enrolled a total of 1009 participants, including 77 patients diagnosed with Hakim's disease, 380 healthy volunteers, 163 with mild cognitive impairment, 256 with Alzheimer's disease, and 217 with other types of neurodegenerative diseases. DESH, ventriculomegaly, tightened sulci in the high convexities, and Sylvian fissure dilatation were evaluated on three-dimensional T1-weighted MRI by radiologists. The total ventricles, high-convexity part of the subarachnoid space, and Sylvian fissure and basal cistern were automatically segmented using the CSF Space Analysis application (FUJIFILM Corporation). Moreover, DESH, Venthi, and Sylhi indices were calculated based on these 3 regions. The area under the receiver-operating characteristic curves of these indices and region volumes (volume ratios) for DESH detection were calculated. RESULTS: Of the 1009 participants, 101 (10%) presented with DESH. The DESH, Venthi, and Sylhi indices performed well with 95.0%-96.0% sensitivity and 91.5%-96.8% specificity at optimal thresholds. All patients with Hakim's disease were diagnosed with DESH, despite variations in severity. In patients with Hakim's disease, with or without Alzheimer's disease, the DESH index and total ventricular volume were significantly higher compared to patients with Alzheimer's disease, although the total intracranial cerebrospinal fluid volume was significantly lower. CONCLUSION: DESH, Venthi, and Sylhi indices, and the volumes and volume ratios of the ventricle and high-convexity part of the subarachnoid space computed using deep learning were useful for the DESH detection that may help to improve the diagnosis of Hakim's disease (ie, iNPH).