Rationale:Cerebellar liponeurocytoma is a rare tumor of the central nervous system (CNS) characterized by low proliferation but high likelihood of recurrence. Because of its rarity and the paucity of systematic follow-up, the biological behaviors and clinical features of this tumor are still poorly understood. We herein reported a case of cerebellar liponeurocytoma originating in the cerebral hemisphere.Patient concerns:A 11-year-old male with intermittent headache, nausea, and vomiting. The first computed tomography revealed a large mass in the right cerebral hemisphere. He was transferred to our institution for neurosurgical treatment.Diagnosis:Magnetic resonance imaging showed a large cystic—solid mass in the right frontal lobe with obvious contrast enhancement. Histopathological examinations showed sheets of isomorphic small neoplastic cells with clear cytoplasm and focal lipomatous differentiation. On immunohistochemistry, tumor cells were positive for synaptophysin, microtubule-associated protein 2, and neuronal nuclei antigen.Interventions:The patient was performed a right fronto-parietal craniotomy, and gross total resection of the tumor was achieved without adjuvant therapy.Outcomes:No clinical or neuroradiological evidence of recurrence or residual of the tumor was found 6 years and 2 months after initial surgery.Lessons:Cerebellar liponeurocytoma developing in supratentorial cerebral hemisphere was first reported in the present study. The radiological and histopathological features may be useful in differentiating this rare tumor from other tumors at similar locations. A change in the nomenclature of cerebellar liponeurocytomas should be considered in future World Health Organization (WHO) classifications.

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IMPORTANCEDeep learning may be able to use patient magnetic resonance imaging (MRI) data to aid in brain tumor classification and diagnosis. OBJECTIVE To develop and clinically validate a deep learning system for automated identification and classification of 18 types of brain tumors from patient MRI data. DESIGN, SETTING, AND PARTICIPANTS This diagnostic study was conducted using MRI data collected between 2000 and 2019 from 37 871 patients. A deep learning system for segmentation and classification of 18 types of intracranial tumors based on T1-and T2-weighted images and T2 contrast MRI sequences was developed and tested. The diagnostic accuracy of the system was tested using 1 internal and 3 external independent data sets. The clinical value of the system was assessed by comparing the tumor diagnostic accuracy of neuroradiologists with vs without assistance of the proposed system using a separate internal test data set. Data were analyzed from March 2019 through February 2020. MAIN OUTCOMES AND MEASURES Changes in neuroradiologist clinical diagnostic accuracy inbrain MRI scans with vs without the deep learning system were evaluated. RESULTS A deep learning system was trained among 37 871 patients (mean [SD] age, 41.6 [11.4] years; 18 519 women [48.9%]). It achieved a mean area under the receiver operating characteristic curve of 0.92 (95% CI, 0.84-0.99) on 1339 patients from 4 centers' data sets in diagnosis and classification of 18 types of tumors. Higher outcomes were found compared with neuroradiologists for accuracy and sensitivity and similar outcomes for specificity (for 300 patients in the Tiantan

Objective-The aim of this study was to determine how hemodynamics of the posterior cerebral artery (PCA) are associated with cerebral ischemic lesions in moyamoya disease (MMD).Methods-Thirty-six patients with ischemic MMD (Suzuki grade IV-V) were retrospectively analyzed. Hemodynamic parameters of the PCA were measured by transcranial color-coded sonography. We classified the range of ischemic lesions into 3 grades and perfusion levels into 3 grades according to computed tomography (CT) results. PCA steno-occlusion and leptomeningeal collaterals were confirmed by digital subtraction angiography. Ultrasonographic parameters in the PCA were compared with these radiographic findings. Results-The velocity in the involved PCA (mean flow velocity [MFV] median, 42.00 [range, 34.50-58.00] cm/s) was significantly lower than that in the normal PCA (MFV median, 95.00 [range, 76.50-119.50] cm/s) (P < .001). The velocity in the PCA increased significantly as the leptomeningeal collateral stage advanced (MFV stage 1: median, 38.50 [range, 29.75-63.50] cm/s; stage 2: median, 55.00 [range, 44.00-96.00] cm/s; stage 3: median, 94.00 [range, 54.00-118.25] cm/s; stage 4: median, 85.50 [range, 70.50-117.75] cm/s, respectively) (P < .05). Decreased PCA velocities were associated with a larger ischemic area on CT (P ≤ .001). PCA velocity had no correlation with CT perfusion level of the temporal and frontal lobes. PCA velocity had significant correlations with perfusion level in the occipital (P < .001) and parietal lobes (P < .05).Conclusions-Our results suggest ischemic lesion patterns (as demonstrated on CT imaging) are associated with PCA velocity measurements in the advanced stage of MMD. Thus, monitoring PCA velocity in patients with advanced MMD may provide additional information to assist in managing these patients.