Tuberomammillary Fusion and Moya-Moya Vasculopathy Associated with PHACE Syndrome
Bibliographic record
Abstract
A 1-year and 5 month-old female child with a large right facial hemangioma ([ Fig. 1 ]) presents recurrent seizures. Brain magnetic resonance imaging ([ Fig. 2 ]) demonstrated posterior fossa malformations, with a right colobomatous cyst and an extensive sub-occlusive vasculopathy with a Moya-Moya pattern consistent with PHACE syndrome. The PHACE syndrome is a phakomatosis also known as cutaneous hemangioma–vascular complex syndrome or Pascual-Castroviejo type II syndrome. There are some extracerebral and intracranial vascular abnormalities, including the Moya-Moya arteriopathy[ 1 ] which affects less than 7% of the patients.[ 2 ] Also, there are no previous papers reporting the association with tuberomammillary fusion in this clinical scenario. This uncommon association of PHACE syndrome is extremely relevant for neurologists, neuropediatricians, and neuroradiologists, expanding the imaging phenotype features. Fig. 1 Photos of the patient's face demonstrating the right segmental forehead and facial hemangioma at birth ( A ), proliferative phase ( B ), and the involutional phase ( C ). Note made for a right ocular prosthesis in the last image. Fig. 2 CT head without contrast ( A ) and 3.0 Tesla MRI and MRA of the brain ( B–L ). T1 MPRAGE-weighted images (B and C), T2 (D), FLAIR (E and F), DWI (G), ADC map (H), T1 post-gadolinium (I), and 3D-TOF (J–L). Multiple features of PHACE syndrome. Focal calcification in the proximal left PCA (blue arrow), left-sided tuberomammillary fusion (orange arrows), large colobomatous cyst in the right eye globe (green asterisk), left anterior pons hypoplasia (yellow arrow), cerebellar dysplasia (white dashed circle), diffuse “ivy”-sign (E and F), external watershed zone infarcts (purple arrows), and hyper-leptomeningeal enhancement (I). Moya-Moya pattern of steno-occlusive vasculopathy, with involvement of the carotid and vertebrobasilar systems (red arrows), pseudo-occlusion of the right ICA (red dashed arrows), and multiple pial collaterals (red dashed circles). 3D-TOF, three-dimensional time of flight; CT, computed tomography; DWI, diffusion-weighted imaging; FLAIR, fluid attenuated inversion recovery; ICA, internal carotid artery; MRA, magnetic resonance angiography; MRI, magnetic resonance imaging; PCA, posterior cerebral artery. Author Contribution 1. Case report project: A: conception; B: organization; C: execution. 2. Manuscript: A: writing of the first draft; B: review and critique. Freitas L.F.: 1A, 1B, 1C, 2A, 2B. Miranda E.C.: 1A, 1B, 1C. Amaro A.P.: 1A, 1B, 1C. Narvaez E.O.: 1A, 1B, 1C. Duarte M.L.: 1C, 2A, 2B. Ethical Statement Full consent was obtained from the patient for the case report publication. Financial Disclosure Dr. Freitas reports no disclosure. Dr. Miranda reports no disclosure. Dr. Amaro reports no disclosure. Dr. Narvaez reports no disclosure. Dr. Duarte reports no disclosure. Disclosure The authors report no disclosures relevant to the manuscript. Publication History Received: 05 June 2023 Accepted: 13 September 2023 Accepted Manuscript online: 16 September 2023 Article published online: 29 October 2023 © 2023. Thieme. All rights reserved. Georg Thieme Verlag KG Rüdigerstraße 14, 70469 Stuttgart, Germany
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".