Distinctive facial features in idiopathic Moyamoya disease in Caucasians: a first systematic analysis

Cutaneous manifestations in Moyamoya angiopathy: A review

BACKGROUND AND PURPOSE

Moyamoya angiopathy (MA) is a progressive cerebrovascular disease with a poorly understood pathophysiology. It is mainly characterized by progressive bilateral stenosis of the terminal intracranial part of the supraclinoid internal carotid arteries and the proximal parts of the middle and anterior cerebral arteries. This results in early-onset ischemic or hemorrhagic strokes. The disease may be idiopathic (known as Moyamoya disease) or associated with other heritable or acquired conditions, including type 1 neurofibromatosis or other RASopathies, sickle cell disease, Down syndrome, or autoimmune disorders (known as Moyamoya syndrome). Apart from the brain, other organ manifestations including cutaneous ones have also been described in MA patients.

MATERIALS AND METHODS

A literature research on PubMed was performed for articles mentioning the cutaneous association in MA and published between 1994 and October 2020.

CONCLUSION

The present review summarizes the cutaneous associations as well as the coincidental dermatological findings seen in MA patients. Those include changes in the epidermis, dermis, or skin appendages for example café-au-lait spots, hypomelanosis of Ito, livedo racemosa, hemangiomas, premature graying of hair, chilblains etc.

Capillary microscopy in Europeans with idiopathic Moyamoya angiopathy

OBJECTIVE

In Europe, MMA is a very rare non-inflammatory vasculopathy. MMA is an important differential diagnosis of cerebral vasculitis. Systemic manifestations, such as livedo racemosa or renal artery stenosis, associated with Moyamoya variants suggest the involvement also of non-cerebral vessels. Hypothetically, capillary microscopy could be a promising non-invasive screening method to visualize microcirculation, for example prior to cerebral angiography.

METHODS

Standardized capillary microscopic images were taken in European patients with MMA and subsequently evaluated in a blinded analysis, using data obtained from a large NP cohort and a large SLE cohort by the same blinded Investigator as controls.

RESULTS

Twenty-four European MMD patients and 14 healthy accompanying controls were included in this study. The results were compared to 116 SLE patients and 754 NP subjects. In MMD patients, no capillary morphological differences were found in comparison with NP, in particular no density reduction or increased neoangiogenesis. The pattern observed in the SLE cohort was clearly distinct from NP and MMD with regard to vascular density, vascular damage, and neoangiogenesis.

CONCLUSIONS

MMD is not associated with microvascular changes of the nailfold capillaries. In this respect, it is clearly distinct from SLE.

Quantification of Facial Traits

Measuring facial traits by quantitative means is a prerequisite to investigate epidemiological, clinical, and forensic questions. This measurement process has received intense attention in recent years. We divided this process into the registration of the face, landmarking, morphometric quantification, and dimension reduction. Face registration is the process of standardizing pose and landmarking annotates positions in the face with anatomic description or mathematically defined properties (pseudolandmarks). Morphometric quantification computes pre-specified transformations such as distances. Landmarking: We review face registration methods which are required by some landmarking methods. Although similar, face registration and landmarking are distinct problems. The registration phase can be seen as a pre-processing step and can be combined independently with a landmarking solution. Existing approaches for landmarking differ in their data requirements, modeling approach, and training complexity. In this review, we focus on 3D surface data as captured by commercial surface scanners but also cover methods for 2D facial pictures, when methodology overlaps. We discuss the broad categories of active shape models, template based approaches, recent deep-learning algorithms, and variations thereof such as hybrid algorithms. The type of algorithm chosen depends on the availability of pre-trained models for the data at hand, availability of an appropriate landmark set, accuracy characteristics, and training complexity. Quantification: Landmarking of anatomical landmarks is usually augmented by pseudo-landmarks, i.e., indirectly defined landmarks that densely cover the scan surface. Such a rich data set is not amenable to direct analysis but is reduced in dimensionality for downstream analysis. We review classic dimension reduction techniques used for facial data and face specific measures, such as geometric measurements and manifold learning. Finally, we review symmetry registration and discuss reliability.

Machine learning for detecting moyamoya disease in plain skull radiography using a convolutional neural network

Background

Recently, innovative attempts have been made to identify moyamoya disease (MMD) by focusing on the morphological differences in the head of MMD patients. Following the recent revolution in the development of deep learning (DL) algorithms, we designed this study to determine whether DL can distinguish MMD in plain skull radiograph images.

Methods

Three hundred forty-five skull images were collected as an MMD-labeled dataset from patients aged 18 to 50 years with definite MMD. As a control-labeled data set, 408 skull images of trauma patients were selected by age and sex matching. Skull images were partitioned into training and test datasets at a 7:3 ratio using permutation. A total of six convolution layers were designed and trained. The accuracy and area under the receiver operating characteristic (AUROC) curve were evaluated as classifier performance. To identify areas of attention, gradient-weighted class activation mapping was applied. External validation was performed with a new dataset from another hospital.

Findings

For the institutional test set, the classifier predicted the true label with 84·1% accuracy. Sensitivity and specificity were both 0·84. AUROC was 0·91. MMD was predicted by attention to the lower face in most cases. Overall accuracy for external validation data set was 75·9%.

Interpretation

DL can distinguish MMD cases within specific ages from controls in plain skull radiograph images with considerable accuracy and AUROC. The viscerocranium may play a role in MMD-related skull features.

Fund

This work was supported by grant no. 18-2018-029 from the Seoul National University Bundang Hospital Research Fund.

Microduplication of 15q13.3 and Microdeletion of 18q21.32 in a Patient with Moyamoya Syndrome

Moyamoya angiopathy (MA) is a cerebrovascular disease determining a progressive stenosis of the terminal part of the internal carotid arteries (ICAs) and their proximal branches and the compensatory development of abnormal “moyamoya” vessels. MA occurs as an isolated cerebral angiopathy (so-called moyamoya disease) or in association with various conditions (moyamoya syndromes) including several heritable conditions such as Down syndrome, neurofibromatosis type 1 and other genomic defects. Although the mechanism that links MA to these genetic syndromes is still unclear, it is believed that the involved genes may contribute to the disease susceptibility. Herein, we describe the case of a 43 years old woman with bilateral MA and peculiar facial characteristics, having a 484-kb microduplication of the chromosomal region 15q13.3 and a previously unreported 786 kb microdeletion in 18q21.32. This patient may have a newly-recognized genetic syndrome associated with MA. Although the relationship between these genetic variants and MA is unclear, our report would contribute to widening the genetic scenario of MA, in which not only genic mutation, but also genome unbalances are possible candidate susceptibility factors.