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Extracranial vascular association of moyamoya disease: a systematic review and machine learning analysis

  • Ritwick Mondal,
  • Shramana Deb,
  • Nirmalya Ray,
  • Ananya Sengupta,
  • Purbita Sen,
  • Debarati Barma,
  • Jayanta Roy,
  • Anjan Chowdhury,
  • Julián Benito-León

摘要

Background

Intracranial steno-occlusive lesions are characteristic of moyamoya disease (MMD), but increasing reports of extracranial vascular involvement suggest a possible systemic arteriopathy.

Methods

We conducted a PRISMA-guided systematic review (1974–May 2025) to identify extracranial vascular involvement in classical MMD. Seventy-four studies were included. Because the search was limited to English-language publications, language bias may be present. A subset of 85 patients from 46 studies was used for exploratory multilabel machine-learning and deep-learning modeling based on clinical variables.

Results

Seventy-four studies comprising 143 patients (59.4% female) were included; mean age was 24.9 years. Bilateral intracranial disease predominated. Among patients with isolated extracranial involvement, coronary lesions were most frequent (33.1%), followed by renal (31.5%), pulmonary (14.6%), external carotid (10.8%), vertebral (4.6%), and celiac/mesenteric (3.1%) involvement; other rare single-vessel lesions accounted for 2.3%. Multisite involvement occurred in 9.1%. Of 25 genotyped patients, 23 (92.0%) carried RNF213 variants. Mortality was highest in the pulmonary subgroup (31.6%). Logistic regression and random forest showed the best overall balance of accuracy, F1 score, and ROC area, whereas one-dimensional convolutional neural networks were the strongest deep-learning models, although deep learning overall underperformed machine learning. Feature selection identified vascular risk factors and bilateral MMD as the strongest predictors.

Conclusions

Extracranial vascular involvement in MMD appears age- and vascular bed-specific. These findings are consistent with, but do not establish, a systemic arteriopathy framework and support further prospective validation.