# Statistical tensor regression models for intracranial aneurysm growth prediction

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2024 · $529,410

## Abstract

Project Summary/Abstract
Cerebral aneurysms occur in about 6% of the population and have a very high morbidity and mortality rate if they
rupture. Fortunately, most unruptured intracranial aneurysms (UIAs) rarely cause symptoms and do not require
an invasive treatment that may itself causes severe cerebrovascular disorders. However, it is very difficult to
predict which UIAs will rupture. Recent evaluations of the hemodynamic features of UIAs, using 4D Flow MRI
(4DF), have shown promising results that suggest specific hemodynamic variables may have a great impact on
aneurysm growth or rupture. However, the clinical applicability of these hemodynamic variables in predicting UIA
growth has not yet been realized due to the lack of robust methods for gathering them, and also for describing their
relationship to UIA growth. To fill in the gaps, the proposed research aims to develop a comprehensive statistical
and computational framework to predict: (a) the growth of UIAs at the 24th month (b) their growth trajectory over
a five-year period. Our goal is to develop a statistical framework to improve the UIA growth prediction that, in
turn, will improve the UIA rupture risk assessment. Toward achieving this goal, we will develop a unique tensor
regression machine learning framework that will (1) enhance 4DF resolution (2) predict the UIA growth at the 24th
month and (3) predict the longitudinal UIA growth trajectory. Successful completion of the proposed research will
provide a comprehensive computational system that can assist physicians when deciding whether a patient with
UIA needs treatment, or follow-up imaging, as well as the time interval for the surveillance imaging.

## Key facts

- **NIH application ID:** 10877389
- **Project number:** 1R01NS132766-01A1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** fei jiang
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $529,410
- **Award type:** 1
- **Project period:** 2024-06-15 → 2029-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10877389

## Citation

> US National Institutes of Health, RePORTER application 10877389, Statistical tensor regression models for intracranial aneurysm growth prediction (1R01NS132766-01A1). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10877389. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*