Huy Dinh

Stanford University
Country flagUnited States

Research Interests

Explore related searches

Contact this professor

Get a free first email built on one of their papers, ready for you to edit and send yourself.

Articles (9)

Year:

2025

Background Although image‐derived predictors of intracranial aneurysm (IA) rupture have been well explored, current understanding of IA growth is limited. Pulsatility index (PI) and wall shear stress PI (WSSPI) are important metrics measuring temporal hemodynamic instability. However, they have not been investigated in IA growth research. The present study seeks to verify reliable predictors of IA growth with comparative analyses of several important morphological and hemodynamic metrics between stable and growing cases among a group of unruptured IAs. Methods Using 3‐dimensional images, vascular models of 16 stable and 20 growing cases were constructed and verified using geodesic techniques. With an overall mean follow‐up period of 25 months, cases exhibiting a ≥10% increase in diameter were considered growing. Patient‐specific, pulsatile simulations were performed, and hemodynamic calculations were computed at 5 important regions of each aneurysm (inflow artery, aneurysm neck, body, dome, and outflow artery). Index values were compared between growing and stable IAs using ANCOVA controlling for aneurysm diameter. Stepwise multiple logistic regression and receiver operation characteristic analyses were conducted to investigate predictive models of IA growth. Results Compared with stable IAs, growing IAs exhibited significantly higher intrasaccular PI, intrasaccular WSSPI, intrasaccular spatial flow rate deviation, and intrasaccular spatial wall shear stress deviation. Stepwise logistic regression analysis revealed a significant predictive model involving PI at aneurysm body, WSSPI at inflow artery, and WSSPI at aneurysm body. Conclusions Our results showed that high degrees of hemodynamic variation within IAs are linked to growth, even after controlling for morphological parameters. Furthermore, evaluation of PI in conjunction with WSSPI yielded a highly accurate predictive model of IA growth. On validation in future cohorts, these metrics may aid in early identification of IA growth and understanding of IA remodeling mechanisms.

Year:

2023

Year:

2023

Year:

2022

Year:

2022

Year:

2022

Year:

2021

Year:

2021

Year:

2021

Collaborators (3)

Joanne Zhou

Emory University

UNITED STATES
View details

Jingwen Yao

Assistant Professor

University of California, Los Angeles

UNITED STATES
View details

Aichi Chien

-

UNITED STATES
View details
ApplyKite TwinLearns as you apply

Make your next move.

Prepare your application

Review drafts before you send them.