Director of Imaging
Children's Hospital of Philadelphia
Dr. Ali’s research focuses on multimodality imaging using structural and physiologic MRI imaging with additional PET probes and molecular imaging techniques to better understand the complex nature of brain tumor microenvironment. The ultimate goal of his research is to use imaging and liquid biopsy biomarkers in prognostication, tumor response evaluation, and differentiation of tumor progression from treatment effect.
Ali joined D³b in 2020 and is also currently an Assistant Professor of Radiology at the Hospital of the University of Pennsylvania. He is committed to facilitating collaboration and accelerating the implementation of advanced MRI and molecular imaging in pediatric brain tumors.
Radiology, Clinical Research
Children’s Hospital of Philadelphia
Leveraging existing pediatric low-grade tumor specimens, clinical and imaging outcome data and artificial intelligence innovations to develop integrated biomarkers of response for children with low-grade glioma
This study will utilize artificial intelligence algorithms to predict underlying mutational status and outcomes for pediatric low grade glioma based on complex brain tumor MRI imaging features.
Benjamin H. Kann
Low-Grade Gliomas also called astrocytomas are the most common cancer of the central nervous system in children. They represent a heterogeneous group of tumors that can be discovered anywhere within the brain or spinal cord. Although surgical resection may be curative, up to 20% of children still su
Current state of pediatric neuro-oncology imaging, challenges and future directions
Imaging plays an important role in the diagnosis, treatment, and monitoring of brain tumors. With the development of new imaging technologies and data analysis methods, doctors can create large databases of brain images that can be used to improve patient care. In this review, researchers discusses
Ali Nabavizadeh, Matthew J Barkovich, Ali Mian, Van Ngo, Anahita Fathi Kazerooni, Javier E Villanueva-Meyer
Unsupervised machine learning using K-means identifies radiomic subgroups of pediatric low-grade gliomas that correlate with key molecular markers
This study aimed to investigate if an unsupervised machine learning approach based on radiomic features could reveal distinct imaging subtypes of pediatric low-grade gliomas (pLGGs). The researchers collected multi-parametric MR images from 157 patients with pLGGs and extracted quantitative radiom
Debanjan Haldar, Anahita Fathi Kazerooni, Sherjeel Arif, Ariana Familiar, Rachel Madhogarhia, Nastaran Khalili, Sina Bagheri, Hannah Anderson, Ibraheem Salman Shaikh, Aria Mahtabfar, Meen Chul Kim, Wenxin Tu, Jefferey Ware, Arastoo Vossough, Christos Davatzikos, Phillip B. Storm, Adam Resnick, Ali Nabavizadeh