Hamed Akbari

Assisshtant Professor

Santa Clara University

United States

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About

Hamed Akbari is an Assistant Professor at Santa Clara University in the United States. His research focuses on glioblastoma, with recent articles covering topics such as radiomethylomic signatures, machine learning-based prognostic subgrouping, and the integration of imaging and genomic data. He is particularly interested in the sex-specific differences in glioma patients and the relationship between radiogenomic features and oncogenic drivers.

Articles (18)

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2025

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2024

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2021

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2020

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2020

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2020

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2020

Collaborators (11)

Sharyn Katz

University of Pennsylvania Perelman School of Medicine

UNITED STATES

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Graeme Woodworth

Professor

University of Maryland

UNITED STATES

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Kimberley Louise Alexander

Head of Brain Cancer Research

Chris O'Brien Lifehouse

AUSTRALIA

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Evan Calabrese

Assistant Professor

Duke University

UNITED STATES

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Bardia Yousefi

University of Pennsylvania

UNITED STATES

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Suyash Mohan

UNITED STATES

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Daniel Orringer

Associate Professor

NYU Langone Health

UNITED STATES

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Virginia Hill

Assistant Professor

Northwestern University Feinberg School of Medicine

UNITED STATES

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Spyridon Bakas

Kingston University

UNITED KINGDOM

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Aimilia Gastounioti

Assistant Professor

Washington University in St. Louis

UNITED STATES

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Aristeidis Sotiras

Assistant Professor

Washington University in St. Louis

UNITED STATES

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Hamed Akbari

Research Interests

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Articles (18)

Intratumoral preservation of the stem-like malignant cell proportion in glioblastoma, prognostic impact, and its radiomethylomic signatures

Machine learning-based prognostic subgrouping of glioblastoma: A multicenter study

The radiogenomic and spatiogenomic landscapes of glioblastoma and their relationship to oncogenic drivers

Sex-Specific Differences in Patients withIDH1–Wild-Type Grade 4 Glioma in the ReSPOND Consortium

Integrating imaging and genomic data for the discovery of distinct glioblastoma subtypes: a joint learning approach

Association of partial T2-FLAIR mismatch sign and isocitrate dehydrogenase mutation in WHO grade 4 gliomas: results from the ReSPOND consortium

Association of partial T2-FLAIR mismatch sign and isocitrate dehydrogenase mutation in WHO grade 4 gliomas: results from the ReSPOND consortium

Artificial intelligence-based locoregional markers of brain peritumoral microenvironment

The University of Pennsylvania glioblastoma (UPenn-GBM) cohort: advanced MRI, clinical, genomics, & radiomics

The University of Pennsylvania glioblastoma (UPenn-GBM) cohort: advanced MRI, clinical, genomics, & radiomics

Interactive Machine Learning-Based Multi-Label Segmentation of Solid Tumors and Organs

Impartially Validated Multiple Deep-Chain Models to Detect COVID-19 in Chest X-ray Using Latent Space Radiomics

SPAER: Sparse Deep Convolutional Autoencoder Model to Extract Low Dimensional Imaging Biomarkers for Early Detection of Breast Cancer Using Dynamic Thermography

Applications of Radiomics and Radiogenomics in High-Grade Gliomas in the Era of Precision Medicine

Reproducibility analysis of multi‐institutional paired expert annotations and radiomic features of the Ivy Glioblastoma Atlas Project (Ivy GAP) dataset

Histopathology‐validated machine learning radiographic biomarker for noninvasive discrimination between true progression and pseudo‐progression in glioblastoma

AI-based prognostic imaging biomarkers for precision neuro-oncology: The ReSPOND consortium

Overall survival prediction in glioblastoma patients using structural magnetic resonance imaging (MRI): Advanced radiomic features may compensate for lack of advanced MRI modalities

Collaborators (11)

Sharyn Katz

Graeme Woodworth

Kimberley Louise Alexander

Evan Calabrese

Bardia Yousefi

Suyash Mohan

Daniel Orringer

Virginia Hill

Spyridon Bakas

Aimilia Gastounioti

Aristeidis Sotiras

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