Evan Calabrese

Assistant Professor

Duke University

United States

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Evan Calabrese is an Assistant Professor at Duke University in the United States. His research focuses on artificial intelligence applications in medical imaging and diagnostic interpretations, with significant contributions to the fields of brain tumor assessment, structured data extraction from diagnostic reports, and enhancing outcome predictions in neonates. Recent articles include studies on automated segmentation and the role of tumor boards in clinical decision-making related to glioma treatments.

Articles (19)

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2025

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2022

Collaborators (14)

Andreas Rauschecker

Assistant Professor

University of California, San Francisco

UNITED STATES

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Hamed Akbari

Assisshtant Professor

Santa Clara University

UNITED STATES

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Robert McKinstry

Professor

Washington University

UNITED STATES

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Kimberley Louise Alexander

Head of Brain Cancer Research

Chris O'Brien Lifehouse

AUSTRALIA

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Amit Mathur

Professor and Interim Director Division of Neonatal-Perinatal Medicine

Saint Louis University

UNITED STATES

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J. Russell Huie

Assistant Prof. Researcher

University of California, San Francisco

UNITED STATES

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Carol Kruchko

President & Chief Mission Officer

Central Brain Tumor Registry of the United States

UNITED STATES

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Michael Caton

Mount Sinai Hospital

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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Pierre Nedelec

University of California, San Francisco

UNITED STATES

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Patrick Roth

University Hospital of Zurich

SWITZERLAND

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Spyridon Bakas

Kingston University

UNITED KINGDOM

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Christopher Hess

University of California, San Francisco

UNITED STATES

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Evan Calabrese

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Articles (19)

Open-Weight Language Models and Retrieval-Augmented Generation for Automated Structured Data Extraction from Diagnostic Reports: Assessment of Approaches and Parameters

Development and Evaluation of Automated Artificial Intelligence–Based Brain Tumor Response Assessment in Patients with Glioblastoma

Grade-stratified meningioma risk among individuals who are non-Hispanic Black and interactions with male sex

Deep learning segmentation-based bone removal from computed tomography of the brain improves subdural hematoma detection

Role of the tumor board when prescribing mutant isocitrate dehydrogenase inhibitors to patients with isocitrate dehydrogenase-mutant glioma

Artificial Intelligence Outcome Prediction in Neonates with Encephalopathy (AI-OPiNE)

The University of California San Francisco Adult Longitudinal Post-Treatment Diffuse Glioma MRI Dataset

Automated segmentation of ablated lesions using deep convolutional neural networks: A basis for response assessment following laser interstitial thermal therapy

The University of California San Francisco Brain Metastases Stereotactic Radiosurgery (UCSF-BMSR) MRI Dataset

A Deep Learning Approach for Automated Bone Removal from Computed Tomography Angiography of the Brain

Correlating Quantitative MRI-based Apparent Diffusion Coefficient Metrics with 24-month Neurodevelopmental Outcomes in Neonates from the HEAL Trial

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

Deep Learning to Simulate Contrast-enhanced Breast MRI of Invasive Breast Cancer

Parieto-Occipital Injury on Diffusion MRI Correlates with Poor Neurologic Outcome following Cardiac Arrest

Interinstitutional Portability of a Deep Learning Brain MRI Lesion Segmentation Algorithm

The University of California San Francisco Preoperative Diffuse Glioma MRI Dataset

Longitudinal Assessment of Posttreatment Diffuse Glioma Tissue Volumes with Three-dimensional Convolutional Neural Networks

NIMG-67. MULTI-PARAMETRIC MRI-BASED MACHINE LEARNING ANALYSIS FOR PREDICTION OF NEOPLASTIC INFILTRATION AND RECURRENCE IN PATIENTS WITH GLIOBLASTOMA: UPDATES FROM THE MULTI-INSTITUTIONAL RESPOND CONSORTIUM

Combining radiomics and deep convolutional neural network features from preoperative MRI for predicting clinically relevant genetic biomarkers in glioblastoma

Collaborators (14)

Andreas Rauschecker

Hamed Akbari

Robert McKinstry

Kimberley Louise Alexander

Amit Mathur

J. Russell Huie

Carol Kruchko

Michael Caton

Suyash Mohan

Daniel Orringer

Pierre Nedelec

Patrick Roth

Spyridon Bakas

Christopher Hess

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