Farzad Khalvati, University of Toronto

Research

Read how they describe their research on their University of Toronto profile.

Artificial Intelligence Genomics Neuroscience Outcome Paediatric Stroke Predictive Modelling Genetic Marker Prediction Magnetic Resonance Imaging

Latest papers

Integrating chemotherapy, radiotherapy, and O6-methylguanine-DNA methyltransferase (MGMT) status with deep-learning cellular tumor volumetry sharpens prediction of glioblastoma recurrence on postoperative MRI.
Neuro-oncology advances · 2026
Multiclass Radiomics-Based Prediction of BRAF Mutation Status in Pediatric Low-grade Gliomas Using Multisequence MRI.
AJNR. American journal of neuroradiology · 2026
Multimodal machine learning for distinguishing pediatric multiple sclerosis from non-inflammatory conditions using optical coherence tomography.
Frontiers in neurology · 2026

All 66 publications

Latest funding

Predicting Risk and Outcome in Paediatric Stroke
CIHR · 2025 · Principal investigator
$1,101,600
Artificial Intelligence for Equitable Healthcare Delivery in Diagnostic Imaging
SSHRC · 2022 · Principal investigator
$125,000
Imaging-based classification and treatment response prediction of pediatric low-grade gliomas for precision child health
CIHR · 2022 · Nominated PI
$235,500

All 5 grants and awards

66 publications.

01

Integrating chemotherapy, radiotherapy, and O6-methylguanine-DNA methyltransferase (MGMT) status with deep-learning cellular tumor volumetry sharpens prediction of glioblastoma recurrence on postoperative MRI.

Belbadaoui T, Forester A, Khalvati F, Gagnon L

Neuro-oncology advances2026

DOI: 10.1093/noajnl/vdag178 PMID: 42487901

02

Multiclass Radiomics-Based Prediction of BRAF Mutation Status in Pediatric Low-grade Gliomas Using Multisequence MRI.

Yahav-Dovrat A, Namdar K, Wagner MW, Sheng M, Naseri M, Soldatelli MD, Dirks P, Yeom KW, Hawkins C, Eran A, Tabori U, Khalvati F, Ertl-Wagner BB

AJNR. American journal of neuroradiology2026

DOI: 10.3174/ajnr.a9296 PMID: 41819797

03

Multimodal machine learning for distinguishing pediatric multiple sclerosis from non-inflammatory conditions using optical coherence tomography.

Chen C, Soltanieh S, Rajapaksa S, Khalvati F, Yeh EA

Frontiers in neurology2026

DOI: 10.3389/fneur.2026.1791696 PMID: 42093931

04

Federated Learning in Neurology: Bridging Data Privacy and Artificial Intelligence for Brain Health.

Soltanieh S, Khalvati F, Yeh EA

Seminars in neurology2026

DOI: 10.1055/a-2769-6752 PMID: 41461211

05

Canadian Spine Society

Chan V, Gausper A, Liu A, Andras LM, Illingworth KD, Skaggs DL, Imbeault R, Dufresne J, Parent S, Deschênes S

Canadian Journal of Surgery2025

DOI: 10.1503/cjs.020125

06

Segmentation-free pretherapeutic assessment of BRAF-status in pediatric low-grade gliomas.

Kudus K, Wagner M, Sheng M, Bennett J, Liu A, Dirks P, Tabori U, Hawkins C, Ertl-Wagner BB, Khalvati F

Communications medicine2025Senior author

DOI: 10.1038/s43856-025-01204-y PMID: 41310360

07

Multiclass machine learning models for molecular subtype identification of pediatric low-grade glioma using bi-institutional MRIs for precision medicine.

Namdar K, Wagner MW, Sheng M, Dirks P, Yeom KW, Hawkins C, Tabori U, Ertl-Wagner BB, Khalvati F

NPJ precision oncology2025Senior author

DOI: 10.1038/s41698-025-01136-9 PMID: 41249829

08

Correction: Generative AI for weakly supervised segmentation and downstream classification of brain tumors on MR images.

Yoo JJ, Namdar K, Wagner MW, Yeom KW, Nobre LF, Tabori U, Hawkins C, Ertl-Wagner BB, Khalvati F

Scientific reports2025Senior author

DOI: 10.1038/s41598-025-24254-7 PMID: 41102502

09

Seeing beyond the scan: artificial intelligence and the future of detecting hypoxic-ischemic brain injury in children.

Scholefield BR, Khalvati F

Resuscitation2025Senior author

DOI: 10.1016/j.resuscitation.2025.110731 PMID: 40701530

10

Predicting pediatric diagnostic imaging patient no-show and extended wait-times using LLMs, regression, and tree based models.

Rafique D, Liu X, Gong B, Belsito L, McCradden MD, Mazwi ML, Lee W, Ohanlon G, Tsang K, Shroff M, Ertl-Wagner B, Khalvati F

Frontiers in artificial intelligence2025Senior author

DOI: 10.3389/frai.2025.1652397 PMID: 40969170

Predicting Risk and Outcome in Paediatric Stroke

Principal investigatorCIHR2025Project Grant

Principal investigators: Dlamini, Nomazulu; Khalvati, Farzad

Keywords: Artificial Intelligence; Genomics; Neuroscience; Outcome; Paediatric Stroke; Predictive Modelling

$1,101,600

Artificial Intelligence for Equitable Healthcare Delivery in Diagnostic Imaging

Principal investigatorSSHRC2022New Frontiers in Research Fund - Exploration

Principal investigators: Khalvati, Farzad

Keywords: Equitable Access to Healthcare Diagnostic Imaging Compassionate Healthcare Machine Learning Accessibility Barriers Negative Appointment Experiences

$125,000

Imaging-based classification and treatment response prediction of pediatric low-grade gliomas for precision child health

Nominated PICIHR2022Project Grant - Priority Announcement: Pediatric Cancer Research

Principal investigators: Khalvati, Farzad; Ertl-Wagner, Birgit B

Keywords: Artificial Intelligence; Genetic Marker Prediction; Magnetic Resonance Imaging; Medical Image Analysis; Pediatric Cancer Research; Pediatric Low-Grade Glioma; Precision Child Health; Treatment Response Prediction

$235,500

Artificial Intelligence for Equitable Healthcare Delivery in Diagnostic Imaging

Principal investigatorSSHRC2022New Frontiers in Research Fund - Exploration

Principal investigators: Khalvati, Farzad

Keywords: Equitable Access to Healthcare ; Diagnostic Imaging; Compassionate Healthcare ; Machine Learning; Accessibility Barriers; Negative Appointment Experiences

$125,000

Attention Networks and Optimized Deep Learning Architectures for Cancer Diagnosis and Prognosis in Medical Imaging

Principal investigatorNSERC2021Discovery Grants Program - Individual

Principal investigators: Khalvati, Farzad

$112,000

From CIHR, NSERC and SSHRC funding decisions: CIHR since 2008, NSERC since 1991 and SSHRC since 1998, including their latest published competition results.

Frequent collaborators

Uri Tabori and Cynthia Hawkins: 122 shared papers
Uri Tabori and Eric Bouffet: 117 shared papers
Birgit Ertl-Wagner and Birgit Ertl-Wagner: 102 shared papers
Cynthia Hawkins and Eric Bouffet: 92 shared papers
Cynthia Hawkins and Farzad Khalvati: 13 shared papers
Uri Tabori and Farzad Khalvati: 12 shared papers
Uri Tabori and Steven Gallinger: 9 shared papers
Eric Bouffet and Birgit Ertl-Wagner: 8 shared papers
Eric Bouffet and Birgit Ertl-Wagner: 8 shared papers
Paul Karanicolas and Steven Gallinger: 7 shared papers
Farzad Khalvati and Steven Gallinger: 7 shared papers
Uri Tabori and Birgit Ertl-Wagner: 6 shared papers
Uri Tabori and Birgit Ertl-Wagner: 6 shared papers
Paul Karanicolas and Farzad Khalvati: 5 shared papers
Arjun Sahgal and Christopher Witiw: 4 shared papers
Farzad Khalvati and Birgit Ertl-Wagner: 3 shared papers
Farzad Khalvati and Birgit Ertl-Wagner: 3 shared papers
Eve Tsai and Francois Mathieu: 3 shared papers
Cynthia Hawkins and Arjun Sahgal: 2 shared papers
Chester Ho and Eve Tsai: 2 shared papers
Eric Bouffet and Farzad Khalvati: 2 shared papers
Frederick Zeiler and Arjun Sahgal: 2 shared papers
Frederick Zeiler and Christopher Witiw: 2 shared papers
Frederick Zeiler and Eve Tsai: 2 shared papers
Frederick Zeiler and David Lebel: 2 shared papers
Frederick Zeiler and Francois Mathieu: 2 shared papers
Arjun Sahgal and David Lebel: 2 shared papers
Arjun Sahgal and Francois Mathieu: 2 shared papers
Birgit Ertl-Wagner and Alexandre Boutet: 2 shared papers
Christopher Witiw and Eve Tsai: 2 shared papers
Christopher Witiw and David Lebel: 2 shared papers
Birgit Ertl-Wagner and Alexandre Boutet: 2 shared papers
Engy Abbas and Farzad Khalvati: 1 shared paper
Engy Abbas and Steven Gallinger: 1 shared paper
Desmond Kwok and Eric Parent: 1 shared paper
Desmond Kwok and Chester Ho: 1 shared paper
Desmond Kwok and Frederick Zeiler: 1 shared paper
Desmond Kwok and Farzad Khalvati: 1 shared paper
Desmond Kwok and Christopher Sun: 1 shared paper
Desmond Kwok and Ervin Sejdić: 1 shared paper
Desmond Kwok and Eisha Christian: 1 shared paper
Desmond Kwok and Christopher Bailey: 1 shared paper
Desmond Kwok and Brent Lanting: 1 shared paper
Uri Tabori and Alexandre Boutet: 1 shared paper
Eric Parent and Chester Ho: 1 shared paper
Eric Parent and Frederick Zeiler: 1 shared paper
Eric Parent and Farzad Khalvati: 1 shared paper
Eric Parent and Christopher Sun: 1 shared paper
Eric Parent and Ervin Sejdić: 1 shared paper
Eric Parent and Eisha Christian: 1 shared paper
Eric Parent and Christopher Bailey: 1 shared paper
Eric Parent and Brent Lanting: 1 shared paper
Chester Ho and Farzad Khalvati: 1 shared paper
Chester Ho and Christopher Sun: 1 shared paper
Chester Ho and Ervin Sejdić: 1 shared paper
Chester Ho and Eisha Christian: 1 shared paper
Chester Ho and Christopher Bailey: 1 shared paper
Chester Ho and Brent Lanting: 1 shared paper
Frederick Zeiler and Farzad Khalvati: 1 shared paper
Arjun Sahgal and Farzad Khalvati: 1 shared paper
Farzad Khalvati and Christopher Witiw: 1 shared paper
Farzad Khalvati and Christopher Sun: 1 shared paper
Farzad Khalvati and Alexandre Boutet: 1 shared paper
Farzad Khalvati and Ervin Sejdić: 1 shared paper
Farzad Khalvati and Eve Tsai: 1 shared paper
Farzad Khalvati and Eisha Christian: 1 shared paper
Farzad Khalvati and David Lebel: 1 shared paper
Farzad Khalvati and Francois Mathieu: 1 shared paper
Farzad Khalvati and Chris McIntosh: 1 shared paper
Farzad Khalvati and Christopher Bailey: 1 shared paper
Farzad Khalvati and Brent Lanting: 1 shared paper

Department of Surgery
Surgery
Department of Medical Imaging
Department of Laboratory Medicine and Pathobiology
Genetics and Genome Biology
Medical Biophysics
Neurosciences and Mental Health
Other

1 shared papers, latest 2023

Write to this professor

A short, specific email works best. This draft uses one of their recent papers; replace the parts in brackets with your own details before sending.

Applying for

Paper to mentionMention their CIHR grant from 2025

Subject: Prospective PhD student: Artificial Intelligence Dear Professor Khalvati, My name is [your name], and I am a [your current degree or role] at [your institution]. I am applying for a PhD position starting in [term and year]. I read your paper "Integrating chemotherapy, radiotherapy, and O6-methylguanine-DNA methyltransferase (MGMT) status with deep-learning cellular tumor volumetry sharpens prediction of glioblastoma recurrence on postoperative MRI." (2026), and it connects closely to my own work on [your research topic]. [One or two sentences on what you did and what you learned.] I also saw your CIHR-funded project "Predicting Risk and Outcome in Paediatric Stroke" (2025), and I would be glad to contribute to work in that direction. Are you accepting new PhD students for [term and year]? I have attached my CV and transcripts, and I would be glad to talk at a time that suits you. Thank you for your time. Kind regards, [your name]