Faculty profile

Belinda Curpen

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Assistant ProfessorDepartment of Medical ImagingUniversity of Toronto

Belinda Curpen

Assistant Professor

Department of Medical Imaging

Temerty Faculty of Medicine

University of Toronto

Toronto, Ontario, Canada

Supervision signals

Professors usually supervise graduate students in their department's programs.
21 publications in the last five years, latest in 2026.
Senior (last) author on 6 recent papers, a sign of leading a research group.

These signals come from public records, including federal and provincial funding decisions. Ask the professor or the graduate program whether they are taking new students.

Academic metrics

Publications

492

Citations

Collaborators

Grants

Publications by year

Counts cover the publications linked to this profile. Citations and the h-index appear when the source records them.

Profiles and links

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Signals and details

Research

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

Latest papers

Early tumour size changes from neoadjuvant chemotherapy as a predictor of pathologic response in breast cancer.
PloS one · 2026
Validation of a Quantitative Ultrasound Texture Analysis Model for Early Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer: A Prospective Serial Imaging Study
Cancers · 2025
Prediction of Chemotherapy Response in Locally Advanced Breast Cancer Patients at Pre-Treatment Using CT Textural Features and Machine Learning: Comparison of Feature Selection Methods
Tomography · 2025

All 31 publications

Latest funding

Using MRI imaging to develop a personalized risk-based breast cancer screening strategy
CIHR · 2019 · Co-investigator
$925,650

All 1 grants and awards

31 publications.

Early tumour size changes from neoadjuvant chemotherapy as a predictor of pathologic response in breast cancer.

Chung EZ, Alberico D, Sannachi L, Dasgupta A, Yip J, Pena MLA, Gandhi S, Curpen B, Czarnota GJ

PloS one2026

DOI: 10.1371/journal.pone.0346704 PMID: 42201919

Validation of a Quantitative Ultrasound Texture Analysis Model for Early Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer: A Prospective Serial Imaging Study

Moore-Palhares D, Sannachi L, Chan AW, Dasgupta A, DiCenzo D, Gandhi S, Pezo R, Eisen A, Warner E, Wright F

Cancers20255 citationsOpen Access

DOI: 10.3390/cancers17152594

Prediction of Chemotherapy Response in Locally Advanced Breast Cancer Patients at Pre-Treatment Using CT Textural Features and Machine Learning: Comparison of Feature Selection Methods

Moslemi A, Osapoetra LO, Dasgupta A, Halstead S, Alberico D, Trudeau M, Gandhi S, Eisen A, Wright F, Look-Hong N

Tomography20255 citationsOpen Access

DOI: 10.3390/tomography11030033

End-to-end CNN-based deep learning enhances breast lesion characterization using quantitative ultrasound (QUS) spectral parametric images

Osapoetra LO, Moslemi A, Moore-Palhares D, Halstead S, Alberico D, Hwang A, Sannachi L, Curpen B, Czarnota GJ

Scientific Reports20254 citationsOpen Access

DOI: 10.1038/s41598-025-15772-5

Validation of Quantitative Ultrasound and Texture Derivative Analyses-Based Model for Upfront Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer

Chan AW, Sannachi L, Moore-Palhares D, Dasgupta A, Gandhi S, Pezo R, Eisen A, Warner E, Wright FC, Hong NL

Journal of Imaging20252 citationsOpen Access

DOI: 10.3390/jimaging11040109

Quantitative ultrasound imaging for predicting response and guiding personalized neoadjuvant chemotherapy in breast cancer: randomized phase 2 clinical trial results

Moore-Palhares D, Alberico D, Chan AW, DiCenzo D, Sannachi L, Dasgupta A, Yip J, Pena MLA, Gandhi S, Pezo R

npj Precision Oncology20252 citationsOpen Access

DOI: 10.1038/s41698-025-01134-x

Impact of menopause and age on breast density and background parenchymal enhancement in dynamic contrast-enhanced magnetic resonance imaging

Grey Kuling, Jennifer D. Brooks, Belinda N. Curpen, Ellen Warner, Anne Louise Martel

Journal of medical imaging20251 citationsOpen Access

DOI: 10.1117/1.jmi.12.s2.s22002 PMID: 40078986

Performance of ChatGPT-4o in Determining Radiology-Pathology Concordance and Management Recommendations Following Image-Guided Breast Biopsies.

Lee A, Curpen B, Alikhassi A

Diagnostics (Basel, Switzerland)2025

DOI: 10.3390/diagnostics15192536 PMID: 41095755

Radiation enhancement using focussed ultrasound-stimulated microbubbles for breast cancer: A Phase 1 clinical trial

Moore-Palhares D, Dasgupta A, Saifuddin M, Pena MLA, Prasla S, Ho L, Lu L, Kung J, McNabb E, Sannachi L

PLoS Medicine202434 citationsOpen Access

DOI: 10.1371/journal.pmed.1004408

Apriori prediction of chemotherapy response in locally advanced breast cancer patients using CT imaging and deep learning: transformer versus transfer learning

Moslemi A, Osapoetra LO, Dasgupta A, Alberico D, Trudeau M, Gandhi S, Eisen A, Wright F, Look-Hong N, Curpen B

Frontiers in Oncology202411 citationsOpen Access

DOI: 10.3389/fonc.2024.1359148

Profile data last refreshed on September 29, 2026 from the university directory, publication records and public research funding records.

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Belinda Curpen

Research

Latest papers

Latest funding

Early tumour size changes from neoadjuvant chemotherapy as a predictor of pathologic response in breast cancer.

Validation of a Quantitative Ultrasound Texture Analysis Model for Early Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer: A Prospective Serial Imaging Study

Prediction of Chemotherapy Response in Locally Advanced Breast Cancer Patients at Pre-Treatment Using CT Textural Features and Machine Learning: Comparison of Feature Selection Methods

End-to-end CNN-based deep learning enhances breast lesion characterization using quantitative ultrasound (QUS) spectral parametric images

Validation of Quantitative Ultrasound and Texture Derivative Analyses-Based Model for Upfront Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer

Quantitative ultrasound imaging for predicting response and guiding personalized neoadjuvant chemotherapy in breast cancer: randomized phase 2 clinical trial results

Impact of menopause and age on breast density and background parenchymal enhancement in dynamic contrast-enhanced magnetic resonance imaging

Performance of ChatGPT-4o in Determining Radiology-Pathology Concordance and Management Recommendations Following Image-Guided Breast Biopsies.

Radiation enhancement using focussed ultrasound-stimulated microbubbles for breast cancer: A Phase 1 clinical trial

Apriori prediction of chemotherapy response in locally advanced breast cancer patients using CT imaging and deep learning: transformer versus transfer learning

Using MRI imaging to develop a personalized risk-based breast cancer screening strategy

Frequent collaborators

Sonal Gandhi

Maureen Trudeau

Frances Wright

Gregory Czarnota

Andrea Eisen

Michael Kolios

Nicole Look Hong

Ali Sadeghi-Naini

William Tran

Daniel Moore Freitas Palhares

Ellen Warner

Greg Stanisz

Afsaneh Alikhassi

Mia Skarpathiotakis

Arjun Sahgal

Carrie Betel

Anne Martel

Rossanna Pezo

Danny Vesprini

David Lim

Irene Karam

Navneet Singh

Roberta Jong