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Gregory J. Czarnota

Hospital for Sick Children · CA
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Area of research
Biomedical Engineering · Radiology, Nuclear Medicine and Imaging
Research interest
Research interests include Breast cancer, Medicine, Ultrasound, Chemotherapy, Radiomics, and Computer science.
h-index
citations
519
works
20
NIH funding
primary concept
email

Recent publications

Synthesizing Ultrasound B-mode Images from Subsampled RF Data: A Data-Driven Deep Learning Approach <sup>*</sup>
2025cited by 0position: middledoi
Quantitative ultrasound imaging for predicting response and guiding personalized neoadjuvant chemotherapy in breast cancer: randomized phase 2 clinical trial results
npj Precision Oncology 2025cited by 0position: lastdoi
QUANTITATIVE ULTRASOUND RADIOMICS GUIDED ADAPTIVE NEOADJUVANT CHEMOTHERAPY IN BREAST CANCER: A RANDOMIZED STUDY
Ultrasound in Medicine & Biology 2025cited by 0position: firstdoi
Quantitative ultrasound radiomics guided adaptive neoadjuvant chemotherapy in breast cancer: early results from a randomized feasibility study
Frontiers in Oncology 2024cited by 8position: lastdoi
Enhanced full-inversion-based ultrasound elastography for evaluating tumor response to neoadjuvant chemotherapy in patients with locally advanced breast cancer
Physica Medica 2023cited by 2position: middledoi
Deep learning of quantitative ultrasound multi-parametric images at pre-treatment to predict breast cancer response to chemotherapy
Scientific Reports 2022cited by 42position: middledoi
Prediction of chemotherapy response in breast cancer patients at pre-treatment using second derivative texture of CT images and machine learning
Translational Oncology 2021cited by 29position: lastdoi
Characterizing intra-tumor regions on quantitative ultrasound parametric images to predict breast cancer response to chemotherapy at pre-treatment
Scientific Reports 2021cited by 23position: middledoi
Radiomics in predicting recurrence for patients with locally advanced breast cancer using quantitative ultrasound
Oncotarget 2021cited by 21position: lastdoi
MRI texture features from tumor core and margin in the prediction of response to neoadjuvant chemotherapy in patients with locally advanced breast cancer
Oncotarget 2021cited by 20position: lastdoi
Quantitative ultrasound radiomics in predicting response to neoadjuvant chemotherapy in patients with locally advanced breast cancer: Results from multi‐institutional study
Cancer Medicine 2020cited by 77position: lastdoi
Quantitative ultrasound radiomics for therapy response monitoring in patients with locally advanced breast cancer: Multi-institutional study results
PLoS ONE 2020cited by 61position: lastdoi
Quantitative ultrasound radiomics using texture derivatives in prediction of treatment response to neo-adjuvant chemotherapy for locally advanced breast cancer
Oncotarget 2020cited by 41position: lastdoi
A priori prediction of tumour response to neoadjuvant chemotherapy in breast cancer patients using quantitative CT and machine learning
Scientific Reports 2020cited by 31position: lastdoi
Effect of Treatment Sequencing on the Tumor Response to Combined Treatment With <scp>Ultrasound‐Stimulated</scp> Microbubbles and Radiotherapy
Journal of Ultrasound in Medicine 2020cited by 12position: lastdoi
Machine Learning-Based A Priori Chemotherapy Response Prediction in Breast Cancer Patients using Textural CT Biomarkers
2020cited by 2position: lastdoi
Radiomics in Predicting Recurrence for Patients with Locally Advanced Breast Cancer using Quantitative Ultrasound
Research Square 2020cited by 0position: lastdoi
Quantitative MRI Biomarkers of Stereotactic Radiotherapy Outcome in Brain Metastasis
Scientific Reports 2019cited by 69position: middledoi
Breast Cancer Treatment Response Monitoring Using Quantitative Ultrasound and Texture Analysis: Comparative Analysis of Analytical Models
Translational Oncology 2019cited by 51position: lastdoi
Predictive Quantitative Ultrasound Radiomic Markers Associated With Treatment Response in Head and Neck Cancer
Future Science OA 2019cited by 30position: lastdoi

Grants

No grants ingested yet.

Frequent collaborators

Ali Sadeghi‐Naini · Sunnybrook Health Science Centre19 papers (2019–2025)Lakshmanan Sannachi · Sunnybrook Health Science Centre14 papers (2019–2025)Sonal Gandhi · University of Illinois Chicago11 papers (2019–2025)Belinda Curpen · Sunnybrook Health Science Centre10 papers (2019–2025)Frances C. Wright · McGill University Health Centre9 papers (2019–2025)Maureen Trudeau · Sunnybrook Health Science Centre9 papers (2020–2025)Daniel DiCenzo · Sunnybrook Health Science Centre8 papers (2020–2025)Michael C. Kolios · Sunnybrook Health Science Centre8 papers (2020–2025)Archya Dasgupta · Sunnybrook Health Science Centre8 papers (2020–2025)Nicole Look-Hong · Sunnybrook Health Science Centre7 papers (2020–2025)Andrea Eisen · Sunnybrook Health Science Centre7 papers (2020–2025)William T. Tran · York University6 papers (2019–2020)Kashuf Fatima · Sunnybrook Health Science Centre5 papers (2020–2021)Karina Quiaoit · Sunnybrook Health Science Centre4 papers (2020–2021)Hadi Moghadas-Dastjerdi · Sunnybrook Health Science Centre4 papers (2020–2021)Arjun Sahgal · Sunnybrook Research Institute4 papers (2019–2021)Greg J. Stanisz · Sunnybrook Health Science Centre4 papers (2019–2021)Mehrdad J. Gangeh · Sunnybrook Health Science Centre4 papers (2019–2020)Divya Bhardwaj · Sunnybrook Health Science Centre4 papers (2020–2021)Stephen Brade · Sunnybrook Health Science Centre3 papers (2020–2021)
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