Dr Mathieu Gaudreault
Honorary Conjoint Senior Fellow
The Sir Peter MacCallum Department of Oncology
25 Scholarly works
1 Projects
HIGHLIGHTS
2026
Journal article
A deep learning approach for predicting linear accelerator output settings in automated radiotherapy planning of oligometastatic cancer
DOI: 10.1016/j.phro.2025.1008902025
Journal article
Single- versus multi-model in the deep learning prediction of monitor units per control point for automated treatment planning in prostate cancer
DOI: 10.1002/acm2.702292025
Conference Proceedings
Machine Output Settings Prediction for Volumetric Modulated Arc Therapy Artificial Intelligence Treatment Planning of Small Tumors in the Lung
DOI: 10.1016/j.ijrobp.2025.06.33512025
Conference Proceedings
Higher Baseline Monocyte and Neutrophil Counts Are Associated with Significantly Worse Overall Survival in NSCLC Patients Treated with Curative-Intent Chemoradiation
DOI: 10.1016/j.ijrobp.2025.06.36712025
Journal article
3233 Combining different metastatic anatomical locations for the deep learning prediction of the monitor units per control point
DOI: 10.1016/s0167-8140(25)01568-32025
Journal article
1048 Deep learning monitor units per control point prediction for automated VMAT treatment planning in prostate cancer
DOI: 10.1016/s0167-8140(25)00263-42024
Research grants (other domestic)
Next-Generation Radiotherapy Treatment Planning With Artificial Intelligence
RECENT SCHOLARLY WORKS
2025
Journal article
Effect of arc length on the deep learning prediction of monitor units in lung stereotactic ablative radiation therapy treatment
DOI: 10.1016/j.ejmp.2025.1050182024
Journal article
The BeamSplitter – An algorithm providing the dose per control point of radiation therapy treatment plans
DOI: 10.1016/j.ejmp.2024.1048452024
Journal article
Dose-Effect Relationship of Kidney Function After SABR for Primary Renal Cell Carcinoma: TROG 15.03 FASTRACK II
DOI: 10.1016/j.ijrobp.2024.04.0662024
Journal article
Automated lattice radiation therapy treatment planning personalised to tumour size and shape
DOI: 10.1016/j.ejmp.2024.104490