Abstract #0237

CEST Radiomics for Accurate Differentiation of True Tumour Progression from Treatment Effects in Brain Metastases Using Machine Learning

Dylan Young^1,2,3, Wilfred W Lam³, Rachel W Chan³, Dafna Sussman^1,2,4, Pejman J Maralani⁵, Arjun Sahgal^3,6, Hany Soliman^3,6, and Greg J Stanisz^3,7

¹Department of Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University, Toronto, Canada; Institute for Biomedical Engineering, Science and Technology (iBEST) at Toronto Metropolitan University, Toronto, ON, Canada, ²St. Michael’s Hospital, Toronto, Canada & Keenan Research Centre for Biomedical Science, St. Michael’s Hospital, Toronto, ON, Canada, ³Sunnybrook Research Institute, Toronto, ON, Canada, ⁴Department of Obstetrics and Gynaecology, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada, Toronto, ON, Canada, ⁵Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada, ⁶Department of Radiation Oncology, Sunnybrook Odette Cancer Center, University of Toronto, Toronto, ON, Canada, ⁷Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada

Synopsis

Keywords: Diagnosis/Prediction, AI/ML Software

Motivation: Brain metastases are common and difficult to diagnose accurately post-stereotactic radiosurgery (SRS), particularly in distinguishing true tumor progression (TP) from radiation necrosis (RN) non-invasively.

Goal(s): To develop a machine learning (ML) model using chemical exchange saturation transfer (CEST) imaging and MRI sequences to differentiate TP from RN.

Approach: Radiomic features from APT, NOE, and asymmetry CEST images, along with FLAIR, T1, and T2 MRI sequences, were extracted to train ML models for TP and RN classification.

Results: The Extra Trees model with APT and asymmetry-based MTR features achieved high accuracy (AUC=0.86), showing AI’s potential to improve non-invasive diagnostics in clinical practice.

Impact: These findings suggest that AI-driven analysis of combined CEST and MRI features could serve as an effective diagnostic tool in clinical practice, potentially improving patient management without the need for further invasive procedures.

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