Author profile
Department of Basic and Translational Research, BC Cancer Research Institute
Radiomics enables quantitative characterization of tumors from PET and CT imaging. When anatomic-region-specific cancer data are limited, transfer and semi-supervised learning can improve prediction by leveraging data from different regions; however, the basi...
AI–based radiomics models for thyroid ultrasound often lack interpretability, limiting clinical trust. This study aims to develop and validate a fully interpretable radiomics framework for thyroid nodule classification by linking quantitative ultrasound featu...
To address the limited robustness of existing CT-based radiogenomic models, this study develops a multicenter framework for non-invasive dual prediction of EGFR and KRAS gene mutations in personalized management of non-small cell lung cancer (NSCLC), comparin...
To apply a multiparametric model selection strategy within a tensor radiomics paradigm, whereby different flavours of radiomics features are generated from multiple PET-CT image fusion strategies, to identify reliable and generalizable machine learning (ML) m...