Author profile
University of British Columbia
Accurate 177Lu SPECT quantification is essential for patient-specific dosimetry in radiopharmaceutical therapies. Monte Carlo (MC) based reconstruction uses comprehensive system modeling to stochastically simulate photon transport and detector interactions, p...
Physiologically based pharmacokinetic (PBPK) models used in radiopharmaceutical therapy (RPT) and theranostic digital twins are rapidly increasing in size and mechanistic detail, often containing tens to hundreds of parameters. While such models fit imaging a...
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 develop and evaluate a personalized physician-in-the-loop (PPitL) AI framework for accurate and efficient CT tumor segmentation through iterative clinician feedback.
Radiopharmaceutical therapies (RPTs) effectively treat metastatic castration-resistant prostate cancer, yet injected radioactivities remain empirically prescribed. Although physiologically-based pharmacokinetic (PBPK) and pharmacodynamic (PD) models can separ...
Existing "one-size-fits-all" approaches in PSMA radiopharmaceutical therapies (RPTs) fail to account for critical inter-patient variabilities, risking under- or over-treatment. To address this, we are developing theranostic digital twins (TDTs) for reliable p...
Computational nuclear oncology (CNO) methods are advancing rapidly but often developed in isolated tools and workflows that are difficult to reproduce, compare, and share. This fragmentation limits virtual studies and slows translation of physiologically base...
Lesion tracking establishes correspondence across imaging time points to assess disease evolution and treatment response. Despite registration-, graph-, and AI-based methods across CT, MR, PET, and PET/MR, robust correspondence in whole-body CT remains challe...
Theranostic digital twins (TDTs) are virtual representations of individual patients, that integrate clinical patient history, imaging, and physiologically based pharmacokinetic (PBPK) modeling to simulate radiopharmaceutical therapies (RPTs). Their validation...
Accurate quantitative assessment in positron emission tomography (PET) is essential for reliable lesion characterization and therapy response evaluation. Quantitative accuracy is conventionally investigated using physical phantoms or numerical simulations; ho...
Automated quantification of tumor burden in PSMA PET/CT imaging is hampered by the low specificity of image-only AI models, which frequently misclassify physiological uptake as disease. This necessitates time-intensive manual corrections, limiting clinical ut...
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...
To determine whether radiomic features extracted from normal endocrine organs, when combined with clinical variables, can capture systemic imaging signatures predictive of clinical progression in patients with PSMA-negative prostate cancer.
To develop and evaluate a radiomic phenotyping and multi-lesion aggregation scheme to derive patient-level biomarkers for identifying progressive disease and predicting time-to-progression (TTP) in diffuse large B-cell lymphoma (DLBCL).
Radiopharmaceuticals, Theranostics, and Nuclear Medicine