Dissertation Defence: A Probabilistic Framework for Prostate Core-Needle Biopsies in HDR Brachytherapy

Matthew Muscat, supervised by Dr. Andrew Jirasek, will defend their dissertation titled “A Probabilistic Framework for Prostate Core-Needle Biopsies in HDR Brachytherapy: From Clinical Quality Assurance to Translational Research” in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Medical Physics.

An abstract for Matthew Muscat’s dissertation is included below.

Examinations are open to all members of the campus community as well as the general public. This examination will be offered in hybrid format.  Registration is not required to attend in person; however, please email andrew.jirasek@ubc.ca to receive the Zoom link for this exam.

Abstract

Prostate core-needle biopsies acquired during high-dose-rate (HDR) brachytherapy are clinically useful for diagnosis and can also support translational research when linked to sampled tissue, imaging-defined anatomy, and delivered dose. The main obstacle is that the effective biopsy location is uncertain by several millimetres because of registration error, contouring variability, needle placement uncertainty, and specimen-related effects. This thesis develops a probabilistic framework for mapping tissue, dose, and imaging data, including image information derived from multiparametric MRI (mpMRI), to econstructed prostate biopsy cores under explicit localization uncertainty.

Biopsies were represented as voxelized core segments in the clinical imaging and treatment-planning reference frame. A shared Monte Carlo localization model was used to propagate uncertainty into along-core tissue-class probabilities, voxel- and biopsy-level dose summaries, distributional dose volume histogram (DVH) metrics, and cohort-level robustness measures. The framework was first applied to estimate the probability that each position along a biopsy sampled dominant intraprostatic lesion and other nearby organs at risk. These tissue-assignment probabilities can be used for targeting quality assurance because they quantify how plausibly a biopsy sampled the intended imaging-defined target. They may also serve as imaging-derived covariates for testing associations with pathology and malignancy-related measures. The framework was then extended to biopsy-scale HDR dosimetry, showing that nominal assignments can differ materially from uncertainty-propagated quantities and that biopsy dose is heterogeneous along the core. At cohort level, DVH-style pass probabilities and robustness classes were derived under the localization model, yielding a probabilistic form of dosimetric quality assurance in which nominal distance from threshold emerged as the dominant determinant of high-confidence passing. Finally, Gaussian process regression was used to model along-core spatial correlation in dose, yielding more stable covariance-informed uncertainty summaries than independent voxel-wise Monte Carlo reporting alone.

In this setting, biopsy-linked tissue and dosimetry analysis is better framed as probabilistic spatial inference than as deterministic assignment. By parameterizing and propagating localization uncertainty, and by explicitly modelling along-core spatial correlation, the framework yields interpretable biopsy-scale descriptors that can support quality assurance, assay contextualization, and future dose-biology studies in prostate HDR brachytherapy.