  High-grade serous ovarian carcinoma (HGSOC) is characterised by significant
spatial and temporal heterogeneity, typically manifesting at an advanced
metastatic stage. A major challenge in treating advanced HGSOC is effectively
monitoring localised change in tumour burden across multiple sites during
neoadjuvant chemotherapy (NACT) and predicting long-term pathological response
and overall patient survival. In this work, we propose a self-supervised
deformable image registration algorithm that utilises a general-purpose image
encoder for image feature extraction to co-register contrast-enhanced
computerised tomography scan images acquired before and after neoadjuvant
chemotherapy. This approach addresses challenges posed by highly complex tumour
deformations and longitudinal lesion matching during treatment. Localised
tumour changes are calculated using the Jacobian determinant maps of the
registration deformation at multiple disease sites and their macroscopic areas,
including hypo-dense (i.e., cystic/necrotic), hyper-dense (i.e., calcified),
and intermediate density (i.e., soft tissue) portions. A series of experiments
is conducted to understand the role of a general-purpose image encoder and its
application in quantifying change in tumour burden during neoadjuvant
chemotherapy in HGSOC. This work is the first to demonstrate the feasibility of
a self-supervised image registration approach in quantifying NACT-induced
localised tumour changes across the whole disease burden of patients with
complex multi-site HGSOC, which could be used as a potential marker for ovarian
cancer patient's long-term pathological response and survival.
