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A Self-supervised Image Registration Approach for Measuring Local Response Patterns in Metastatic Ovarian Cancer

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Biomedical Image Registration (WBIR 2024)

Abstract

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.

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Notes

  1. 1.

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Acknowledgements

We acknowledge funding and support from Cancer Research UK (A22905) and the Cancer Research UK Cambridge Centre [CTRQQR-2021-100012 and A25177], The Mark Foundation for Cancer Research [RG95043], GE HealthCare, and the CRUK National Cancer Imaging Translational Accelerator (NCITA) [A27066]. Additional support was provided by the National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre [NIHR203312 and BRC-1215-20014] and the EPSRC Tier-2 capital grant [EP/T022221/1]. This work was further supported by the backing of the Federal Ministry of Education and Research (BMBF, Grant Nos. 01ZZ2315B and 01KX2021), the Bavarian Cancer Research Center (BZKF, Lighthouse AI and Bioinformatics), and the German Cancer Consortium (DKTK, Joint Imaging Platform). Further support was received through the St. Baldrick’s Career Development Grant, NIH R61NS126792, NIH R21TR004265, and NIH R21NS21735. Additionally, we recognize the contribution of the Helmholtz Information and Data Science Academy. The authors would like to acknowledge the support of the Scientific Computing team from the Cancer Research UK Cambridge Institute.

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Correspondence to Inês P. Machado .

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Machado, I.P. et al. (2024). A Self-supervised Image Registration Approach for Measuring Local Response Patterns in Metastatic Ovarian Cancer. In: Modat, M., Simpson, I., Špiclin, Ž., Bastiaansen, W., Hering, A., Mok, T.C.W. (eds) Biomedical Image Registration. WBIR 2024. Lecture Notes in Computer Science, vol 15249. Springer, Cham. https://doi.org/10.1007/978-3-031-73480-9_23

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  • DOI: https://doi.org/10.1007/978-3-031-73480-9_23

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