Evaluating principal component analysis models for representing anatomical changes in head and neck radiotherapy

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  • Raul Argota-Perez
  • Jennifer Robbins, University of Manchester
  • ,
  • Andrew Green, University of Manchester
  • ,
  • Marcel van Herk, University of Manchester
  • ,
  • Stine Korreman
  • Eliana Vásquez-Osorio, University of Manchester

Background and purpose: Anatomical changes during radiotherapy pose a challenge to robustness of plans. Principal component analysis (PCA) is commonly used to model such changes. We propose a toolbox to evaluate how closely a given PCA model can represent actual deformations seen in the patient and highlight regions where the model struggles to capture these changes. Materials and methods: We propose to calculate a residual error map from the difference between an actual displacement vector field (DVF) and the closest DVF that the PCA model can produce. This was done by taking the inner product of the DVF with the PCA components from the model. As a global measure of error, the 90th percentile of the residual errors (Mres90) across the whole scan was used. As proof of principle, we demonstrated this approach on both patient-specific cases and a population-based PCA in head and neck (H&N) cancer patients. These models were created using deformation data from deformable registrations between the planning computed tomography and cone-beam computed tomography (CBCTs), and were evaluated against DVFs from registrations of CBCTs not used to create the model. Results: For our example cases, the oropharyngeal and the nasal cavity regions showed the largest local residual error, indicating the PCA models struggle to predict deformations seen in these regions. Mres90 ranged from 0.4 mm to 6.3 mm across the different models. Conclusions: A method to quantitatively evaluate how well PCA models represent observed anatomical changes was proposed. We demonstrated our approach on H&N PCA models, but it can be applied to other sites.

TidsskriftPhysics and Imaging in Radiation Oncology
Sider (fra-til)13-19
Antal sider7
StatusUdgivet - apr. 2022

Bibliografisk note

Funding Information:
This work was supported by The Danish Cancer Society (grant no R167-A11003), DCCC-RT - The Danish National Research Centre for Radiotherapy, Danish Cancer Society (grant no. R191-A11526) and Danish Comprehensive Cancer Centre, and by Aarhus University Research Foundation (grant no AUFF-F-2016-FLS-8-4 HD). This work was supported by Cancer Research UK via funding to the Cancer Research Manchester Centre [C147/A25254]. Marcel van Herk was supported by NIHR Manchester Biomedical Research Centre. We kindly acknowledge Dr. Kenneth Jensen, DCPT, for his help with collecting the patient data. We would like to thank Rebecca Holley for helping reduce the word count in the final manuscript.

Publisher Copyright:
© 2022 The Author(s)

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