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

R. Argota Perez | Zendy; Jennifer Robbins | Zendy; Andrew Green | Zendy; Marcel van Herk | Zendy; S. Korreman | Zendy; Eliana Vásquez-Osorio | Zendy

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

Author(s) -

R. Argota Perez,

Jennifer Robbins,

Andrew Green,

Marcel van Herk,

S. Korreman,

Eliana Vásquez-Osorio

Publication year - 2022

Publication title -

physics and imaging in radiation oncology

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.777

H-Index - 12

ISSN - 2405-6316

DOI - 10.1016/j.phro.2022.04.002

Subject(s) - principal component analysis , residual , artificial intelligence , percentile , robustness (evolution) , pattern recognition (psychology) , computer science , image registration , head and neck , nuclear medicine , mathematics , statistics , medicine , algorithm , surgery , biology , biochemistry , image (mathematics) , gene

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.

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