Development of age-specific population-based paediatric computational phantoms for image-based data mining and other radiotherapy applications.

Autor:	Ahmad R; Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom., Cantwell J; Radiotherapy, University College London Hospitals NHS Foundation Trust, London, United Kingdom., Borrelli C; Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom., Lim P; Department of Oncology, University College London Hospitals NHS Foundation Trust, London, United Kingdom., D'Souza D; Radiotherapy Physics Services, University College London Hospitals NHS Foundation Trust, London, United Kingdom., Gaze MN; Department of Oncology, University College London Hospitals NHS Foundation Trust, London, United Kingdom., Moinuddin S; Radiotherapy, University College London Hospitals NHS Foundation Trust, London, United Kingdom., Gains J; Department of Oncology, University College London Hospitals NHS Foundation Trust, London, United Kingdom., Veiga C; Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom.
Jazyk:	angličtina
Zdroj:	Biomedical physics & engineering express [Biomed Phys Eng Express] 2024 Nov 13; Vol. 11 (1). Date of Electronic Publication: 2024 Nov 13.
DOI:	10.1088/2057-1976/ad8c4a
Abstrakt:	Objective. Computational anatomical models have many applications in paediatric radiotherapy. Age-specific computational anatomical models were historically developed to represent average and/or healthy individuals, where cancer patients may present with anatomical variations caused by the disease and/or treatment effects. We developed RT-PAL, a library of computational age-specific voxelized anatomical models tailored to represent the paediatric radiotherapy population. Approach. Data from patients undergoing craniospinal irradiation (CSI) were used (n = 74, median age 7.3y, range: 1-17y). The RT-PAL phantoms were generated using groupwise deformable image registration to spatially normalize and average a sub-set of twenty clinical CTs and contours (n = 74, median age 7.7y, range: 3-14 y). To assess their anatomical and age-dependency plausibility, the RT-PAL models were compared against clinical cancer patient data and two healthy population based libraries of phantoms: the International Commission on Radiological Protection (ICRP) pediatric reference computational phantoms (n = 8, median age 7.5y, range: 1-15y) and a range of 4D paediatric extended cardiac torso (XCAT) phantoms (n = 75, median age 9.1y, range: 1-18y). For each dataset, nineteen organs were segmented on all age models to determine their volume. Each set was evaluated through a linear fit of organ volume with age, where comparisons were made relative to the linear fit of the clinical data. Main Results. Overall good anatomical plausibility was found for the RT-PAL phantoms. The age-dependency reported was comparable to both the clinical data and other phantoms, demonstrating their efficacy as a library of age-specific phantoms. Larger discrepancies with the clinical, ICRP and XCAT organ data were attributable to differences in organ filling, segmentation strategy and age distribution of the datasets, limitations of RT-PAL generation methodology, and/or possible anatomical differences between healthy and cancer populations. Significance. The RT-PAL models showed potential in representing the paediatric radiotherapy cohort, who are most likely to benefit from dedicated, age-specific anatomical phantoms. (Creative Commons Attribution license.)
Databáze:	MEDLINE
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