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Two non-parametric methods for derivation of constraints from radiotherapy dose-histogram data.
Phys. Med. Biol. 59, N101-N111 (2014)
Dose constraints based on histograms provide a convenient and widely-used method for informing and guiding radiotherapy treatment planning. Methods of derivation of such constraints are often poorly described. Two non-parametric methods for derivation of constraints are described and investigated in the context of determination of dose-specific cut-points-values of the free parameter (e.g., percentage volume of the irradiated organ) which best reflect resulting changes in complication incidence. A method based on receiver operating characteristic (ROC) analysis and one based on a maximally-selected standardized rank sum are described and compared using rectal toxicity data from a prostate radiotherapy trial. Multiple test corrections are applied using a free step-down resampling algorithm, which accounts for the large number of tests undertaken to search for optimal cut-points and the inherent correlation between dose-histogram points. Both methods provide consistent significant cut-point values, with the rank sum method displaying some sensitivity to the underlying data. The ROC method is simple to implement and can utilize a complication atlas, though an advantage of the rank sum method is the ability to incorporate all complication grades without the need for grade dichotomization.
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Publikationstyp
Artikel: Journalartikel
Dokumenttyp
Wissenschaftlicher Artikel
Schlagwörter
Radiotherapy ; Dose Volume Histograms ; Treatment Complications ; Toxicity ; Constraints; Late Rectal Toxicity; 03.04 Radar Trial; Prostate-cancer; Conformal Radiotherapy; Volume Histograms; Optimal Cutpoints; Statistics; Grade-2; System; Impact
ISSN (print) / ISBN
0031-9155
e-ISSN
1361-6560
Zeitschrift
Physics in Medicine and Biology
Quellenangaben
Band: 59,
Heft: 13,
Seiten: N101-N111
Verlag
Institute of Physics Publishing (IOP)
Verlagsort
Bristol
Begutachtungsstatus
Peer reviewed
Institut(e)
Institute of Computational Biology (ICB)