Abstract

Dose and volume metrics to organs at risk are used for evaluation and optimization in radiotherapy planning. However, the numerous choices of metrics can be confusing. In a series of patients treated with hypofractionation and an integrated boost for breast cancer, we aim to determine if a parsimonious selection of representative metrics can be identified. The dosimetries of 42 patients receiving 42 Gy to the breast, with or without nodal irradiation, and 51 Gy integrated boost to tumor bed in 15 fractions were reviewed. For each organ—heart, lungs, and contralateral breast—cumulative dose-volume histograms were used to extract values for 3 basic metric classes:1.The dose D.yy (Gy) received by yy% of the organ volume.2.The percentage of organ volume Vrel.xx (%) receiving the dose of xx Gy.3.The absolute organ volume Vabs.xx (mL) receiving the dose of xx Gy.Two additional classes were considered:•The mean dose (Gy) received by the organ.•The dose standard deviation (Gy) received by the organ.Pearson correlation coefficient R was calculated between pairs of values within each basic class and with the 2 additional classes for each organ. The interquartile ranges of correlations for D.yy, Vrel.xx, and Vabs.xx were as follows:•Heart: 0.90-0.99, 0.50-0.92, and 0.58-0.93.•Ipsilateral lung: 0.81-0.98, 0.35-0.95, and 0.46-0.96.•Contralateral lung: 0.83-0.98, 0.71-1.00, and 0.65-1.00.•Contralateral breast: 0.74-0.97, 0.30-0.92, and 0.45-0.95.The mean dose correlated with all basic classes for the heart and lungs, and with dose D.yy and volumes at Vrel.10-Vabs.10 for the contralateral breast. The standard deviation correlated with Vrel.xx and Vabs.xx for the heart and lungs (R ≥ 0.70). Among the D.yy, D.50 (median dose) correlated with the mean and standard deviation for all organs (R = 0.65-0.96). The mean, standard deviation, and median doses were the preeminent correlators. These statistics appear to be parsimonious representatives of doses to organs. Further studies with other radiotherapy series will be necessary to validate these observations.

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