期刊
RADIOTHERAPY AND ONCOLOGY
卷 144, 期 -, 页码 127-134出版社
ELSEVIER IRELAND LTD
DOI: 10.1016/j.radonc.2019.11.013
关键词
Radiation-induced alopecia; Proton therapy; Brain tumors; NTCP
资金
- Italian National Institute for Nuclear Physics (INFN) CSN5 Call MoVe-IT
Purpose: To develop normal tissue complication probability (NTCP) models for radiation-induced alopecia (RIA) in brain tumor patients treated with proton therapy (PT). Methods and materials: We analyzed 116 brain tumor adult patients undergoing scanning beam PT (median dose 54 GyRBE; range 36-72) for CTCAE v.4 grade 2 (G2) acute (<= 90 days), late (>90 days) and permanent (>12 months) RIA. The relative dose-surface histogram (DSH) of the scalp was extracted and used for Lyman-Kutcher-Burman (LKB) modelling. Moreover, DSH metrics (Sx: the surface receiving >= X Gy, D-2%: near maximum dose, D-mean: mean dose) and non-dosimetric variables were included in a multivariable logistic regression NTCP model. Model performances were evaluated by the cross-validated area under the receiver operator curve (ROC-AUC). Results: Acute, late and permanent G2-RIA was observed in 52%, 35% and 19% of the patients, respectively. The LKB models showed a weak dose-surface effect (0.09 <= n <= 0.19) with relative steepness 0.29 <= m <= 0.56, and increasing tolerance dose values when moving from acute and late (22 and 24 GyRBE) to permanent RIA (44 GyRBE). Multivariable modelling selected S-21Gy for acute and S-25Gy, for late G2-RIA as the most predictive DSH factors. Younger age was selected as risk factor for acute G2-RIA while surgery as risk factor for late G2-RIA. D-2% was the only variable selected for permanent G2-RIA. Both LKB and logistic models exhibited high predictive performances (ROC-AUCs range 0.86-0.90). Conclusion: We derived NTCP models to predict G2-RIA after PT, providing a comprehensive modelling framework for acute, late and permanent occurrences that, once externally validated, could be exploited for individualized scalp sparing treatment planning strategies in brain tumor patients. (C) 2019 Elsevier B.V. All rights reserved.
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