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, D2%: near maximum dose, Dmean: 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 S21Gy for acute and S25Gy, 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. D2% 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.

中文翻译：

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对接受扫描质子束治疗的脑肿瘤患者的辐射诱发脱发风险进行建模

目的 为接受质子疗法 (PT) 治疗的脑肿瘤患者的辐射诱发脱发 (RIA) 开发正常组织并发症概率 (NTCP) 模型。方法和材料 我们分析了 116 名接受扫描束 PT（中位剂量 54 GyRBE；范围 36-72）的 CTCAE v.4 2 级（G2）急性（≤90 天）、晚期（>90 天）和永久性脑肿瘤成人患者（>12 个月）RIA。提取头皮的相对剂量表面直方图 (DSH) 并用于 Lyman-Kutcher-Burman (LKB) 建模。此外，DSH 指标（Sx：接受≥ X Gy 的表面，D2%：接近最大剂量，Dmean：平均剂量）和非剂量学变量包括在多变量逻辑回归 NTCP 模型中。通过受试者操作曲线下的交叉验证面积 (ROC-AUC) 评估模型性能。结果急性，分别在 52%、35% 和 19% 的患者中观察到晚期和永久性 G2-RIA。LKB 模型显示出较弱的剂量表面效应 (0.09 ≤ n ≤ 0.19)，相对陡度为 0.29 ≤ m ≤ 0.56，并且当从急性和晚期（22 和 24 GyRBE）转变为永久性 RIA（44 GyRBE）时，耐受剂量值增加. 多变量建模选择 S21Gy 用于急性期，S25Gy 用于晚期 G2-RIA 作为最具预测性的 DSH 因素。选择年轻作为急性 G2-RIA 的危险因素，而手术作为晚期 G2-RIA 的危险因素。D2% 是为永久性 G2-RIA 选择的唯一变量。LKB 和逻辑模型都表现出很高的预测性能（ROC-AUC 范围为 0.86-0.90）。结论我们推导出了 NTCP 模型来预测 PT 后的 G2-RIA，为急性、晚期和永久性事件提供了一个综合的建模框架，