Integrated models for the prediction of late genitourinary complaints after high-dose intensity modulated radiotherapy for prostate cancer: Making informed decisions
Integrated models for the prediction of late genitourinary complaints after high-dose intensity modulated radiotherapy for prostate cancer: Making informed decisions
复制标题
DOI：
10.1016/j.radonc.2014.04.005
复制
发表时间：
2014-07-01
影响因子：
5.7
通讯作者：
Thierens, Hubert
中科院分区：
文献类型：
--
作者：
De Langhe, Sofie;De Meerleer, Gert;Thierens, Hubert
背景和目的：为了开发预测模型，晚期放射性血尿和尿失禁，允许患者个性化的估计治疗前risk.Materials和方法：我们研究了262例PCa患者治疗与治疗性调强放疗的完整前列腺或前列腺床。共使用372个变量进行预测建模，其中343个遗传变异。使用内部开发的毒性量表对毒性进行评分。预测因子选择通过EMLasso程序实现，这是一种惩罚逻辑回归方法，使用EM算法处理缺失数据和交叉验证，避免过拟合。模型的性能表示的曲线下面积（AUC）和灵敏度和specificity.Results：模型预测晚期血尿（36/262）的变量是膀胱体积接收>= 75戈伊，前列腺经尿道切除术和四个多态性。(AUC= 0.80，灵敏度= 83.3%，特异性= 61.5%）。当遗传标记被排除时，AUC下降到0.67。预测晚期乳腺癌的模型（29/262）包含最小临床靶体积（CTV）剂量、CTV体积和三个多态性（AUC = 0.76，灵敏度= 75.9%，特异性= 67.4%）。与非遗传模型（AUC为0.60）相比，该模型是一个更好的预测指标。结论：我们能够建立模型，预测晚期辐射诱导的血尿和血尿的发生，包括遗传因素，这可能会提高晚期泌尿毒性的预测。(C)2014爱思唯尔爱尔兰有限公司版权所有。
Background and purpose: To develop predictive models for late radiation-induced hematuria and nocturia allowing a patient individualized estimation of pre-treatment risk.Materials and methods: We studied 262 PCa patients treated with curative intensity modulated radiotherapy to the intact prostate or prostate bed. A total of 372 variables were used for prediction modeling, among which 343 genetic variations. Toxicity was scored using an in-house developed toxicity scale. Predictor selection is achieved by the EMLasso procedure, a penalized logistic regression method with an EM algorithm handling missing data and crossvalidation avoiding overfit. Model performance was expressed by the area under the curve (AUC) and by sensitivity and specificity.Results: Variables of the model predicting late hematuria (36/262) are bladder volume receiving >= 75 Gy, prostatic transurethral resection and four polymorphisms. (AUC = 0.80, sensitivity = 83.3%, specificity = 61.5%). The AUC drops to 0.67 when the genetic markers are left out. The model that predicts for late nocturia (29/262) contains the minimal clinical target volume (CTV) dose, the CTV volume and three polymorphisms (AUC = 0.76, sensitivity = 75.9%, specify = 67.4%). This model is a better predictor for nocturia compared to the nongenetic model (AUC of 0.60).Conclusions: We were able to develop models that predict for the occurrence of late radiation-induced hematuria and nocturia, including genetic factors which might improve the prediction of late urinary toxicity. (C) 2014 Elsevier Ireland Ltd. All rights reserved.