THE BENEFITS OF INCLUDING CLINICAL FACTORS IN RECTAL NORMAL TISSUE COMPLICATION PROBABILITY MODELING AFTER RADIOTHERAPY FOR PROSTATE CANCER
THE BENEFITS OF INCLUDING CLINICAL FACTORS IN RECTAL NORMAL TISSUE COMPLICATION PROBABILITY MODELING AFTER RADIOTHERAPY FOR PROSTATE CANCER
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DOI：
10.1016/j.ijrobp.2011.03.056
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发表时间：
2012-03-01
影响因子：
7
通讯作者：
Lebesque, Joos V.
中科院分区：
文献类型：
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作者：
Defraene, Gilles;Van den Bergh, Laura;Lebesque, Joos V.
目的：研究直肠正常组织并发症的概率建模的荷兰前列腺dose-escalation trial.Methods和材料的更新结果的临床诱发因素的影响：512例患者（共治疗68戈伊[n = 284]和78戈伊[n = 228]）的毒性数据进行了研究，在放疗后3年完成随访。评分终点为直肠出血、大便次数增多和大便失禁。使用最大似然法拟合两种传统的基于剂量的模型（Lyman-Kutcher-Burman（LKB）和相对序列性（RS））和logistic模型。此外，通过纳入最重要的临床因素，这些模型拟合得到了改善。的受试者工作特征曲线（AUC）下的面积被用来比较所有fits.Results的辨别能力：包括临床因素显着增加了所有端点的模型的预测能力。在直肠出血和大便失禁的最佳LKB、RS和Logistic模型中，第一个显著（p = 0.011-0.013）临床因素是“既往腹部手术”。"作为第二个显著（p = 0.012-0.016）因素，“心脏病史”被包括在所有三个直肠出血拟合中，而包括“糖尿病”在大便失禁建模中是显著的（p = 0.039-0.048），但仅在LKB和逻辑模型中。高排便频率拟合仅从纳入基线毒性评分中显著获益（p = 0.003-0.006）。对于所有模型，直肠出血拟合具有最高的AUC（0.77），其中高大便频率和大便失禁的AUC分别为0.63和0.68。LKB和逻辑模型拟合导致体积参数的相似值。logistic模型中的陡度参数略高，也导致D-50略低。肛门壁DVH用于大便失禁，而肛门直肠壁剂量最好的描述其他两个endpoint.Conclusions：可比预测模型与LKB，RS和逻辑NTCP模型。包括临床因素显著提高了所有模型的预测能力。(C)2012 Elsevier Inc.
Purpose: To study the impact of clinical predisposing factors on rectal normal tissue complication probability modeling using the updated results of the Dutch prostate dose-escalation trial.Methods and Materials: Toxicity data of 512 patients (conformally treated to 68 Gy [n = 284] and 78 Gy [n = 228]) with complete follow-up at 3 years after radiotherapy were studied. Scored end points were rectal bleeding, high stool frequency, and fecal incontinence. Two traditional dose-based models (Lyman-Kutcher-Burman (LKB) and Relative Seriality (RS) and a logistic model were fitted using a maximum likelihood approach. Furthermore, these model fits were improved by including the most significant clinical factors. The area under the receiver operating characteristic curve (AUC) was used to compare the discriminating ability of all fits.Results: Including clinical factors significantly increased the predictive power of the models for all end points. In the optimal LKB, RS, and logistic models for rectal bleeding and fecal incontinence, the first significant (p = 0.011-0.013) clinical factor was "previous abdominal surgery.'' As second significant (p = 0.012-0.016) factor, "cardiac history'' was included in all three rectal bleeding fits, whereas including "diabetes'' was significant (p = 0.039-0.048) in fecal incontinence modeling but only in the LKB and logistic models. High stool frequency fits only benefitted significantly (p = 0.003-0.006) from the inclusion of the baseline toxicity score. For all models rectal bleeding fits had the highest AUC (0.77) where it was 0.63 and 0.68 for high stool frequency and fecal incontinence, respectively. LKB and logistic model fits resulted in similar values for the volume parameter. The steepness parameter was somewhat higher in the logistic model, also resulting in a slightly lower D-50. Anal wall DVHs were used for fecal incontinence, whereas anorectal wall dose best described the other two endpoints.Conclusions: Comparable prediction models were obtained with LKB, RS, and logistic NTCP models. Including clinical factors improved the predictive power of all models significantly. (C) 2012 Elsevier Inc.