Integrated models for the prediction of late genitourinary complaints after high-dose intensity modulated radiotherapy for prostate cancer: Making informed decisions

Published on Jul 1, 2014in Radiotherapy and Oncology4.856
· DOI :10.1016/J.RADONC.2014.04.005
Sofie De Langhe5
Estimated H-index: 5
(UGent: Ghent University),
Gert De Meerleer36
Estimated H-index: 36
(Ghent University Hospital)
+ 4 AuthorsHubert Thierens61
Estimated H-index: 61
(UGent: Ghent University)
Abstract 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 ⩾75Gy, 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.
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