Dose-response of acute urinary toxicity of long-course preoperative chemoradiotherapy for rectal cancer

Abstract

Background. Long-course preoperative chemoradiotherapy (chemo-RT) improves outcomes for rectal cancer patients, but acute side effects during treatment may cause considerable patient discomfort and may compromise treatment compliance. We developed a dose-response model for acute urinary toxicity based on a large, single-institution series. Material and methods. In total 345 patients were treated with (chemo-)RT for primary rectal cancer from January 2007 to May 2012. Urinary toxicity during RT was scored prospectively using the CTCAE v 3.0 cystitis score (grade 0-5). Clinical variables and radiation dose to the bladder were related to graded toxicity using multivariate ordinal logistic regression. Three models were optimized, each containing all available clinical variables and one of three dose metrics: Mean dose (Dmean), equivalent uniform dose (EUD), or relative volume given x Gy or above (dose cut-off model, Vx). The optimal dose metric was chosen using the Akaike Information Criterion (AIC). Results. Grade 1 cystitis was experienced by 138 (40%), grade 2 by 39 (11%) and grade 3 by two (1%) patients, respectively. Dose metrics were significantly correlated with toxicity in all models, but the dose cut-off model provided the best AIC value. The only significant clinical risk factors in the Vx model were male gender (p = 0.006) and brachy-therapy boost (p = 0.02). Reducing the model to include gender, brachytherapy boost and Vx yielded odds ratios ORmale = 1.82 (1.17-2.80), ORbrachy = 1.36 (1.02-1.80 for each 5 Gy), x = 35.1 Gy (28.6-41.5 Gy). The predicted risk of grade 2 and above cystitis ranged from 2% to 26%. Conclusion. Acute cystitis correlated significantly with radiation dose to the bladder; the dose-cut-off model (V35Gy) was superior to Dmean and EUD models. Male gender and brachytherapy boost increased the risk of toxicity. Wide variation in predicted risks suggests room for treatment optimization using individual dose constraints.