Predicting Radiotherapy Impact on Late Bladder Toxicity in Prostate Cancer Patients: An Observational Study.

Abstract

Simple SummaryProstate cancer (PC) is the most common cancer in men over 70 years old, with heterogeneous characteristics and, nowadays, multiple treatment strategies obtaining optimal outcomes and improving survival. Personalized medicine and even more morbidity treatment reduction are increasing needs. The development of decision support systems to personalize treatments is a key challenge in oncology. The study PRODIGE 1.1 aimed to elaborate on a model analyzing dosimetric parameters in Prostate Cancer patients treating by radiation therapy, predicting late bladder toxicity in order to customize treatments and reduce late morbidity.Background and purpose: The aim of our study was to elaborate a suitable model on bladder late toxicity in prostate cancer (PC) patients treated by radiotherapy with volumetric technique. Materials and methods: PC patients treated between September 2010 and April 2017 were included in the analysis. An observational study was performed collecting late toxicity data of any grade, according to RTOG and CTCAE 4.03 scales, cumulative dose volumes histograms were exported for each patient. Vdose, the value of dose to a specific volume of organ at risk (OAR), impact was analyzed through the Mann–Whitney rank-sum test. Logistic regression was used as the final model. The model performance was estimated by taking 1000 samples with replacement from the original dataset and calculating the AUC average. In addition, the calibration plot (Hosmer–Lemeshow goodness-of-fit test) was used to evaluate the performance of internal validation. RStudio Software version 3.3.1 and an in house developed software package “Moddicom” were used. Results: Data from 175 patients were collected. The median follow-up was 39 months (min–max 3.00–113.00). We performed Mann–Whitney rank-sum test with continuity correction in the subset of patients with late bladder toxicity grade ≥ 2: a statistically significant p-value with a Vdose of 51.43 Gy by applying a logistic regression model (coefficient 4.3, p value 0.025) for the prediction of the development of late G ≥ 2 GU toxicity was observed. The performance for the model’s internal validation was evaluated, with an AUC equal to 0.626. Accuracy was estimated through the elaboration of a calibration plot. Conclusions: Our preliminary results could help to optimize treatment planning procedures and customize treatments.