Late rectal bleeding after 3D-CRT for prostate cancer: development of a neural-network-based predictive model.

The aim of this study was to develop a model exploiting artificial neural networks (ANNs) to correlate dosimetric and clinical variables with late rectal bleeding in prostate cancer patients undergoing radical radiotherapy and to compare the ANN results with those of a standard logistic regression (LR) analysis. 718 men included in the AIROPROS 0102 trial were analyzed. This multicenter protocol was characterized by the prospective evaluation of rectal toxicity, with a minimum follow-up of 36 months. Radiotherapy doses were between 70 and 80 Gy. Information was recorded for comorbidity, previous abdominal surgery, use of drugs and hormonal therapy. For each patient, a rectal dose-volume histogram (DVH) of the whole treatment was recorded and the equivalent uniform dose (EUD) evaluated as an effective descriptor of the whole DVH. Late rectal bleeding of grade ≥ 2 was considered to define positive events in this study (52 of 718 patients). The overall population was split into training and verification sets, both of which were involved in model instruction, and a test set, used to evaluate the predictive power of the model with independent data. Fourfold cross-validation was also used to provide realistic results for the full dataset. The LR was performed on the same data. Five variables were selected to predict late rectal bleeding: EUD, abdominal surgery, presence of hemorrhoids, use of anticoagulants and androgen deprivation. Following a receiver operating characteristic analysis of the independent test set, the areas under the curves (AUCs) were 0.704 and 0.655 for ANN and LR, respectively. When evaluated with cross-validation, the AUC was 0.714 for ANN and 0.636 for LR, which differed at a significance level of p = 0.03. When a practical discrimination threshold was selected, ANN could classify data with sensitivity and specificity both equal to 68.0%, whereas these values were 61.5% for LR. These data provide reasonable evidence that results obtained with ANNs are superior to those achieved with LR when predicting late radiotherapy-related rectal bleeding. The future introduction of patient-related personal characteristics, such as gene expression profiles, might improve the predictive power of statistical classifiers. More refined morphological aspects of the dose distribution, such as dose surface mapping, might also enhance the overall performance of ANN-based predictive models.