Elena Cagna

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PURPOSE Development of user-friendly tools for the prediction of single-patient probability of late rectal toxicity after conformal radiotherapy for prostate cancer. METHODS AND MATERIALS This multicenter protocol was characterized by the prospective evaluation of rectal toxicity through self-assessed questionnaires (minimum follow-up, 36 months) by 718(More)
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(More)
We tested and compared performances of Roach formula, Partin tables and of three Machine Learning (ML) based algorithms based on decision trees in identifying N+ prostate cancer (PC). 1,555 cN0 and 50 cN+ PC were analyzed. Results were also verified on an independent population of 204 operated cN0 patients, with a known pN status (187 pN0, 17 pN1 patients).(More)
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