Leydon, PatrickPatrickLeydonSullivan, FrankFrankSullivanJamaluddin, FaisalFaisalJamaluddinWoulfe, PeterPeterWoulfeGreene, DerekDerekGreeneCurran, Kathleen M.Kathleen M.Curran2019-05-012019-05-012015 the A2015-08-28978-0-9934207-0-2http://hdl.handle.net/10197/10237The Irish Machine Vision and Image Processing Conference (IMVIP 2015), Dublin, Ireland, 26-28 August 2015A retrospective analysis of brachytherapy implant data was carried out on 351 patients that underwent permanent I125 brachytherapy for treatment of low-risk prostate cancer. For each patient, the dose received by 2cm3 of the rectum (D2cc) 30 days post implant was defined as belonging one of two classes, ”Low” and ”High” depending on whether or not it was above or below a particular dose threshold. The aim of the study was to investigate the application of a number of machine learning classification techniques to intra-operative implant dosimetry data for prediction of rectal dose classes determined 30 days post implant. Algorithm performance was assessed in terms of its true and false positive rates and Receiver Operator Curve area based on a 10-fold cross validation procedure using Weka software. This was repeated for a variety of dose class thresholds to determine the point at which the highest accuracy was achieved. The highest ROC areas were observed at a threshold of D2cc = 90 Gy, with the highest area achieved by Bayes Net (0.943). At more clinically useful thresholds of D2cc = 145 Gy, classification was less reliable, with the highest ROC area achieved by Bayes Net (0.613).enThis work is distributed free of charge by the Irish Pattern Recognition & Classification Society on behalf of the organisers of the Irish Machine Vision and Image Processing Conference, and the contributing authors to this conference. Both organisers and authors own the rights of their contributions to this book.BrachytherapyProstate cancerMachine learningClassificationWekaConference Publication2018-11-16https://creativecommons.org/licenses/by-nc-nd/3.0/ie/