Machine Learning in Prediction of Prostate Brachytherapy Rectal Dose Classes at Day 30

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Title: Machine Learning in Prediction of Prostate Brachytherapy Rectal Dose Classes at Day 30
Authors: Leydon, Patrick
Sullivan, Frank
Jamaluddin, Faisal
Woulfe, Peter
Greene, Derek
Curran, Kathleen M.
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Date: 28-Aug-2015
Online since: 2019-05-01T09:17:53Z
Abstract: A 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).
Funding Details: Irish Research Council
Type of material: Conference Publication
Publisher: Irish Pattern Recognition & Classification Society
Copyright (published version): 2015 the Authors
Keywords: BrachytherapyProstate cancerMachine learningClassificationWeka
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Language: en
Status of Item: Peer reviewed
Is part of: Dahyot, R., Lacey, G., Dawson-Howe, K., Pitié, F., Moloney, D. (eds.). Irish Machine Vision and Image Processing Conference Proceedings 2015
Conference Details: The Irish Machine Vision and Image Processing Conference (IMVIP 2015), Dublin, Ireland, 26-28 August 2015
ISBN: 978-0-9934207-0-2
Appears in Collections:Computer Science Research Collection
Medicine Research Collection
Insight Research Collection

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