Predictive modelling analysis for development of a radiotherapy decision support system in prostate cancer: a preliminary study. Issue 2 (31st January 2017)

• Record Type: Journal Article
• Authors: Kim, Kwang Hyeon; Lee, Suk; Shim, Jang Bo; Chang, Kyung Hwan; Cao, Yuanjie; Choi, Suk Woo; Jeon, Se Hyeong; Yang, Dae Sik; Yoon, Won Sup; Park, Young Je; Kim, Chul Yong
• Is Part Of: Journal of radiotherapy in practice. Volume 16:Issue 2(2016)
• Page Start: 161
• Page End: 170
• Publication Date: 2017-01-31
• Subjects: predictive modelling, -- prostate cancer, -- radiation treatment planning decision support program (PDSS), -- radiation treatment planning (RTP) system, -- toxicity; Radiotherapy -- Periodicals; 615.842005
• DOI: 10.1017/S1460396916000583
• Languages: English
• ISSNs: 1460-3969

Abstract

Abstract: Purpose: The aim of this study is to develop predictive models to predict organ at risk (OAR) complication level, classification of OAR dose-volume and combination of this function with our in-house developed treatment decision support system. Materials and methods: We analysed the support vector machine and decision tree algorithm for predicting OAR complication level and toxicity in order to integrate this function into our in-house radiation treatment planning decision support system. A total of 12 TomoTherapy TM treatment plans for prostate cancer were established, and a hundred modelled plans were generated to analyse the toxicity prediction for bladder and rectum. Results: The toxicity prediction algorithm analysis showed 91·0% accuracy in the training process. A scatter plot for bladder and rectum was obtained by 100 modelled plans and classification result derived. OAR complication level was analysed and risk factor for 25% bladder and 50% rectum was detected by decision tree. Therefore, it was shown that complication prediction of patients using big data-based clinical information is possible. Conclusion: We verified the accuracy of the tested algorithm using prostate cancer cases. Side effects can be minimised by applying this predictive modelling algorithm with the planning decision support system for patient-specific radiotherapy planning.