Predictive modelling analysis for development of a radiotherapy decision support system in prostate cancer: a preliminary study
- 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
- Issue Date
- 6월-2017
- Publisher
- CAMBRIDGE UNIV PRESS
- Keywords
- predictive modelling; prostate cancer; radiation treatment planning decision support program (PDSS); radiation treatment planning (RTP) system; toxicity
- Citation
- JOURNAL OF RADIOTHERAPY IN PRACTICE, v.16, no.2, pp.161 - 170
- Indexed
- SCOPUS
- Journal Title
- JOURNAL OF RADIOTHERAPY IN PRACTICE
- Volume
- 16
- Number
- 2
- Start Page
- 161
- End Page
- 170
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/83277
- DOI
- 10.1017/S1460396916000583
- ISSN
- 1460-3969
- 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.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - Graduate School > Department of Biomedical Sciences > 1. Journal Articles
- College of Medicine > Department of Medical Science > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.