Predictive modelling analysis for development of a radiotherapy decision support system in prostate cancer: a preliminary study

Kwang Hyeon Kim, Suk Lee, Jang Bo Shim, Kyung Hwan Chang, Yuanjie Cao, Suk Woo Choi, Se Hyeong Jeon, Dae-Sik Yang, Won Sup Yoon, Young Je Park, Chul Yong Kim

Research output: Contribution to journalArticle

3 Citations (Scopus)

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 TomoTherapyTM 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.

Original languageEnglish
Pages (from-to)1-10
Number of pages10
JournalJournal of Radiotherapy in Practice
DOIs
Publication statusAccepted/In press - 2017 Jan 31

Keywords

  • predictive modelling
  • prostate cancer
  • radiation treatment planning (RTP) system
  • radiation treatment planning decision support program (PDSS)
  • toxicity

ASJC Scopus subject areas

  • Oncology
  • Radiology Nuclear Medicine and imaging

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    Kim, K. H., Lee, S., Shim, J. B., Chang, K. H., Cao, Y., Choi, S. W., Jeon, S. H., Yang, D-S., Yoon, W. S., Park, Y. J., & Kim, C. Y. (Accepted/In press). Predictive modelling analysis for development of a radiotherapy decision support system in prostate cancer: a preliminary study. Journal of Radiotherapy in Practice, 1-10. https://doi.org/10.1017/S1460396916000583