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

Published online by Cambridge University Press:  31 January 2017

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  and
Young Je Park
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Kwang Hyeon Kim
Affiliation:
Department of Biomedical Science, Graduate School of Korea University, Seoul, South Korea Department of Radiation Oncology, College of Medicine, Korea University, Seoul, South Korea
Suk Lee*
Affiliation:
Department of Radiation Oncology, College of Medicine, Korea University, Seoul, South Korea
Jang Bo Shim
Affiliation:
Department of Radiation Oncology, College of Medicine, Korea University, Seoul, South Korea
Kyung Hwan Chang
Affiliation:
CQURE Healthcare, Seoul, South Korea
Yuanjie Cao
Affiliation:
Innotems, Daejeon, South Korea
Suk Woo Choi
Affiliation:
Asan Medical Center, Seoul, South Korea
Se Hyeong Jeon
Affiliation:
Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
Dae Sik Yang
Affiliation:
Department of Radiation Oncology, College of Medicine, Korea University, Seoul, South Korea
Won Sup Yoon
Affiliation:
Department of Radiation Oncology, College of Medicine, Korea University, Seoul, South Korea
Young Je Park
Affiliation:
Department of Radiation Oncology, College of Medicine, Korea University, Seoul, South Korea
Chul Yong Kim
Affiliation:
Department of Radiation Oncology, College of Medicine, Korea University, Seoul, South Korea
*
Correspondence to: Suk Lee, PhD, Department of Radiation Oncology, Korea University Medical Center, 126-1, Anamdong, Seongbukgu, 02841 Seoul, Korea. Tel: +82-2-920-5519, Fax: +82-2-927-1419, E-mail: sukmp@korea.ac.kr
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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.

Keywords

predictive modellingprostate cancerradiation treatment planning decision support program (PDSS)radiation treatment planning (RTP) systemtoxicity

Information

Type
Original Articles
Information
Journal of Radiotherapy in Practice , Volume 16 , Issue 2 , June 2017 , pp. 161 - 170
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Copyright
© Cambridge University Press 2017 

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