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

  • Kwang Hyeon Kim (a1) (a2), Suk Lee (a2), Jang Bo Shim (a2), Kyung Hwan Chang (a3), Yuanjie Cao (a4), Suk Woo Choi (a5), Se Hyeong Jeon (a6), Dae Sik Yang (a2), Won Sup Yoon (a2), Young Je Park (a2) and Chul Yong Kim (a2)...



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.


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.


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.


Corresponding author

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:


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1. Meldolesi, E, van Soest, J, Damiani, A et al. Standardized data collection to build rediction models in oncology: a prototype for rectal cancer. Future Oncol 2016; 12 (1): 119136.
2. Zhang, H H, D’Souza, W D, Shi, L, Meyer, R R. Modeling plan-related clinical complications using machine learning tools in a multiplan IMRT framework. Int J Radiat Oncol Biol Phys 2009; 74 (5): 16171626.
3. Guidi, G, Maffei, N, Vecchi, C et al. A support vector machine tool for adaptive tomotherapy treatments: prediction of head and neck patients criticalities. Phys Med 2015; 31 (5): 442451.
4. Cao, Y J, Lee, S, Chang, K H et al. Patient performance-based plan parameter optimization for prostate cancer in tomotherapy. Med Dosim 2015; 40 (4): 285289.
5. Cao, Y J, Lee, S, Chang, K H et al. Optimized planning target volume margin in helical tomotherapy for prostate cancer: is there a preferred method? J Kor Phys 2015; 67 (1): 2632.
6. Çınar, M, Engin, M, Engin, E Z, Atesçi, Y Z. Early prostate cancer diagnosis by using artificial neural networks and support vector machines. Exp Sys App 2009; 36: 63576361.
7. De Bari, B, Vallati, M, Gatta, R et al. Could machine learning improve the prediction of pelvic nodal status of prostate cancer patients? Preliminary results of a pilot study. Cancer Invest 2015; 33 (6): 232240.
8. Mohammed, J Z, Wagner Meira, J R. Data Mining and Analysis. England: Cambridge University Press, 2014.
9. El Naqa, I, Li, R, Murphy, M J. Machine Learning in Radiation Oncology: Theory and Applications, 2nd edition. Switzerland: Springer, 2015.
10. Lambin, P, van Stiphout, R G, Starmans, M H et al. Predicting outcomes in radiation oncology – multifactorial decision support systems. Nat Rev Clin Oncol 2015; 10 (1): 2740.
11. Sanchez-Nieto, B, Nahum, A E. BIOPLAN: software for the biological evaluation of radiation therapy. Med Dosim 2000; 25 (2): 7176.
12. Pinter, C, Lasso, A, Wang, A, Jaffray, D, Fichtinger, G. SlicerRT: radiation therapy research toolkit for 3D Slicer. Med Phys 2012; 39 (10): 63326338.
13. Bentzen, S M, Constine, L S, Deasy, J O et al. Quantitative analyses of normal tissue effects in the clinic (QUANTEC): an introduction to the scientific issues. Int J Radiat Oncol Biol Phys 2010; 76 (3): S3S9.
14. El Naqa, I, Bradley, J D, PE, L, Hope, A J, Deasy, J O. Predicting radiotherapy outcomes using statistical learning techniques. Phys Med Biol 2009; 54 (18): S9.
15. Kang, J, Schwartz, R, Flickinger, J, Beriwal, S. Machine learning approaches for predicting radiation therapy outcomes: a clinician’s perspective. Int J Radiat Oncol Biol Phys 2015; 93 (5): 11271135.
16. Bibault, J E, Giraud, P, Burgun, A. Big data and machine learning in radiation oncology: state of the art and future prospects. Cancer Lett 2016; 16: 3034630349.
17. Trifiletti, D M, Showalter, T N. Big data and comparative effectiveness research in radiation oncology: synergy and accelerated discovery. Front Oncol 2015; 5: 274.
18. Coates, J, Souhami, L, El Naqa, I. Big data analytics for prostate radiotherapy. Front Oncol 2016; 6: 149.



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