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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: [email protected]
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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
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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References

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): 119–136.Google Scholar
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): 1617–1626.Google Scholar
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): 442–451.Google Scholar
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): 285–289.CrossRefGoogle ScholarPubMed
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): 26–32.Google Scholar
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: 6357–6361.Google Scholar
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): 232–240.CrossRefGoogle ScholarPubMed
8. Mohammed, J Z, Wagner Meira, J R. Data Mining and Analysis. England: Cambridge University Press, 2014.Google Scholar
9. El Naqa, I, Li, R, Murphy, M J. Machine Learning in Radiation Oncology: Theory and Applications, 2nd edition. Switzerland: Springer, 2015.Google Scholar
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): 27–40.Google Scholar
11. Sanchez-Nieto, B, Nahum, A E. BIOPLAN: software for the biological evaluation of radiation therapy. Med Dosim 2000; 25 (2): 71–76.Google Scholar
12. Pinter, C, Lasso, A, Wang, A, Jaffray, D, Fichtinger, G. SlicerRT: radiation therapy research toolkit for 3D Slicer. Med Phys 2012; 39 (10): 6332–6338.Google Scholar
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): S3–S9.Google Scholar
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.Google Scholar
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): 1127–1135.Google Scholar
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: 30346–30349.Google Scholar
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.CrossRefGoogle ScholarPubMed
18. Coates, J, Souhami, L, El Naqa, I. Big data analytics for prostate radiotherapy. Front Oncol 2016; 6: 149.CrossRefGoogle ScholarPubMed