Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30850
Applications of Artificial Neural Network to Building Statistical Models for Qualifying and Indexing Radiation Treatment Plans

Authors: Tsair-Fwu Lee, Pei-Ju Chao, Long-Chang Chen, Wei-Luen Huang, Te-Jen Su, Wen-Ping Chen


The main goal in this paper is to quantify the quality of different techniques for radiation treatment plans, a back-propagation artificial neural network (ANN) combined with biomedicine theory was used to model thirteen dosimetric parameters and to calculate two dosimetric indices. The correlations between dosimetric indices and quality of life were extracted as the features and used in the ANN model to make decisions in the clinic. The simulation results show that a trained multilayer back-propagation neural network model can help a doctor accept or reject a plan efficiently. In addition, the models are flexible and whenever a new treatment technique enters the market, the feature variables simply need to be imported and the model re-trained for it to be ready for use.

Keywords: Tumor, Neural Network, dosimetric index, radiation treatment

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1372


[1] T. F. Lee, P. J. Chao, C. Y. Wang, J. H. Lan, Y. J. Huang, H. C. Hsu, C. C. Sung, T. J. Su, S. L. Lian, and F. M. Fang, "Dosimetric comparison of helical tomotherapy and dynamic conformal arc therapy in stereotactic radiosurgery for vestibular schwannomas," Medical Dosimetry, vol.
[2] T. F. Lee, F. M. Fang, P. J. Chao, T. J. Su, L. K. Wang, and S. W. Leung, "Dosimetric comparisons of helical tomotherapy and stepand- shoot intensity-modulated radiotherapy in nasopharyngeal carcinoma," Radiother Oncol, vol. 89, pp. 89-96, 2008.
[3] A. U. Khan, T. K. Bandopadhyaya, and S. Sharma, "Comparisons of Stock Rates Prediction Accuracy Using Different Technical Indicators with Backpropagation Neural Network and Genetic Algorithm Based Backpropagation Neural Network," Emerging Trends in Engineering and Technology, 2008. ICETET '08. First International Conference on, pp. 575-580, 2008.
[4] H. F. Soliman, A. M. Sharaf, M. M. Mansour, S. A. Kandil, and M. H. El-Shafii, "Adaptive ANN Rule-Based Controller for a Chopper fed PMDC Motor Electric Vehicles Drive," Intelligent Vehicles '94 Symposium, Proceedings of the, pp. 429-434, 1994.
[5] A. Islam, M. R. Hasan, R. Rahaman, S. M. R. Kabir, and S. Ahmmed, "Designing ANN using sensitivity & hypothesis correlation testing " iccit 2007. 10th international conference on, Computer and information technology, pp. 1-6, 2007.
[6] M. Simsek and N. S. Sengor, "An Efficient Inverse Ann Modeling Approach Using Prior Knowledge Input with Difference Method," Circuit Theory and Design, 2009. ECCTD 2009. European Conference on, pp. 323-326, 2009.
[7] L. Xinran, W. Lide, and L. Peiqiang, "The Study on Composite Load Model Structure of Artificial Neural Network," Electric Utility Deregulation and Restructuring and Power Technologies, 2008. DRPT 2008. Third International Conference on, pp. 1564-1570, 2008.
[8] L. Jian-Chang, N. Dong-Xiao, and J. Zheng-Yuan, "A study of shortterm load forecasting based on ARIMA-ANN," Proceedings of 2004 International Conference on, Machine Learning and Cybernetics, pp. 3183-3187, 2004.
[9] S. Kuan-Hao, W. Liang-Chih, L. Jih-Shian, L. Ren-Shyan, and C. Jyh-Cheng, "A Novel Method to Improve Image Quality for 2-D Small Animal PET Reconstruction by Correcting a Monte Carlo- Simulated System Matrix Using an Artificial Neural Network," IEEE Transactions on, Nuclear Science, vol. 56, pp. 704-714, 2009.
[10] I. Masood and A. Hassan, "Synergistic-ANN Recognizers for Monitoring and Diagnosis of Multivariate Process Shift Patterns," Soft Computing and Pattern Recognition, 2009. SOCPAR '09. International Conference of, pp. 266-271, 2009.
[11] M.-Y. Cho, T.-F. Lee, S.-W. Kau, C.-S. Shieh, and C.-J. Chou, "Fault diagnosis of power transformers using SVM/ANN with clonal selection algorithm for features and kernel parameters selection," 1st International Conference on Innovative Computing, Information and Control 2006, ICICIC'06, August 30, 2006 - September 1, 2006, Beijing, United states, pp. 26-30, 2006.
[12] H.-Y. Wu, C.-Y. Hsu, T.-F. Lee, and F.-M. Fang, "Improved SVM and ANN in incipient fault diagnosis of power transformers using clonal selection algorithms," International Journal of Innovative Computing, Information and Control, vol. 5, pp. 1959-1974, 2009.
[13] K. Taji, T. Miyake, and H. Tamura, "On Error Backpropagation Algorithm Using Absolute Error Function," 1999 IEEE International Conference on, Systems, Man, and Cybernetics, pp. 401-406, 1999.
[14] M. A. Sovierzoski, F. I. M. Argoud, and F. M. de Azevedo, "Evaluation of ANN Classifiers During Supervised Training with ROC Analysis and Cross Validation," BioMedical Engineering and Informatics, 2008. BMEI 2008. International Conference on, pp. 274-278, 2008.
[15] Y. Yoru, T. H. Karakoc, and A. Hepbasli, "Application of Artificial Neural Network (ANN) Method to Exergy Analysis of Thermodynamic Systems," Machine Learning and Applications, 2009. ICMLA '09. International Conference on, pp. 715-718, 2009.