Applications of Artificial Neural Network to Building Statistical Models for Qualifying and Indexing Radiation Treatment Plans
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.
[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.
[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.
@article{"International Journal of Medical, Medicine and Health Sciences:51463", author = "Pei-Ju Chao and Tsair-Fwu Lee and Wei-Luen Huang and Long-Chang Chen and Te-Jen Su and Wen-Ping Chen", title = "Applications of Artificial Neural Network to Building Statistical Models for Qualifying and Indexing Radiation Treatment Plans", abstract = "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 = "neural network, dosimetric index, radiation treatment, tumor", volume = "4", number = "9", pages = "411-6", }
