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ASME Press Select Proceedings

Intelligent Engineering Systems through Artificial Neural Networks, Volume 20

By
Cihan H. Dagli
Cihan H. Dagli
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ISBN:
9780791859599
No. of Pages:
686
Publisher:
ASME Press
Publication date:
2010

Significant interest exists in establishing radiologic imaging as a valid biomarker for assessing the response of cancer to a variety of treatments. This preliminary research study has demonstrated that Statistical Learning Theory algorithms, properly used in a clinical setting, have the potential to address questions and criticisms associated with both Response Evaluation Criteria in Solid Tumors and World Health Organization scoring methods. We also propose that tumor heterogeneity, shape, etc. obtained from computer tomography and/or Magnetic Resonance Imaging scans be added to the Statistical Learning Theory feature vector for processing.

Abstract
Introduction
Why This Research Is Significant
Statistical Learning Theory Models Used in this Research
Results
Conclusions
References
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