Using Statistical Learning Theory to Improve Treatment Response for Metastatic Colorectal Carcinoma


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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