Abstract #0608
Neoadjuvant Chemotherapy Treatment Prediction: A Classification Model Based Approach Utilising Pre-treatment DCE-MRI
Martin D Pickles 1 , Peter Gibbs 1 , Martin Lowry 1 , and Lindsay W Turnbull 1
1
Centre for Magnetic Resonance
Investigations, Hull York Medical School at University
of Hull, Hull, East Yorkshire, United Kingdom
The aim of this work was to develop a classification
model to predict pCR, in patients undergoing neoadjuvant
chemotherapy. To generate empirical vascular parameters
dynamic data was interrogated in a pixel-by-pixel
manner. Following pathological analysis Synthetic
Minority Over-sampling TEchnique (SMOTE) was utilised to
balance the pCR and non-pCR classes and a classification
model was developed. High predictive accuracy was
obtained from only 4 DCE-MRI parameters. This study
suggests that prediction of pathological complete
response, secondary to NAC treatment, can be made even
prior to the initiation of chemotherapy from DCE-MRI
parameters with a 86% accuracy.
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