The Prediction Model of Neoadjuvant Chemotherapy Response for Breast Cancer Based on The Parametric Dynamics Features of The Pretreatment and Early-Treatment MR-PET and QDS-IR Images.

Detailed Summary

The main purpose of this study is to develop a computer-aided prediction model for NAC treatment response. Based on the heterogeneity of internal parametric tumor composition commonly observed, this study will utilize the histologic characteristics and treatment response to investigate the image features as input data for predicting treatment response using Deep Learning technology. Using this technique, preoperative treatment evaluation may be facilitated by tumor heterogeneity analysis from developed dynamic radiomics, and the possibility of personal medicine can be realized not far ahead. In the first two years of this study using images from DCE-MRI, PET/CT and QDS-IR, we plan to develop the image processing algorithms, including segmenting breast and tumor region, extracting image feature which reflects angiogenic properties and permeability of tumor, which are highly correlated with NAC treatment response. During the third year of the project, the morphology and texture features from first two years can be combined for PET/MRI and prediction model can be achieved in accordance with the features extracted from dynamic features extraction using longitudinal images of PET/MRI.

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