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.

Breast cancer is the most frequently diagnosed cancer and remains the fourth leading cause of cancer deaths in Taiwan women over the past decade. Decisions about the best treatment for breast cancer is based on the result of estrogen (ER) and progesterone receptor (PR) test, human epidermal growth factor type 2 receptor (HER2) test, and TNM staging using biopsy. After evaluation of menopause status and response of ER, PR and HER2, the treatments for stage 2 or above breast cancer may consider neoadjuvant chemotherapy (NAC) for the benefits of (1) converting an inoperable to a surgical resectable cancer, (2) metastasis management, (3) shrink the tumor, (4) improved overall survival and recurrence free survival rate (5) histologic parameters predictive. It is known that patients with pathological complete response (pCR) after NAC are associated with better disease-free survival and improved overall survival. Therefore, it is essential to develop more effective regimens and stratify patients based on computer assisted prediction model to evaluate the response of NAC.

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. The followings are the expected contributions:

To propose a novel parametric dynamics features for overcoming the issues with traditional thresholding method.

To develop segmentation algorithms for breast tissue and tumor region on DCE MRI in order to improve treatment response prediction.

To develop trajectory analysis for non-invasive QDS-IR image To develop segmentation algorithm for metabolic tumor volume by registering PET uptake boundary with CT tumor boundary in order to improve reliability and reproducibility of morphology feature.