Using Imaging Data and Genomic Data to Predict Metastasis of Breast Cancer After Treatment

Breast cancer is the second leading cause of death for women around the world. Notably, most breast cancer patients die from tumor metastases in the liver, lungs, bones, or brain, not the primary tumor itself. Currently, clinicians are generally successful in treating primary tumors using standard protocols that are based on tumor sub-type and staging, as well as by the presence or absence of prognostic biomarkers. However, it remains difficult to assess in advance the likelihood of metastasis or relapse in any given patient.Physicians can only rely on regular post-treatment screening to monitor any secondary onset. By the time metastasis is detected, the golden window for treatment adjustment has often already passed. This project proposes to develop an analytical tool for predicting the likelihood of metastasis in breast cancer patients post-treatment using imaging and genomic data. We will evaluate our prediction model using prospectively-collected patient data. This new prognostic tool will enable physicians to adjust and tailor therapeutic strategies to each patient in a timely manner. Overall, the tool will personalize patient care, and improve their survival chances and quality of life.

Sex

Female

Inclusion Criteria:

* Clinical diagnosis of breast cancer
* With mammogram
* With surgical treatment
* With chemotherapy, radiotherapy or both

Exclusion Criteria:

Clinical diagnosis of other major diseases