Patient-Specific Modeling of Breast Biomechanics with Applications to Breast Cancer Detection and Treatment

Abstract

There are many challenges clinicians are faced with when diagnosing and treating breast cancer. Biomechanical modeling of the breast is a field of research that aims to assist clinicians by providing a physics based approach to addressing some of these challenges. This review describes the state of the art in the field, from aiding co-location of information between various medical imaging modalities used to identify tumours; to providing the ability to predict the location of these tumors during different biopsy or surgical procedures; to aiding temporal registration of follow-up medical images used to review the progress of suspicious lesions and therefore evaluate effectiveness of breast cancer treatments; to aiding implant selection for breast augmentation procedures and the subsequent prediction of the resulting appearance following such procedures. Significant technical challenges remain in terms of improving the accuracy of such biomechanical models. These include the precise determination and application of loading and boundary constraints applied during different clinical procedures, and accurate characterization of individual-specific mechanical properties of the different breast tissues. In addition to these more technical challenges, a number of practical challenges exist when translating biomechanical models from research based environments into clinical workflows, which demand general applicability, and ease and speed of use. This review outlines such challenges and provides an overview of the steps researchers are taking to address them. Once these challenges have been met, there is potential for extending the use of biomechanics to simulate more complex clinical procedures, from modeling needle insertions into breast tissue during real-time biopsy procedures, to simulating and predicting the outcome of different surgical procedures such as tumorectomies. Clinical adoption of such state-of-the-art modeling techniques has significant potential for reducing the number of misdiagnosed breast cancers while also helping improve clinical treatment of patients.