The (Virtual) 26th Annual Multidisciplinary Symposium on Breast Disease provides a multidisciplinary overview of the diagnosis and treatment of all stages of breast cancer. Faculty includes experts in the fields of surgical, medical and radiation oncology, pathology, radiology, surgery and patient advocacy. The target audience includes clinicians, specialists, and scientists involved in the care and treatment of patients with breast cancer, as well as research and education concerning this important disease.
Upon completion of this course, participants should be able to:
- Understand the definition of oligometastasis
- Recognize the spectrum of histologic features of DCIS, including subtypes, variants and differential diagnosis.
- Review the strengths and limitations of various risk assessment models and how to employ them in clinical care.
- Understand the role of the surgeon in the counseling, test interpretation and management of genetic testing.
- Review the benefit to individual and community health of breast cancer risk assessment and breast and ovarian cancer prevention programs and one community program’s successful implementation.
- Recognize when axillary dissection can be avoided and strategies for reducing lymphedema risk when it is required.
- Discuss the available tools for increasing the accuracy of nodal staging following neoadjuvant chemotherapy.
- Discuss the options for surgical management following neoadjuvant endocrine therapy.
- Review current clinical trial data for immunotherapy for breast cancer.
- Understand new and old data regarding the prevention and treatment of chemotherapy-induced neuropathy
- Understand the role of olanzapine for prevention of chemotherapy-induced nausea/vomiting and treatment of advanced cancer-associated nausea/vomiting.
- Review the treatment of chemotherapy during pregnancy for breast cancer.
- Understand the utility of oxybutynin and other agents for treatment hot flashes.
- Discuss the key areas of breast cancer survivorship care across the age continuum.
- Understand now artificial intelligence/natural language processing can help to extract structured data from the free text that is commonly found in the EHR.