New tool helps predict recurrence in patients with noninvasive breast cancer

Doctors can use a prediction tool to help determine treatment risks and benefits for patients with noninvasive breast cancer, according to a study published in the Journal of Clinical Oncology (2010 Jul 12. [Epub ahead of print]).

In a study focused on finding a way to help physicians and patients weigh the risks and benefits of the available options regarding treatment following breast-conserving surgery, researchers from Memorial Sloan-Kettering Cancer Center (MSKCC) collected clinical and pathological data from 1,681 women who had breast-conserving surgery. Using 10 commonly available variables, including the patient's age, family history, clinical presentation, margin status, and histopathological features, the team built a tool called the nomogram.

“For the first time, using readily available information, a patient and her oncologist can estimate her individualized risk, and then use this tool to help in the decision-making process regarding treatment options,” said Kimberly Van Zee, MD, an attending surgeon in the Breast Cancer Surgical Service at MSKCC and the study's lead author. “To date, there has been no other way to integrate all of the known risk factors for recurrence and come up with an individualized absolute risk estimate. This nomogram will be a valuable tool in weighing the pros and cons of various treatments.”

According to background information provided by the authors, the decision regarding treatment following breast-conserving surgery for patients diagnosed with ductal carcinoma in-situ (DCIS) has long been an area of discussion and confusion for both patients and physicians. For example, in a woman at very high risk of recurrence, the added benefit of radiation and/or hormone treatments would be relatively large as compared to a woman at very low risk of recurrence.

“Given that nomograms have been repeatedly shown to be more accurate at risk estimation than expert opinion, it is very helpful to have mathematical models to integrate available information and improve the decision-making process for our patients,” concluded Dr. Van Zee.

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