Washington, Dec 10 (ANI): Researchers from Fox Chase Cancer Centre have verified several factors-including high tumour grade, negative progesterone receptor status, and inflammatory breast cancer-that are associated with an increased risk.
"If we can identify those patients who are predisposed to brain metastases, we may be able to mirror the model used in small cell lung cancer where prophylactic cranial irradiation has decreased the frequency of brain metastases and improved patient survival," said Veeraiah Siripurapu.
In this study, Siripurapu and colleagues identified 49 patients with brain metastases who were included in a prospectively-collected database of breast cancer patients. They compared these patients with control patients who had similar tumor size, nodal status, and estrogen receptor status at diagnosis but lacked brain tumors.
The patients with brain metastases had a median overall survival of just 38.6 months compared with the group of control patients which had not reached a median overall survival with a mean follow-up of 100 months.
When the team compared the tumor characteristics of the two patient groups, they found that prior non-brain metastases, high nuclear tumor grade, progesterone receptor negativity, and inflammatory breast cancer were associated with an increased risk of brain metastases in a univariate analysis with high nuclear grade remaining significant in a multivariable analysis.
"The data are accumulating in the literature with regard to what tumor characteristics are associated with brain metastases, but there is no consensus on what should be included in a model to predict risk," Siripurapu said.
"Looking at our case-control analysis - which is a novel approach for this question - we also found that high tumor grade was certainly a marked factor in risk. Progesterone receptor negativity and a diagnosis of inflammatory breast cancer may also be valuable additions to a predictive model." Siripurapu added.
He cautions that it is too early to say how a predictive model might alter patient care.
"At a minimum, we might be able to use it to identify patients who should be followed more closely," he said.
"Ultimately, we might be able to use in a preventive treatment strategy, but that would require having a model that has higher sensitivity and specificity than we can achieve right now."
"The importance of piecing together a strong predictive model is that it would allow us to test the possibility in a randomized clinical trial." (ANI)