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The benefit that a low-risk patient receives from chemotherapy usually hovers between 10-15%. This makes accurate risk prediction an all-important prerequisite for effective treatment. It is therefore necessary to detect low-risk instances to avoid overtreatment in the form of chemotherapy, given its side effects and the toll it takes on the patients’ quality of life.
CanAssist-Breast is a machine-learning based test that assesses the expression of metastasis-related biomarkers to predict the probability of recurrence of invasive breast carcinoma. It categorizes the risk of recurrence clearly as either high or low with no grey areas in between.
This clear distinction of recurrence risk allows doctors to devise treatment plans that are in tune with the prognosis, maintaining a sensible balance between the benefits and side effects. It makes customized treatment possible while shortening treatment cycles and keeping the costs down in low-risk cases.
The benefits of relying on the CanAssist-Breast test go further. It predicts how much a high-risk patient would benefit from the addition of chemotherapy to the existing treatment regimen.
CanAssist-Breast provides accurate prediction of breast cancer recurrence for hormone-positive patients in stages 1 and 2, through use of a machine learning-based algorithm that incorporates data from each patients tumour biology, as well as clinicopathological data (i.e tumour size, tumour grade and node status) to arrive at a comprehensive CanAssist-Breast risk score that correlates with the patients risk of distant recurrence within 5 years.
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