A new AI-powered test offers more accurate prediction of breast cancer recurrence risk

A new study published in the journal npj Breast Cancer demonstrates that an artificial intelligence (AI) model offers more accurate predictions of recurrence risk in patients with early-stage breast cancer than the widely used 21-gene Recurrence Score. This research, conducted by a team from the ECOG-ACRIN Cancer Research Group (ECOG-ACRIN) and Caris Life Sciences, specifically targets hormone receptor-positive (HR+) and HER2-negative disease, the most common subtype of breast cancer, which accounts for about half of all breast cancer cases in the United States.

“Powered by artificial intelligence integrating clinical, molecular and histopathology data, this new test provides more reliable prognostic information for breast cancer recurrence,” said lead author Joseph A. Sparano, MD, of the Icahn School of Medicine at Mount Sinai.

The new model, named IICM+, combines digitized pathology images of the tumor with features such as patient age, tumor size and grade, and molecular information from an expanded 42-gene panel. The team developed and independently validated IICM+ using tumor specimens and more than a decade of clinical outcomes from 4,429 participants in ECOG-ACRIN’s TAILORx breast cancer trial.

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