SimBioSys Wins FDA Clearance for Breast Cancer Recurrence AI Tool

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SimBioSys announced on October 15, 2026 that the U.S. Food and Drug Administration has granted 510(k) clearance to TumorSight Risk, an artificial intelligence software device that analyzes breast MRI scans alongside pathology and clinical data to estimate a patient’s risk of breast cancer recurrence at five and ten years.

In its announcement, the Chicago-based company described TumorSight Risk as the world’s first AI-powered, MRI-derived prognostic tool for assessing breast cancer recurrence risk. The software is intended for use as a prognostic indicator of recurrence-free survival at five and ten years in adult women with hormone receptor-positive, HER2-negative Stage I–IIIA breast cancer, with results to be considered alongside other relevant clinicopathologic factors when assessing risk and planning care.

The company states that this HR-positive, HER2-negative subtype represents approximately 75 percent of all breast cancers in the U.S.

The cloud-based platform applies proprietary spatial AI modeling to tumor characteristics, surrounding tissue context and clinical factors, producing a proprietary score from zero to 100 and a categorical high-, intermediate- or low-risk classification. It uses data already collected as part of routine care, requires no additional procedure, tissue shipment or specialized hardware, and is designed to make results available within standard clinical workflows.

The product is built on the company’s previously cleared TumorSight Viz foundation.

According to the FDA’s 510(k) clearance letter and summary, the device operates in sequence with TumorSight Viz. A patient’s pre-treatment dynamic contrast-enhanced MRI, acquired according to American College of Radiology guidelines, is first processed by TumorSight Viz, which generates a multi-tissue segmentation map. That map passes automatically into TumorSight Risk, which computes intermediate imaging values and combines them with age, race, clinical tumor stage, clinical nodal stage and tumor histologic grade in a regression-based machine learning model.