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

by ECOG-ACRIN Cancer Research Group

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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.

TAILORx established the value of the 21-gene Recurrence Score, also known as the Oncotype DX Breast Recurrence Score test (Exact Sciences Corporation), for estimating the risk of recurrence and guiding chemotherapy use in patients with HR+, HER2-negative, lymph node-negative early breast cancer. However, the Recurrence Score is more effective at predicting recurrence within the first five years after diagnosis than later recurrence. More than half of distant recurrences in this type of breast cancer occur more than five years after initial surgery, sometimes 10, 15 or even more than 20 years later.

Researchers developed the IICM+ model using data and tumor specimens from 2,808 participants in the TAILORx study. They then evaluated the completed model in an independent group of 1,621 TAILORx participants whose data had not been used to develop the model. Both groups had a median follow-up of more than 11 years. Investigators evaluated overall distant recurrence (the return of cancer in other parts of the body), as well as early recurrence (within five years) and late recurrence (after five years).

Enhancing recurrence risk prediction

In the independent validation group, IICM+ significantly outperformed the 21-gene Recurrence Score in distinguishing between patients at different risks of recurrence, as measured by the C-index (higher scores indicate better performance):

• Overall distant recurrence: C-index 0.735 versus 0.578 (P<0.001)
• Early distant recurrence: C-index 0.791 versus 0.722 (P=0.046)
• Late distant recurrence: C-index 0.710 versus 0.514 (P<0.001)

Identifying differences in risk within Recurrence Score groups

In patients with a Recurrence Score of 0–25, which indicates a lower genomic risk, IICM+ identified a subset with a higher observed risk of distant recurrence. Conversely, in patients with a Recurrence Score of 26–100, indicating a higher genomic risk, IICM+ found a subset whose observed risk of recurrence was lower than what the Recurrence Score alone would have suggested.

"Using multimodal data (clinical data, next-generation sequencing data and data obtained from imaging), integrated through modern artificial intelligence techniques, allowed us to create a truly novel means of interrogating breast cancers for important prognostic information," said George W. Sledge Jr., MD, executive vice president and chief medical officer of Caris.

Unlocking the long-term scientific value of TAILORx

The TAILORx biorepository contains tumor specimens, detailed information about each patient's cancer, and meticulously collected treatment and long-term outcome data. As part of the public-private partnership with ECOG-ACRIN, Caris converted archived TAILORx pathology slides into digital images and then applied modern AI and deep learning methods to extract new information from specimens collected years ago.

"TAILORx continues to yield important new insights years after its original findings—a testament to the enduring value of Cooperative Group research," said Peter J. O'Dwyer, MD, ECOG-ACRIN group co-chair. "Applying today's technologies to this exceptional research resource creates opportunities to further individualize breast cancer care."

The findings do not establish the use of IICM+ for selecting or changing treatment at this time. Further studies are needed—and are in development—to validate the model in other patient populations and determine whether its use can guide treatment decisions and improve patient outcomes.

Publication details

Joseph A. Sparano et al, An Artificial Intelligence (AI) model integrating multiscale foundation model histopathology representations with molecular and clinical features predicts early and late distant recurrence in TAILORx, npj Breast Cancer (2026). DOI: 10.1038/s41523-026-01022-y

Journal information: npj Breast Cancer

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Oncology Provided by ECOG-ACRIN Cancer Research Group Who's behind this story?

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