Miguel Bronchud, Medical Oncologist at GenesisCare Corachán in Barcelona, shared on LinkedIn:
“AI helps with genomic analysis in breast cancer pathology – it was bound to happen?
A multimodal artificial intelligence (AI) model incorporating multiscale histopathology features was able to predict recurrence risk in patients with early – stage hormone receptor – positive and HER2 – negative breast cancer more accurately than the 21 – gene recurrence score, Oncotype DX – particularly with regards late distant recurrences in patients with breast cancer.
The model, called IICM+, was validated on patients from the TAILORx trial with over a decade of follow – up.
See: Sparano, J.A., Lama, N., Gray, R.J. et al. ‘Artificial Intelligence (AI) model integrating multiscale foundation model histopathology representations ….’ (2026).
The ECOG – ACRIN TAILORx trial demonstrated the benefit of the 21 – gene recurrence score for stratifying recurrence risk in patients with breast cancer, and guiding chemotherapy use. In other words, the Oncotype test performed (in a centralized laboratory) helped clinicians to decide whether to recommend or not to breast cancer an adjuvant chemotherapy treatment following primary surgery for relatively early cancers of the breast (not hyper expressing HER – 2 oncogene and not Triple Negative Breast Cancer).
However, the genomic recurrence score has been more effective for predicting recurrence within the first 5 years of diagnosis than later recurrence.
The researchers sought to make a model capable of predicting both early and late distant recurrences in patients with breast cancer.
The IICM+ model integrates clinicopathologic variables (patient age, tumor size, tumor grade, etc), transcriptomic features, and multiscale histopathology – derived image representations from tile – level embeddings and slide – level representations generated by ‘a custom foundation model’ that was pretrained on paired histopathology images, RNA sequencing, and pathology data.
It was developed on data and tumor specimens from 2,808 participants in the TAILORx trial with five – fold cross – validation.
TAILORx was designed and conducted by ECOG – ACRIN with funding from the National Cancer Institute, part of the National Institutes of Health, and participation from the Alliance for Clinical Trials in Oncology, the Canadian Cancer Trials Group, NRG Oncology, and the SWOG Cancer Research Network. Additional funding was provided by the Breast Cancer Research Foundation, Susan G. Komen, and the Breast Cancer Research Stamp (U.S. Postal Service).
The remaining participants’ data were used to evaluate and validate the model.
Patients in the TAILORx trial had received long – term follow – up of over a decade.
Important note – The study authors added the findings do not indicate use of the IICM+ model for selecting or changing treatment.
Further studies are currently in development to validate the model’s use in other patient populations and to see if it can be used to guide treatment decisions.”
Title: An Artificial Intelligence (AI) model integrating multiscale foundation model histopathology representations with molecular and clinical features predicts early and late distant recurrence in TAILORx
Authors: Joseph A. Sparano, Norsang Lama, Robert J. Gray, Md Ashequr Rahman, Victoria Wang, Della F. Makower, Yating Cheng, Sisi Shao, Kathy S. Albain, Eghbal Amidi, Ming Chen, Daniel F. Hayes, Anthony Helmstetter, Charles E. Geyer Jr., Casey Bales, Andrew Hinton, Elizabeth C. Dees, Matthew P. Goetz, John A. Olson Jr., Sunil S. Badve, Thomas J. Saphner, Timothy J. Whelan, Virginia G. Kaklaman, Matthew Oberley, Milan Radovich, David Spetzler, Eleftherios P. Mamounas, Norman Wolmark, George W. Sledge Jr.
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