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Poster

Accurate quantification of slide-level HER2 scores in breast cancer using a machine-learning model, AIM-HER2 Breast Cancer

San Antonio Breast Cancer Symposium | San Antonio, TX

Study Background

HER2 expression level is a key factor in determining the optimal treatment course for breast cancer patients. Roughly 15% of breast cancers are HER2+, and determination of HER2 status is routinely assessed by immunohistochemistry (IHC). Accurate assessment of the HER2 IHC score (0, 1+, 2+, 3+) by pathologists is therefore critical, especially in light of novel therapeutic approaches demonstrating efficacy in the HER2-low setting (IHC scores 1+, and 2+/FISH-)1,2.

To assist pathologists with the consistent provision of reproducible and accurate scores across the entire HER2 scoring range, we developed a machine-learning algorithm (“AIM-HER2”) to generate accurate, slide-level HER2 scores aligned with ASCO-CAP guidelines in clinical breast cancer HER2 IHC specimens.
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Authors

Zahil Shanis1, Ryan Cabeen1,#, Shreya Chakraborty1,#, John Shamshoian1, Marc Thibault1, Blake Martin1, Harshith Pagidela1, Dinkar Juyal1, Syed Ashar Javed1, William Qian1,#, Juhyun Kim1, Beckett Rucker1, Jacqueline Brosnan-Cashman1, Harsha Pokkalla1, Jimish Mehta1,#, Amaro Taylor-Weiner1,#, Ben Glass1, Santhosh Balasubramanian1

1 PathAI, Boston, MA
# Employed at PathAI at time of study