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. Zahil Shanis 1, Ryan Cabeen 1, #, Shreya Chakraborty 1,#, John Shamshoian 1, Marc Thibault 1, Blake Martin 1, Harshith Pagidela 1, Dinkar Juyal 1, Syed Ashar Javed 1, William Qian 1,#, Juhyun Kim 1, Beckett Rucker 1, Jacqueline Brosnan-Cashman 1, Harsha Pokkalla 1, Jimish Mehta 1,#, Amaro Taylor-Weiner 1,#, Ben Glass 1, Santhosh Balasubramanian 1
1 PathAI, Boston, MA
# Employed at PathAI at time of study
