Real-world data of CanAssist Breast- first immunohistochemistry and AI-based prognostic test

CanAssist Breast (CAB), an immunohistochemistry (IHC) and artificial intelligence-based prognostic test, was developed on Hormone receptor-positive (HR +), HER2/neu-negative (HER2-) breast tumors from Indian patients and validated in retrospective global studies. CAB combines the expression of five protein biomarkers with three clinical parameters to segregate patients as low-risk (LR) or high-risk (HR) for distant recurrence. CAB has been in clinical use in South Asia, UAE, Turkey, and Iran for the last 8 years on > 7000 Early breast cancer (EBC) patients. Here we showcase for the first time, the real-world data on the usefulness of CAB to prognosticate across different clinicopathological parameters, histological types, and impact on treatment planning by analysing CAB usage in 5926 patients diagnosed from mid-2016 to 2024. Overall, CAB stratified 72% of patients as LR and 28% as HR for distant recurrence. Interestingly, CAB showed meaningful differences in HR proportions across different histological types; 19% and 29% in mucinous versus mixed mucinous, while 26% and 50% in papillary and micropapillary carcinomas, respectively. In the intermediate Ki67 group, CAB segregated 77% of patients as LR and 23% as HR. In conclusion, CAB is a first-of-its-kind prognostic test that serves as a cost-effective, suitable alternative to Western prognostic tests.