Clinical validation of a multi-modal Ataraxis AI platform for recurrence prediction in early-stage breast cancer across multiple patient cohorts

Background: Breast cancer (BC) treatment selection is traditionally guided by clinical characteristics. However, as clinical characteristics cannot capture the complexity of a disease, genomic tools have been developed. Recent advances in artificial intelligence (AI) have allowed pathology imaging to be used to build more accurate and comprehensive prognostic/predictive models. In this study, we validated an AI test, powered by a pan-cancer histopathology foundation model, that integrates digital pathology images with clinical variables to predict breast cancer recurrence. Methods: The Ataraxis AI prognostic model (ATX) was developed using 4,659 stage I-III BC patients from 10 distinct cohorts. Ataraxis AI platform first extracts novel morphological features from digitized H&E slides using a pre-trained AI foundation model. These morphological features are then integrated with common clinical characteristics, such as TNM staging, ER/PR/HER2 status, age at diagnosis, or lobular or ductal histology to generate a risk score between 0 and 1. We evaluated ATX on 3,502 patients from 5 external cohorts, including 858 patients with available Oncotype DX (ODX) scores. The primary endpoint of this study was disease-free interval (DFI), defined as the time until first recurrence, with deaths prior to recurrence censored. Results: Across 3,502 patients spanning five validation cohorts, ATX accurately predicted DFI with a C-index of 0.71 [0.68–0.75] and hazard ratio (HR) of 3.63 [3.02–4.37, p, 0.01], computed for every 0.2 unit increase in the test score. Compared to ODX (n = 858), the ATX was more accurate, achieving a C-index of 0.67 [0.61–0.74] versus 0.61 [0.49–0.73]. Additionally, ATX added independent prognostic information to ODX in a multivariate analysis (HR: 3.11 [1.91–5.09, p, 0.01]). ATX demonstrated robust accuracy in TNBC (n = 230, C-index: 0.71 [0.62–0.81], HR: 3.81 [2.35–6.17, p = 0.02]) and HER2+ (n = 353, C-index: 0.67 [0.55–0.80], HR: 2.22 [0.99–5.01, p = 0.05]) groups. Conclusions: (1) ATX is predictive of breast cancer recurrence, (2) ATX improves upon the accuracy of ODX, (3) ATX demonstrates robust performance in all main BC subtypes. Research Sponsor: Ataraxis AI.