The presentations, led by Shivani Mahajan, PhD, highlight the diagnostic potential of the company’s AI-powered platform, which combines cell-free DNA methylation patterns with protein biomarkers. Clinical data showcased at the symposium point to three specific areas where the blood test may bridge gaps in current surveillance protocols: identifying missed lesions, clarifying ambiguous scans, and providing a lead-time advantage over radiographic detection.

In some cases, the test identified molecular signals up to nine months before a tumor became visible on traditional scans. Hrishikesh Samant, MD, noted that these molecular signals offer physicians a critical secondary metric when imaging findings remain inconclusive. By integrating machine learning with longitudinal modeling, the company aims to move detection into an earlier window, potentially increasing the number of patients eligible for curative interventions. The data suggests that molecular changes often precede the structural tumor growth required for clear radiographic identification.