Core Insights - PreciseDx completed a $20.7 million Series B funding round in August 2024, with investors including LabCorp and Agilent Technologies, followed by an additional $11.2 million in June 2025, bringing total funding to $42.7 million [1][10] - The company’s proprietary technology, Precise breast, significantly reduces the time for breast cancer recurrence risk assessment from an average of 22 days to 56 hours, while cutting costs by 80% [1][6] Funding and Investment - The total funding raised by PreciseDx as of June 2025 is $42.7 million, with three rounds of financing: $10.8 million in Series A, $20.7 million in Series B, and an additional $11.2 million in Series B extension [10][11] - The involvement of industry capital, such as LabCorp, enhances the company's expansion efficiency by providing clinical laboratory networks for service implementation [11] Technology and Innovation - PreciseDx has developed a unique AI and pathology technology that includes 47 global patents, focusing on Morphometric Feature Array (MFA) algorithms and AI-based dynamic risk assessment models [2][7] - The technology allows for a three-step cancer detection process that is faster, more precise, and more reliable compared to traditional methods [3][4] Clinical Impact - The use of Precise breast has reduced the time from pathology confirmation to treatment decision from an average of 22 days to 56 hours, with a 37% increase in chemotherapy avoidance for low-risk patients [6][9] - The platform's accuracy in predicting breast cancer recurrence risk has improved by 41% compared to traditional pathology grading, particularly in challenging cases like triple-negative breast cancer [9] Market Strategy - PreciseDx's strategic partnership approach includes collaborations with COTA and Baptist Health for data optimization, and plans to integrate with major diagnostic companies like LabCorp and Quest Diagnostics for service expansion [10] - The company aims to penetrate multiple cancer types beyond breast cancer, with ongoing development for lung, colorectal, and prostate cancer AI models [9][10]