Research on breast cancer treatment response evaluation and bladder cancer recurrence prognosis prediction using Qanti Discovery unveiled; multi-cancer expansion model also introduced AIVIS (CEO Daehong Lee), an AI-based pathology image analysis company, announced its participation in the 2026 United States and Canadian Academy of Pathology (USCAP 2026), held in San Antonio, USA, from March 21 to 26. The company presented two research studies using its research-grade pathology AI analysis platform, "Qanti Discovery," and unveiled a multi-cancer expansion model. USCAP is North America's largest pathology conference, with more than 100 years of history. It brings together pathologists and industry professionals from around the world to share research findings and discuss emerging technology trends. Through this presentation, AIVIS aims to extend the application of pathology AI beyond diagnostic support into treatment response evaluation and prognosis prediction, leveraging its Qanti Discovery platform. Breast Cancer Study The first study presents validation results of an AI model that detects residual tumor cells in breast cancer H&E slides following neoadjuvant chemotherapy. The key innovation is applying a tumor cell detection model, trained solely on immunohistochemistry (IHC) data, directly to H&E-stained images. Analysis was conducted on 96 whole slide images (WSI) from an external validation dataset (Post-NAT Dataset). The results confirmed a strong positive correlation between pathologist cellularity assessments and AI-predicted tumor cell ratios. This demonstrates that AI can effectively assist pathologists even in challenging cases where tumor cells remain in small quantities or are widely scattered after chemotherapy. It also lays the groundwork for using quantitative AI analysis in treatment response evaluation processes such as Residual Cancer Burden (RCB) calculation. Bladder Cancer Study In the second study, Professor Jong-won Lee of Korea University Guro Hospital and the AIVIS research team utilized the AIVIS AI model to quantitatively analyze Tumor-Infiltrating Lymphocytes (TIL) density in both the tumor center and the invasive front. The research validated whether AI-quantified central TIL (cTIL) density in bladder cancer surgical tissue could serve as an independent predictor of Recurrence-Free Survival (RFS). While bladder cancer has a high recurrence rate of 50–70% within five years, traditional staging alone has been limited in its ability to accurately predict recurrence. The analysis confirmed that central TIL density is an independent prognostic factor for RFS (HR 0.964, p=0.037). The 5-year RFS for the high-density group was 62.4%, significantly exceeding the 36.8% observed in the low-density group. These results were replicated in an external validation cohort at Seoul Asan Medical Center, where the 5-year RFS was 72.4% for the high-density group versus 53.8% for the low-density group. This study represents the first application of AI-based quantitative TIL analysis to TURB specimens, successfully resolving inter-observer variability while maintaining high concordance with pathologist assessments. Because recurrence risk can be predicted using only H&E slides—without the need for additional immunohistochemistry (IHC) testing—this technology is expected to contribute significantly to establishing personalized treatment strategies and clinical result delivery for bladder cancer patients. Multi-Cancer Expansion Model AIVIS also unveiled its multi-cancer expansion model. Building on the existing breast cancer-optimized Qanti IHC (ER, PR, HER2, Ki-67), the company introduced new models including Ki-67 for neuroendocrine tumors (NET), HER2 for gastric cancer, and Ki-67 for thyroid cancer. With this, AIVIS aims to expand its AI biomarker quantification capabilities, previously centered on breast cancer, to multiple cancer types, elevating its product competitiveness in the global pathology market. AIVIS has obtained approval from the Ministry of Food and Drug Safety (MFDS) for its Qanti IHC product and has completed digital pathology deployments and proof-of-concept (PoC) projects at more than 20 major hospitals in Korea. The company is also conducting joint research in AI pathology for companion diagnostics (CDx) and drug development with pharmaceutical company AstraZeneca, as well as with global medical device company Philips. Most recently, AIVIS signed a joint research agreement with ADC (antibody-drug conjugate) specialist AimedBio and secured a strategic equity investment. CEO Daehong Lee commented: "This presentation is meaningful because it not only shares research results demonstrating the new clinical value of pathology AI in treatment response evaluation and prognosis prediction, but also unveils our multi-cancer expansion models covering NET, gastric cancer, thyroid cancer, and more. We will continue to broaden partnerships with global pharmaceutical companies, CROs (contract research organizations), and IVD (in vitro diagnostics) companies, including our collaboration with AimedBio, so that AIVIS's AI technology can create practical value beyond diagnostics across drug development and precision medicine."