Breast cancer treatment response evaluation and bladder cancer recurrence prognosis prediction studies based on Qanti Discovery unveiled…pan-cancer expansion models also introduced AI-based digital pathology solution company AIVIS (CEO Daehong Lee) announced that it presented two latest research studies using its research-use pathology AI analysis platform, Qanti Discovery, and unveiled its pan-cancer expansion models at the 2026 United States and Canadian Academy of Pathology (USCAP 2026), held from March 21 to 26 in San Antonio, USA. USCAP is the largest pathology conference in North America with a history spanning over 100 years, bringing together pathologists and industry professionals from around the world to share research findings and discuss the latest technological trends. Through this presentation, AIVIS plans to expand the application scope of pathology AI beyond diagnostic assistance into the areas of treatment response assessment and prognostic prediction, based on its research-use pathology AI platform, Qanti Discovery. In the breast cancer study, AIVIS presented validation results of an AI model designed to detect residual tumor cells in H&E slides following neoadjuvant chemotherapy. A key aspect of this study is that a tumor cell detection model trained solely on immunohistochemistry (IHC) data was directly applied to H&E-stained images. The analysis was conducted on 96 whole-slide images (WSIs) from an external validation dataset (Post-NAT Dataset). The results demonstrated a strong positive correlation between the pathologists’ cellularity assessments and the AI-predicted tumor cell ratio. This indicates that the AI can effectively support pathologists even in challenging scenarios where residual tumor cells are sparse or widely dispersed, making interpretation difficult. Furthermore, the study establishes a foundation for utilizing quantitative AI analysis in treatment response evaluation processes such as residual cancer burden (RCB) assessment. The second study, conducted by Professor Jongwon Lee of Korea University Guro Hospital in collaboration with AIVIS researchers, quantified tumor-infiltrating lymphocytes (TILs) in both the tumor core and invasive front using AIVIS AI models. The study evaluated whether AI-quantified central TIL (cTIL) density could serve as an independent predictor of recurrence-free survival (RFS) in bladder cancer. Bladder cancer has a recurrence rate of 50–70% within five years, yet existing staging systems have limitations in accurately predicting recurrence. The analysis confirmed that central TIL density is an independent prognostic factor (HR 0.964, p=0.037), with the high-density group showing a significantly higher 5-year RFS of 62.4% compared to 36.8% in the low-density group. These findings were further validated in an external cohort from Asan Medical Center (5-year RFS: 72.4% vs. 53.8%). This study represents the first case of applying AI-based TIL quantification to TURB specimens, addressing inter-observer variability while achieving high concordance with pathologist assessments. As recurrence risk can be predicted using only H&E slides without additional immunohistochemistry testing, this approach is expected to contribute to the development of personalized treatment strategies for bladder cancer patients. AIVIS also introduced its pan-cancer expansion models. In addition to its existing breast cancer-optimized Qanti IHC solutions (ER, PR, HER2, Ki-67), the company presented models for neuroendocrine tumors (NET) Ki-67, gastric cancer HER2, and thyroid cancer Ki-67. Through this, AIVIS aims to expand its AI-based biomarker quantification capabilities beyond breast cancer into multiple cancer types, thereby enhancing its competitiveness in the global digital pathology market. AIVIS has obtained regulatory approval from the Ministry of Food and Drug Safety (MFDS) for its Qanti IHC solution and has completed deployments and proof-of-concept (PoC) projects across more than 20 major hospitals in Korea. The company is also conducting joint research in the fields of companion diagnostics (CDx) and drug development with global medical device company Philips and pharmaceutical company AstraZeneca. Recently, AIVIS signed a joint research agreement with ADC-specialized company AimedBio and secured a strategic investment. Daehong Lee, CEO of AIVIS, stated, “This presentation is meaningful as it demonstrates new clinical value of pathology AI in treatment response assessment and prognostic prediction, while also introducing pan-cancer expansion models including NET, gastric, and thyroid cancers. We will continue to expand partnerships with global pharmaceutical companies, CROs, and IVD companies so that AIVIS’s AI pathology technology can create practical value beyond diagnostics, across drug development and precision medicine.”