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& What’s Next
strategic partnerships, clinical advancements, and market milestones.
Stay informed as PanGIA Biotech advances its mission to
make early cancer detection more accessible, accurate, and globally scalable.
PanGIA Biotech Non-Invasive Cancer Detection Abstracts Accepted for Presentation at AACR Annual Meeting 2026
Abstracts Accepted for Presentation at AACR Annual Meeting 2026 PanGIA Biotech Non-Invasive Cancer Detection Abstracts Accepted for Presentation at AACR Annual Meeting 2026 PanGIA Biotech
PanGIA Biotech Announces Peer-Reviewed Study in Diagnostics Showing 97.8% Sensitivity in Detecting Prostate Cancer Using a Urine-Based Liquid Biopsy with Machine Learning
Delray Beach, FL–April 2, 2026–PanGIA Biotech, Inc. (“PanGIA”) announced a peer-reviewed clinical study published in Diagnostics, “Urine-Based Machine Learning Assay Detects Prostate Cancer,” 1 demonstrating
PanGIA Biotech to Present Four Abstracts at ISLB 2025 Showcasing Multi-Disease Detection Capabilities
PanGIA’s scalable technology demonstrates ability to detect disease-specific molecular patterns across multiple clinical biofluids October 30, 2025 MIAMI, Fla. — October 30, 2025 — PanGIA
The Future of Multi-Cancer Diagnostics — Built for Care, Ready for Integration
In the interview, Curtiss explained why PanGIA Biotech is advancing a platform for true Multi-Cancer Early Detection (MCED) using urine as the biospecimen. He emphasized how co-development with oncologists ensures results that integrate directly into patient care.
PanGIA Biotech Featured in 360Dx for Advancing Urine-Based Prostate Cancer Test
PanGIA Biotech was featured in GenomeWeb’s 360Dx for its AI-powered approach to non-invasive prostate cancer detection using urine samples.
PanGIA Biotech Unveils AI-Driven Urine Test Data for Early Prostate Cancer Detection at ASCO 2025
The study, Development and validation of an AI-enabled prediction of prostate cancer (PCa) using urine-based liquid biopsy (Abstract #3080), is the first to clinically validate PanGIA®’s novel approach—pairing proprietary chemistry with machine learning to detect cancer-specific biosignatures from a single, non-invasive urine sample.