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PanGIA Biotech Unveils AI-Driven Urine Test Data for Early Prostate Cancer Detection at ASCO 2025
PanGIA Biotech Unveils AI-Driven Urine Test Data for Early Prostate Cancer Detection at ASCO 2025

Business Wire

time4 days ago

  • Business
  • Business Wire

PanGIA Biotech Unveils AI-Driven Urine Test Data for Early Prostate Cancer Detection at ASCO 2025

CHICAGO--(BUSINESS WIRE)--PanGIA Biotech, a leader in non-invasive cancer diagnostics, presented findings at the 2025 Annual Meeting of the American Society of Clinical Oncology (ASCO) from a prospective, multi-center validation study evaluating its AI-powered, urine-based platform for early-stage prostate cancer detection. 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. Share 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. 'This study confirms what we've believed from the start: there's power in non-invasive, data-driven diagnostics,' said Holly Magliochetti, CEO of PanGIA Biotech. 'Our platform helps clinicians detect prostate cancer when intervention is most effective—without costly or invasive procedures.' Key findings presented included: Study Cohort: 197 biopsy-confirmed prostate cancer patients and 84 healthy controls. Classifier Performance: Achieved an F1 score of 0.843 with a recall of 0.967 in distinguishing cancer from non-cancer subjects. Gleason Score Cohorts: Maintained high recall (>0.89) across Gleason scores 6 through 10, with F1 scores ranging from 0.799 to 0.838. Non-Invasive Advantage: Demonstrated strong performance in detecting intermediate- and low-grade cancers, offering a less invasive alternative to traditional diagnostics. Unlike invasive biopsies or blood-based tests that often miss early-stage cases, PanGIA's approach analyzes urinary biosignatures using proprietary AI models—eliminating the need for sequencing and enabling cost-effective, globally scalable testing. Previously published in The Analyst, a journal of the Royal Society of Chemistry¹, the PanGIA platform is designed for diverse healthcare environments and holds promise for broad global adoption. About PanGIA Biotech PanGIA Biotech develops AI-integrated, urine-based liquid biopsy solutions designed for global scalability. The PanGIA® platform supports early detection, monitoring, and disease management—including cancer as early as Stage 1. With machine learning at its core, the platform deciphers biomolecular patterns to deliver accurate diagnostic insights. Backed by patents and peer-reviewed research, PanGIA is committed to reshaping healthcare through innovation. Learn more at ¹ Lim FB, et al. 'A Colorimetric Chemical Tongue Detects And Distinguishes Between Multiple Analytes.' The Analyst. 2022;147(23):5283–5292. doi:10.1039/D2AN01615J

PanGIA Biotech Unveils AI-Driven Urine Test Data for Early Prostate Cancer Detection at ASCO 2025
PanGIA Biotech Unveils AI-Driven Urine Test Data for Early Prostate Cancer Detection at ASCO 2025

Yahoo

time4 days ago

  • Health
  • Yahoo

PanGIA Biotech Unveils AI-Driven Urine Test Data for Early Prostate Cancer Detection at ASCO 2025

CHICAGO, June 02, 2025--(BUSINESS WIRE)--PanGIA Biotech, a leader in non-invasive cancer diagnostics, presented findings at the 2025 Annual Meeting of the American Society of Clinical Oncology (ASCO) from a prospective, multi-center validation study evaluating its AI-powered, urine-based platform for early-stage prostate cancer detection. 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. "This study confirms what we've believed from the start: there's power in non-invasive, data-driven diagnostics," said Holly Magliochetti, CEO of PanGIA Biotech. "Our platform helps clinicians detect prostate cancer when intervention is most effective—without costly or invasive procedures." Key findings presented included: Study Cohort: 197 biopsy-confirmed prostate cancer patients and 84 healthy controls. Classifier Performance: Achieved an F1 score of 0.843 with a recall of 0.967 in distinguishing cancer from non-cancer subjects. Gleason Score Cohorts: Maintained high recall (>0.89) across Gleason scores 6 through 10, with F1 scores ranging from 0.799 to 0.838. Non-Invasive Advantage: Demonstrated strong performance in detecting intermediate- and low-grade cancers, offering a less invasive alternative to traditional diagnostics. Unlike invasive biopsies or blood-based tests that often miss early-stage cases, PanGIA's approach analyzes urinary biosignatures using proprietary AI models—eliminating the need for sequencing and enabling cost-effective, globally scalable testing. Previously published in The Analyst, a journal of the Royal Society of Chemistry¹, the PanGIA platform is designed for diverse healthcare environments and holds promise for broad global adoption. About PanGIA Biotech PanGIA Biotech develops AI-integrated, urine-based liquid biopsy solutions designed for global scalability. The PanGIA® platform supports early detection, monitoring, and disease management—including cancer as early as Stage 1. With machine learning at its core, the platform deciphers biomolecular patterns to deliver accurate diagnostic insights. Backed by patents and peer-reviewed research, PanGIA is committed to reshaping healthcare through innovation. Learn more at ¹ Lim FB, et al. "A Colorimetric Chemical Tongue Detects And Distinguishes Between Multiple Analytes." The Analyst. 2022;147(23):5283–5292. doi:10.1039/D2AN01615J View source version on Contacts Media Contact Joy CappsMedia@ 843-730-3857

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