Back to all news

AI and Multiphoton Microscopy Achieve 96% Accuracy in Pancreatic Tumor Detection

EurekAlertResearch
AI and Multiphoton Microscopy Achieve 96% Accuracy in Pancreatic Tumor Detection

University of Arizona researchers combined label-free multiphoton microscopy with neural networks to accurately classify pancreatic neuroendocrine neoplasms in tissue samples.

Key Details

  • 1Multiphoton microscopy (MPM) was used to image pancreatic neuroendocrine neoplasm (PNEN) samples without labeling.
  • 2Researchers trained both traditional machine learning and four convolutional neural networks (CNNs) on these images.
  • 3CNNs achieved classification accuracies ranging from 90.8% to 96.4%, outperforming the ML algorithm’s 80.6%.
  • 4Analysis showed key features included collagen content and image texture metrics.
  • 5The approach is faster than traditional pathology and was validated across samples from multiple biorepositories.
  • 6Publication: Biophotonics Discovery, October 2, 2025, DOI: 10.1117/1.BIOS.2.4.045001.

Why It Matters

This study demonstrates the potential of advanced imaging and AI for real-time, highly accurate tumor detection, which could transform intraoperative pathology and improve surgical outcomes in oncology. Faster, label-free diagnosis may reduce delays and error in tumor identification during surgery.

Ready to Sharpen Your Edge?

Join hundreds of your peers who rely on RadAI Slice. Get the essential weekly briefing that empowers you to navigate the future of radiology.

We respect your privacy. Unsubscribe at any time.