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.
Source
EurekAlert
Related News
•EurekAlert
AutoML Model Accurately Differentiates Brain Tumors on MRI
Thomas Jefferson University researchers developed an AutoML model that distinguishes pituitary macroadenomas from parasellar meningiomas on MRI with over 97% accuracy.
•EurekAlert
AI Model Improves Differentiation of Brain Tumor Progression from Radiation Necrosis on MRI
A York University-led study shows a novel AI using advanced MRI can distinguish between progressive brain tumors and radiation necrosis more accurately than human assessment.
•EurekAlert
Survey: Public Trusts Doctors Over AI, But Embraces AI For Cancer Diagnosis
Most people trust doctors more than AI for health diagnoses, but see significant potential for AI tools in cancer detection.