New Method Uses AI and Microscopy for Precision Medicine in Pancreatic Cancer

Precision medicine has gained popularity in cancer therapy by tailoring treatment strategies to individual patients based on their disease characteristics and personal background. Disease phenotypes guide physicians in selecting effective treatments, leading to improved survival rates and quality of life for cancer patients. However, tools to identify these phenotypes have been limited, hindering the full potential of precision medicine.

A research team at the University of Arizona developed a faster and more affordable method to identify disease phenotypes in pancreatic cancer. Their work, published in Biophotonics Discovery, introduces a new approach using label-free optical microscopy and artificial intelligence (AI).

By utilizing spatial transcriptomics technology, the researchers created spatial maps of tissue gene expression to understand disease behavior and establish phenotypes. Label-free optical microscopy and AI were then used to predict tissue phenotypes with nearly 90 percent accuracy, showcasing the potential of this approach for precision medicine applications.

This study highlights the importance of AI in linking label-free optical images with disease mechanisms to predict phenotypes accurately. The research represents a significant advancement in optical imaging for precision medicine, offering a more accessible and effective way to identify disease phenotypes.

For more information, refer to the original article by S. Guan et al., 'Optical phenotyping using label-free microscopy and deep learning,' Biophoton. Discovery 2(3), 035001 (2025) doi: 10.1117/1.BIOS.2.3.035001.

According to the source: Mirage News.