Analytical Chemistry - Image Processing links Fluorescence to Cancer Pathology for Clearer IntraSurgical Tumor Boundaries

I'm excited to share that our team's latest paper on statistical image processing for cancer margin detection during surgery has been accepted in Analytical Chemistry.

Our work reveals a strong correlation between tumor and surrounding tissue fluorescence variance, enabling stable tumor boundary identification for up to 24 hours post-injection - a key finding that could help to standardize the window for guided resections.

By stratifying fluorescence signal by standard deviations above background, we demonstrated that SNR thresholds correlate strongly with cancer probability. The approach was validated first in xenograft models and then successfully translated to clinical samples through our collaboration with China Medical University, achieving millimeter scale diagnosis with 87.5% accuracy with no false negatives.

This method offers potential for precise, real-time, quantitative guidance during cancer resection to help surgeons ensure complete tumor removal while preserving healthy tissue.

Full Text