Multispectral fluorescence imaging of ovarian tissue for the characterization and classification of early stage ovarian cancer

Steven Santaniello; Taliah Gorman; Travis Sawyer; Photini F. Rice; Jennifer K. Barton

doi:10.1117/12.2577758

5 March 2021 Multispectral fluorescence imaging of ovarian tissue for the characterization and classification of early stage ovarian cancer

Steven Santaniello, Taliah Gorman, Travis Sawyer, Photini F. Rice, Jennifer K. Barton

Author Affiliations +

Proceedings Volume 11655, Label-free Biomedical Imaging and Sensing (LBIS) 2021; 116550D (2021) https://doi.org/10.1117/12.2577758
Event: SPIE BiOS, 2021, Online Only

Abstract

Ovarian cancer is challenging due to poor detection rates and high mortality. Multispectral fluorescence imaging (MFI) has recently become a favorable method for cancer characterization. By utilizing MFI along with a characterized ovarian cancer mouse model and human fallopian tube histology sections, we were able to study cancer in its earliest stages with a promising modality for early disease detection. Fluorescence images with various emission combinations from 280nm to 550nm excitation and reflectance images from 320nm to 550nm were taken of 8-week-old mouse ovarian tissues. Human fallopian tube histology slides were also imaged with the same fluorescence images. Disease characterization was studied using Quadratic Discriminant Analysis (QDA) on image grayscale intensities for both tissues as well as the greylevel co-occurrence matrix (GLCM) in human tissue slides in order to determine a classification group with the highest predictive merit. Both tissues were able to be classified with greater than 80% accuracy, suggesting promise for MFI as a potential diagnostic candidate.

Conference Presentation

Citation Download Citation

Steven Santaniello, Taliah Gorman, Travis Sawyer, Photini F. Rice, and Jennifer K. Barton "Multispectral fluorescence imaging of ovarian tissue for the characterization and classification of early stage ovarian cancer", Proc. SPIE 11655, Label-free Biomedical Imaging and Sensing (LBIS) 2021, 116550D (5 March 2021); https://doi.org/10.1117/12.2577758

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