1. Quantification of multiphoton and fluorescence images of reproductive tissues from a mouse ovarian cancer model shows promise for early disease detection
- Author
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Faith P. S. Rice, Travis W. Sawyer, Jennifer W. Koevary, Caitlin C. Howard, Olivia J. Austin, Kathy Q. Cai, Denise C. Connolly, and Jennifer K. Barton
- Subjects
Paper ,Fluorescence-lifetime imaging microscopy ,mouse model ,Biomedical Engineering ,Early detection ,01 natural sciences ,Imaging ,010309 optics ,Biomaterials ,Mice ,fluorescence imaging ,0103 physical sciences ,Image Interpretation, Computer-Assisted ,medicine ,Animals ,multiphoton imaging ,Early Detection of Cancer ,Ovarian Neoplasms ,business.industry ,Early disease ,Optical Imaging ,Ovary ,medicine.disease ,Fluorescence ,Atomic and Molecular Physics, and Optics ,3. Good health ,Electronic, Optical and Magnetic Materials ,Disease Models, Animal ,Multiphoton fluorescence microscope ,Microscopy, Fluorescence, Multiphoton ,ovarian cancer ,Late diagnosis ,Folate receptor ,Cancer research ,Female ,Ovarian cancer ,business ,Algorithms - Abstract
Ovarian cancer is the deadliest gynecologic cancer due predominantly to late diagnosis. Early detection of ovarian cancer can increase 5-year survival rates from 40% up to 92%, yet no reliable early detection techniques exist. Multiphoton microscopy (MPM) is a relatively new imaging technique sensitive to endogenous fluorophores, which has tremendous potential for clinical diagnosis, though it is limited in its application to the ovaries. Wide-field fluorescence imaging (WFI) has been proposed as a complementary technique to MPM, as it offers high-resolution imagery of the entire organ and can be tailored to target specific biomarkers that are not captured by MPM imaging. We applied texture analysis to MPM images of a mouse model of ovarian cancer. We also conducted WFI targeting the folate receptor and matrix metalloproteinases. We find that texture analysis of MPM images of the ovary can differentiate between genotypes, which is a proxy for disease, with high statistical significance (p
- Published
- 2019