Quantification of Multiphoton and Fluorescence Images of Reproductive Tissues from a Mouse Ovarian Cancer Model Shows Promise for Early Disease Detection

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  <  0.001). The wide-field fluorescence signal also changes significantly between genotypes (p  <  0.01). We use the features to classify multiple tissue groups to over 80% accuracy. These results suggest that MPM and WFI are promising techniques for the early detection of ovarian cancer.