In vivo multiphoton imaging of an ovarian cancer mouse model

Ovarian cancer is the deadliest gynecologic cancer due to predominantly 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 with tremendous potential for clinical diagnosis. A sub-modality of MPM is second harmonic generation (SHG) imaging, which generates contrast from anisotropic structures like collagen molecules, enabling the acquisition of detailed molecular structure maps. As collagen is known to change throughout the progression of cancer, MPM is a promising candidate for ovarian cancer screening. While MPM has shown favorable results in a research environment, it has not yet found broad success in a clinical setting. One major obstacle is the quantitative analysis of the image content. Recently, the application of texture analysis to MPM images has shown success for characterizing the collagen content of the tissue, making it a prime candidate for disease screening. Unfortunately, existing work is limited in its application to ovarian tissue and few texture analysis approaches have been evaluated in this context. To address these challenges, we applied texture analysis to second harmonic generation (SHG) and two-photon excited fluorescence (TPEF) images of a mouse model (TgMISIIR-TAg) of ovarian cancer. Using features from the grey-level co-occurrence matrix, we find that texture analysis of TPEF images of the ovary can differentiate between genotype with high statistical significance (p<0.001), whereas TPEF and SHG images of the oviducts are most sensitive to age, and SHG images of the ovaries are most sensitive to reproductive status. While these results suggest that texture analysis is suitable for characterizing ovarian tissue health, further work is focused on developing a classification algorithm based on these features, and also to couple the results with a histopathological analysis.