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Ovarian cancer accounts for the highest mortality rate among all gynecologic cancers. Current diagnosis methods of ovarian cancer are time-consuming and labor-intensive. We previously found that the organization changes within the extracellular matrix fiber network can occur during cancer progression. It is desired to monitor these organization changes of extracellular matrix in a rapid way so that it can be used for cancer diagnosis. The principal component of the extracellular matrix fibers is mainly collagen. Detecting the change of collagen orientation can improve our understanding of this deadly cancer and benefit clinical diagnosis and treatments. Collagen with highly non-centrosymmetric molecular assemblies can produce SHG signal, which can be detected by multiphoton microscopy. Multiphoton microscopy is a promising non-invasive, label-free nonlinear imaging technique, which has been proven to be an important diagnostic tool for the visualization of collagen. It also possesses intrinsic advantage for 3D visualization. In this study, we use multiphoton microscopy to obtain 3D image stacks of ovarian cancer and normal tissue without the need for tedious and cumbersome tissue processing. SHG from collagen was excited using 810 nm light and the emission signal was detected in the range of 395-415nm. Then the 3D collagen orientation parameter and collagen directional variance for discriminating cancer and normal tissue was gotten by using a specific 3D image processing method. Our results show that the 3D collagen directional variances between ovarian cancer and normal tissue are distinct (p<0.05). In conclusion, the association of multiphoton microscopy with specific 3D image processing method provides a powerful tool for ovarian cancer diagnosis. It may also help physicians to improve clinical outcomes of patients with ovarian cancer.
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