Quantitative analysis of ovarian cancer pathology using nonlinear optical imaging and lifetime microscopy

The growth and metastasis pathways of ovarian tumors play an important role in physiological and pathological research. Rich pathological information can be obtained by label-free nonlinear optics, a popular technique used in pathological diagnosis. In this work, a multimodal optical system is combined with algorithms to detect ovarian tumor tissues with different pathological characteristics. Two-photon excited fluorescence, second harmonic generation, and fluorescence lifetime imaging microscopy are used to detect the signals of endogenous fluorescent groups. Fast Fourier transform (FFT) and gray level co-occurrence matrix were applied to analyze and classify collagen into three categories to reveal the growth and migration pathway of ovarian cancer. Results show that benign tumors, high-grade serous tumors, and necrotic tissues can be accurately identified. Comparison with traditional pathological diagnosis methods revealed that the combination of multimodal optical imaging and algorithms could be a fast and credible tool for cancer diagnosis.