Quantitative Characterization of Duodenal Gastrinoma Autofluorescence Using Multi-Photon Microscopy

Duodenal gastrinomas (DGASTs) are neuroendocrine tumors that develop in the submucosa of the duodenum and produce the hormone gastrin. Surgical resection of DGASTs is complicated by the small size of these tumors and the tendency for them to develop diffusely in the duodenum. Endoscopic mucosal resection of DGASTS is an increasingly popular method for treating this disease due to its low complication rate but suffers from poor rates of pathologically negative margins. Multiphoton microscopy (MPM) is capable of capturing high-resolution images of biological tissue with contrast generated from endogenous fluorescence (autofluorescence) through two-photon excited fluorescence (2PEF). Second harmonic generation (SHG) is another popular method of generating image contrast with MPM and is a light-scattering phenomenon that occurs predominantly from structures such as collagen in biological samples. Some molecules that contribute to autofluorescence change in abundance from processes related to the cancer disease process (e.g., metabolic changes, oxidative stress, angiogenesis). MPM was used to image 12 separate patient samples of formalin-fixed and paraffinized DGAST slides with a SHG channel 4 2PEF channels, each tuned to capture fluorescence from NADH, FAD, lipofuscin, and porphyrin. We found that there was a significant difference in the relative abundance of signal generated in the 2PEF in comparison to the neighboring tissues of the duodenum. Texture extraction was used to create linear discriminant classifiers for tumor vs all other tissue classes before and after principal component analysis (PCA) of the texture feature dataset. PCA improved the classifier accuracy and reduced the number of features required to achieve maximum accuracy of the classifier. The LDA classifier after PCA distinguished between tumor and other tissue types with an accuracy of 90.6 - 93.8%. These results suggest that MPM 2PEF and SHG imaging is a promising label-free method for discriminating between DGAST tumors and normal duodenal tissue which has implications for future applications of in vivo assessment of resection margins with endoscopic MPM.