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Surgical borders of extramammary Paget's disease (EMPD) are difficult to be identified via its clinical appearance. In this study, we propose a new diagnostic technique which combines nonlinear harmonic generation microscopy (HGM) with the deep learning method to instantaneously determine whether the imaged 3D stack is malignant EMPD or surrounding normal skin digitally. To demonstrate our proposal, in this study different locations of fresh EMPD surgical samples were 3D imaged starting from the surface up to a depth of 180 &#x03BC;m using stain-free HGM. With the followed histopathological examination of the same sample, we mapped the gold-standard results to 3D HGM image stacks with labels for the training of the deep learning model. With only 2095 3D image stacks as training and validation data, the results of EMPD and normal skin tissue classification achieve 98.06% sensitivity, 93.18% specificity and 95.81% accuracy. This study supports our proposed 3D convolutional-neural-network-based technique with a high potential to assist physicians to quickly map the EMPD margins by providing noninvasive instant information regarding the imaged sub-millimeter site as malignant or surrounding normal with a high accuracy.

Margin Assessment of Extramammary Paget's Disease Based On Harmonic Generation Microscopy With Deep Neural Networks