Abstract
Histopathological image analysis performed by a trained expert is currently regarded as the gold-standard for the diagnostics of many pathologies, including cancers. However, such approaches are laborious, time consuming and contain a risk for bias or human error. There is thus a clear need for faster, less intrusive and more accurate diagnostic solutions, requiring also minimal human intervention. Multiphoton microscopy (MPM) can alleviate some of the drawbacks specific to traditional histopathology by exploiting various endogenous optical signals to provide virtual biopsies that reflect the architecture and composition of tissues, both in-vivo or ex-vivo. Here we show that MPM imaging of the dermoepidermal junction (DEJ) in unstained fixed tissues provides useful cues for a histopathologist to identify the onset of non-melanoma skin cancers. Furthermore, we show that MPM images collected on the DEJ, besides being easy to interpret by a trained specialist, can be automatically classified into healthy and dysplastic classes with high precision using a Deep Learning method and existing pre-trained convolutional neural networks. Our results suggest that deep learning enhanced MPM for in-vivo skin cancer screening could facilitate timely diagnosis and intervention, enabling thus more optimal therapeutic approaches.
Highlights
As a result of their inherent optical sectioning capabilities and intrinsic contrast mechanisms, Multiphoton Microscopies (MPM) have emerged over the past three decades as very powerful tools for the label-free characterization of tissue morphology, functionality and biochemical composition (Hoover and Squier, 2013, König, 2018, Williams et al, 2001), in-vivo and ex-vivo
Multiphoton Microscopy (MPM) imaging of the dermoepidermal junction (DEJ) for tissue state assessment In Fig. 1 we present a set of MPM images of the DEJ, collected on normal and dysplastic epithelial tissues
In this work we have focused our attention on MPM imaging of transversal tissue sections containing the DEJ, a region of the skin which is known to harbor important processes and modifications that are relevant with respect to the pathogenesis of epidermal tumors
Summary
As a result of their inherent optical sectioning capabilities and intrinsic contrast mechanisms, Multiphoton Microscopies (MPM) have emerged over the past three decades as very powerful tools for the label-free characterization of tissue morphology, functionality and biochemical composition (Hoover and Squier, 2013, König, 2018, Williams et al, 2001), in-vivo and ex-vivo. Two-Photon Excitation Fluorescence (TPEF) microscopy(So et al, 2000) and Second-harmonic Generation (SHG) microscopy(Campagnola and Loew, 2003), have demonstrated their usefulness for exploring important properties of tissues, which allow establishing their anatomical and functional states, and extracting valuable pathological cues(Balu et al, 2015, Muensterer et al, 2017, Zipfel et al, 2003a, Zipfel et al, 2003b). TPEF microscopy allows a non-invasive assessment of cell morphology, size variation of cell nuclei, blood vessel hyperplasia, or inflammatory reaction related aspects, which are important for assessing the state of a tissue(Benninger and Piston, 2013, Skala et al, 2005, Stanciu et al, 2014, Zipfel et al, 2003a). Investigating collagen distribution with SHG enables a precise and non-invasive assessment of extracellular matrix modifications, which represent a hallmark of cancers(Bonnans et al, 2014, Lu et al, 2012), and of many other pathologies(Raines, 2000)
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