Cutaneous squamous cell carcinoma (cSCC) is a common form of skin cancer with a complex but not fully understood pathogenesis. Recent research suggests the role of beta human papillomavirus (HPV) types and HPV-associated inflammatory processes in cSCC development. Beta HPV types are components of the normal flora; however, under the influence of certain cofactors, the virus may trigger a malignant process. Dysregulation of the immune system (chronic inflammation and immunosuppression), environmental factors (ultraviolet radiation), and genetic factors are the most important cofactors involved in beta HPV-related carcinogenesis. In addition, the oncoproteins E6 and E7 of beta HPV types differ biochemically from their counterparts in the structure of alpha HPV types, resulting in different mechanisms of action in carcinogenesis. The aim of our manuscript is to present an updated point of view on the involvement of beta HPV types in cSCC pathogenesis.

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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.

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Second harmonic generation (SHG) microscopy is acknowledged as an established imaging technique capable to provide information on the collagen architecture in tissues that is highly valuable for the diagnostics of various pathologies. The polarization-resolved extension of SHG (PSHG) microscopy, together with associated image processing methods, retrieves extensive image sets under different input polarization settings, which are not fully exploited in clinical settings. To facilitate this, we introduce PSHG-TISS, a collection of PSHG images, accompanied by additional computationally generated images which can be used to complement the subjective qualitative analysis of SHG images. These latter have been calculated using the single-axis molecule model for collagen and provide 2D representations of different specific PSHG parameters known to account for the collagen structure and distribution. PSHG-TISS can aid refining existing PSHG image analysis methods, while also supporting the development of novel image processing and analysis methods capable to extract meaningful quantitative data from the raw PSHG image sets. PSHG-TISS can facilitate the breadth and widespread of PSHG applications in tissue analysis and diagnostics.

Background and Objectives: Current recommendations and treatment regimens in breast cancer are a reflection of its heterogeneity on multiple levels including histological subtypes, grading, molecular profiling, and numerous prognostic indices. Although based on extensive research, current guidelines are not explicit in the case of surgical specimens showing various degrees of mismatch between different parts of the same tumor and even more so between multicentric lesions. Synchronous breast cancer is the ideal prototype for studying inter- and intra-tumoral heterogeneity, therefore we envisaged that a study on patients with multicentric and multifocal lesions could contribute to the reshaping of the staging, prognosis, and treatment of breast malignancies. Material and Methods: A prospective observational study was conducted between January 2013 and May 2017 on 235 patients diagnosed with breast cancer (BC) and surgically treated at Emergency University Hospital, Bucharest. Thirty-seven patients had multiple breast tumors and were eligible for assessment of the heterogeneity of their lesions. Results: 6 were multicentric and 31 multifocal. The number of foci varied from 2 to 11. We encountered numerous mismatches between the index and the secondary tumors, as follows: 3 cases (8.1%) with histopathological mismatch, 13 (35.1%) with different grades of differentiation, 11 (29.8%) with ER (Estrogen Receptors) status mismatch, 12 (32.4%) with PR (Progesterone Receptors) status mismatch, 8 (21.6%) with molecular phenotype mismatch, and 17 (45.9%) cases with variable Ki-67. After careful analysis of index and secondary tumors, apart from the mismatches reported above, we discovered that the secondary tumors were actually dominant in 5 cases (13.5%), and therefore at least those cases had to be reclassified/restaged, as the supplementary data commanded changes in the therapeutic decision. Conclusions: For synchronous breast tumors, the current Tumor-Node-Metastasis (TNM) staging system ignores not only the histopathological and immunohistochemical characteristics of the secondary foci, but also their size. When secondary lesions are more aggressive or their cumulative mass is significantly bigger than that of the index tumor, the treatment plan should be adapted accordingly. We believe that information obtained from examining secondary foci in synchronous breast cancer and assessment of the cumulative tumoral mass should be reflected in the final staging and definitive treatment. The clinical benefit of staging the patients based on the most aggressive tumor and the cumulative tumoral burden rather than according to the biggest single tumor, will avoid under-treatment in cases with multifocal/multicentric BC displaying intertumoral mismatch.

Second harmonic generation (SHG) microscopy has emerged over the past two decades as a powerful tool for tissue characterization and diagnostics. Its main applications in medicine are related to mapping the collagen architecture of in-vivo, ex-vivo and fixed tissues based on endogenous contrast. In this work we present how H&E staining of excised and fixed tissues influences the extraction and use of image parameters specific to polarization-resolved SHG (PSHG) microscopy, which are known to provide quantitative information on the collagen structure and organization. We employ a theoretical collagen model for fitting the experimental PSHG datasets to obtain the second order susceptibility tensor elements ratios and the fitting efficiency. Furthermore, the second harmonic intensity acquired under circular polarization is investigated. The evolution of these parameters in both forward- and backward-collected SHG are computed for both H&E-stained and unstained tissue sections. Consistent modifications are observed between the two cases in terms of the fitting efficiency and the second harmonic intensity. This suggests that similar quantitative analysis workflows applied to PSHG images collected on stained and unstained tissues could yield different results, and hence affect the diagnostic accuracy.