International cohort study 458 biopsy proven NAFLD Bridging fibrosis F3 (n=159) Liver cirrhosis F4 (n=299) Vascular events Non-hepatic cancers All deaths or liver transplantation Decompensation Moderate alcohol consumption

Prostate cancer is currently one of the most commonly-diagnosed types of cancer among males. Although its death rate has dropped in the last decades, it is still a major concern and one of the leading causes of cancer death. Prostate biopsy is a test that confirms or excludes the presence of cancer in the tissue. Samples extracted from biopsies are processed and digitized, obtaining gigapixel-resolution images called wholeslide images, which are analyzed by pathologists. Automated intelligent systems could be useful for helping pathologists in this analysis, reducing fatigue and making the routine process faster. In this work, a novel Deep Learning based computer-aided diagnosis system is presented. This system is able to analyze wholeslide histology images that are first patch-sampled and preprocessed using different filters, including a novel patch-scoring algorithm that removes worthless areas from the tissue. Then, patches are used as input to a custom Convolutional Neural Network, which gives a report showing malignant regions on a heatmap. The impact of applying a stain-normalization process to the patches is also analyzed in order to reduce color variability between different scanners. After training the network with a 3-fold cross-validation method, 99.98% accuracy, 99.98% F1 score and 0.999 AUC are achieved on a separate test set. The computation time needed to obtain the heatmap of a whole-slide image is, on average, around 15 s. Our custom network outperforms other state-of-the-art works in terms of computational complexity for a binary classification task between normal and malignant prostate whole-slide images at patch level.

BRCA1-associated protein 1 (BAP1)-inactivated melanomas can occur sporadically or in germline contexts, particularly in recently recognized BAP1-tumor predisposition syndrome. Diagnosis represents a clinical and histopathological challenge, requiring comprehensive analysis of morphology and sometimes molecular analysis in addition to immunohistochemistry. We report a BAP1-inactivated cutaneous melanoma initially diagnosed as an atypical Spitz tumor on the auricle in a patient with BAP1-tumor predisposition syndrome. Immunohistochemistry, fluorescence in situ hybridization, and comparative genomic hybridization allowed diagnosis. Cutaneous BAP1-inactivated melanocytic tumors, previously classified as atypical Spitz Nevi, may have a dermal mitotic activity that can resemble melanoma and on the other hand, atypical Spitz tumors are sometimes difficult to differentiate from BAP1-inactivated melanoma. Specific criteria, requiring molecular diagnosis have been proposed in order to support melanoma diagnosis.

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