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Background: Localized prostate cancer (PCa) is a largely heterogeneous disease regarding its clinical behavior. Current risk stratification relies on clinicopathological parameters and distinguishing between indolent and aggressive cases remains challenging. To improve risk stratification, we aimed to identify new prognostic markers for PCa.

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Understanding the spatial heterogeneity of tumours and its links to disease initiation and progression is a cornerstone of cancer biology. Presently, histopathology workflows heavily rely on hematoxylin and eosin and serial immunohistochemistry staining, a cumbersome, tissue-exhaustive process that results in non-aligned tissue images. We propose the VirtualMultiplexer, a generative artificial intelligence toolkit that effectively synthesizes multiplexed immunohistochemistry images for several antibody markers (namely AR, NKX3.

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Unlabelled: International Society of Urological Pathology grade group 1 (GG 1) prostate cancer (PCa) is generally considered insignificant, with recent suggestions that it should even be considered as "noncancerous". We evaluated outcomes for patients with GG 1 PCa on biopsy (bGG 1) and high-risk features (prostate-specific antigen [PSA] >20 ng/ml and/or cT3-4 stage) to challenge the hypothesis that every case of bGG 1 PCa has a benign disease course. We used the multi-institutional EMPaCT database, which includes data for 9508 patients with high-risk PCa undergoing surgery.

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Patients And Methods: The Swiss Group for Clinical Cancer Research (SAKK) 06/17 was an open-label single-arm phase II trial including 61 cisplatin-fit patients with clinical stage (c)T2-T4a cN0-1 operable urothelial MIBC or upper urinary tract cancer. Patients received neoadjuvant cisplatin/gemcitabine and durvalumab followed by surgery.

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