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- W4387211534 abstract "The diagnosis of prostate cancer is driven by the histopathological appearance of epithelial cells and epithelial tissue architecture. Despite the fact that the appearance of the tumor-associated stroma contributes to diagnostic impressions, its assessment has not been standardized. Given the crucial role of the tumor microenvironment in tumor progression, it is hypothesized that the morphological analysis of stroma could have diagnostic and prognostic value. However, stromal alterations are often subtle and challenging to characterize through light microscopy alone. Emerging evidence suggests that computerized algorithms can be used to identify and characterize these changes. This paper presents a deep-learning approach to identify and characterize tumor-associated stroma in multi-modal prostate histopathology slides. The model achieved an average testing AUROC of $$86.53%$$ on a large curated dataset with over 1.1 million stroma patches. Our experimental results indicate that stromal alterations are detectable in the presence of prostate cancer and highlight the potential for tumor-associated stroma to serve as a diagnostic biomarker in prostate cancer. Furthermore, our research offers a promising computational framework for in-depth exploration of the field effect and tumor progression in prostate cancer." @default.
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- W4387211534 date "2023-01-01" @default.
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- W4387211534 title "Deep Learning for Tumor-Associated Stroma Identification in Prostate Histopathology Slides" @default.
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- W4387211534 doi "https://doi.org/10.1007/978-3-031-43987-2_62" @default.
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