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Deep Learning for Tumor-Associated Stroma Identification in Prostate Histopathology Slides

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Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 (MICCAI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14225))

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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.

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Authors and Affiliations

  1. Computational Diagnostics Lab, UCLA, Los Angeles, USA

    Zichen Wang, Mara Pleasure, Haoyue Zhang, Kimberly Flores, Anthony Sisk, William Speier & Corey W. Arnold

  2. The Department of Bioengineering, UCLA, Los Angeles, USA

    Zichen Wang, Haoyue Zhang, William Speier & Corey W. Arnold

  3. Medical Informatics Home Area, Graduate Programs in Bioscience, UCLA, Los Angeles, USA

    Mara Pleasure, William Speier & Corey W. Arnold

  4. The Department of Pathology and Laboratory Medicine, UCLA, Los Angeles, USA

    Kimberly Flores, Anthony Sisk & Corey W. Arnold

  5. The Department of Radiological Sciences, UCLA, Los Angeles, USA

    William Speier & Corey W. Arnold

Authors
  1. Zichen Wang
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  2. Mara Pleasure
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  3. Haoyue Zhang
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  4. Kimberly Flores
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  5. Anthony Sisk
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  6. William Speier
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  7. Corey W. Arnold
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Corresponding author

Correspondence to Corey W. Arnold .

Editor information

Editors and Affiliations

  1. Icahn School of Medicine, Mount Sinai, NYC, NY, USA, Tel Aviv University, Tel Aviv, Israel

    Hayit Greenspan

  2. Emory University, Atlanta, GA, USA

    Anant Madabhushi

  3. Queen’s University, Kingston, ON, Canada

    Parvin Mousavi

  4. The University of British Columbia, Vancouver, BC, Canada

    Septimiu Salcudean

  5. Yale University, New Haven, CT, USA

    James Duncan

  6. IBM Research, San Jose, CA, USA

    Tanveer Syeda-Mahmood

  7. Johns Hopkins University, Baltimore, MD, USA

    Russell Taylor

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Wang, Z. et al. (2023). Deep Learning for Tumor-Associated Stroma Identification in Prostate Histopathology Slides. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14225. Springer, Cham. https://doi.org/10.1007/978-3-031-43987-2_62

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  • DOI: https://doi.org/10.1007/978-3-031-43987-2_62

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-43986-5

  • Online ISBN: 978-3-031-43987-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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