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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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References

  1. Merriel, S.W.D., Funston, G., Hamilton, W.: Prostate cancer in primary care. Adv. Therapy 35, 1285–1294 (2018)

    Article  Google Scholar 

  2. Montironi, R., Beltran, A.L., Mazzucchelli, R., Cheng, L., Scarpelli, M.: Handling of radical prostatectomy specimens: total embedding with large-format histology. Inter. J. Breast Cancer, vol. 2012 (2012)

    Google Scholar 

  3. Han, W., et al.: Histologic tissue components provide major cues for machine learning-based prostate cancer detection and grading on prostatectomy specimens. Sci. Rep. 10(1), 9911 (2020)

    Article  Google Scholar 

  4. Kirby, R.S., Patel, M.I., Poon, D. M.C.: Fast facts: prostate cancer: If, when and how to intervene. Karger Medical and Scientific Publishers (2020)

    Google Scholar 

  5. Nayyar, R., et al.: Upgrading of gleason score on radical prostatectomy specimen compared to the pre-operative needle core biopsy: an indian experience. Indian J. Urology: IJU: J. Urological Soc. India 26(1), 56 (2010)

    Article  Google Scholar 

  6. Jang, W.S., et al.: The prognostic impact of downgrading and upgrading from biopsy to radical prostatectomy among men with gleason score 7 prostate cancer. Prostate 79(16), 1805–1810 (2019)

    Google Scholar 

  7. Bonollo, F., Thalmann, G.N., Julio, M.K.-d, Karkampouna, S.: The role of cancer-associated fibroblasts in prostate cancer tumorigenesis. Cancers 12(7):1887, 2020

    Google Scholar 

  8. Barron, D.A., Rowley, D.R.: The reactive stroma microenvironment and prostate cancer progression. Endocr. Relat. Cancer 19(6), R187–R204 (2012)

    Article  Google Scholar 

  9. Levesque, C., Nelson, P.S.: Cellular constituents of the prostate stroma: key contributors to prostate cancer progression and therapy resistance. Cold Spring Harbor Perspect. Med. 8(8), a030510 (2018)

    Article  Google Scholar 

  10. Liao, Z., Chua, D., Tan, N.S.: Reactive oxygen species: a volatile driver of field cancerization and metastasis. Mol. Cancer 18, 1–10 (2019)

    Article  Google Scholar 

  11. Hayward, S.W., et al.: Malignant transformation in a nontumorigenic human prostatic epithelial cell line. Cancer Res. 61(22), 8135–8142 (2001)

    Google Scholar 

  12. Vivar, A.D.D., et al.: Histologic features of stromogenic carcinoma of the prostate (carcinomas with reactive stroma grade 3). Hum. Pathol. 63, 202–211 (2017)

    Article  Google Scholar 

  13. Bejnordi, B.E., et al.: Using deep convolutional neural networks to identify and classify tumor-associated stroma in diagnostic breast biopsies. Mod. Pathol. 31(10), 1502–1512 (2018)

    Article  Google Scholar 

  14. Zheng, Q., et al.: Machine learning quantified tumor-stroma ratio is an independent prognosticator in muscle-invasive bladder cancer. Int. J. Mol. Sci. 24(3), 2746 (2023)

    Article  Google Scholar 

  15. Millar, E.K.A., et al.: Tumour stroma ratio assessment using digital image analysis predicts survival in triple negative and luminal breast cancer. Cancers 12(12), 3749 (2020)

    Article  Google Scholar 

  16. Ruder, S., et al.: Development and validation of a quantitative reactive stroma biomarker (qrs) for prostate cancer prognosis. Hum. Pathol. 122, 84–91 (2022)

    Article  Google Scholar 

  17. Kirillov, A., Wu, Y., He, K., Girshick, R.: Pointrend: image segmentation as rendering. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9799–9808, 2020

    Google Scholar 

  18. Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)

  19. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  20. Iscen, A., Valmadre, J., Arnab, A., Schmid, C.: Learning with neighbor consistency for noisy labels. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4672–4681 (2022)

    Google Scholar 

  21. Ortego, D., Arazo, E., Albert, P., O’Connor, N.E., McGuinness, K.: Multi-objective interpolation training for robustness to label noise. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6606–6615 (2021)

    Google Scholar 

  22. Gertych, A., et al.: Machine learning approaches to analyze histological images of tissues from radical prostatectomies. Comput. Med. Imaging Graph. 46, 197–208 (2015)

    Article  Google Scholar 

  23. Ing, N., et al.: Semantic segmentation for prostate cancer grading by convolutional neural networks. In: Medical Imaging 2018: Digital Pathology, vol. 10581, pp. 343–355. SPIE (2018)

    Google Scholar 

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Author information

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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© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

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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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