Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Molecular Diagnostics

Machine learning-based pathomics signature could act as a novel prognostic marker for patients with clear cell renal cell carcinoma

British Journal of Cancer volume 126, pages 771–777 (2022)Cite this article

Subjects

Abstract

Background

Traditional histopathology performed by pathologists through naked eyes is insufficient for accurate survival prediction of clear cell renal cell carcinoma (ccRCC).

Methods

A total of 483 whole slide images (WSIs) data from three patient cohorts were retrospectively analyzed. We performed machine learning algorithm to identify optimal digital pathological features and constructed machine learning-based pathomics signature (MLPS) for ccRCC patients. Prognostic performance of the prognostic model was also verified in two independent validation cohorts.

Results

MLPS could significantly distinguish ccRCC patients with high survival risk, with hazard ratio of 15.05, 4.49 and 1.65 in three independent cohorts, respectively. Cox regression analysis revealed that the MLPS could act as an independent prognostic factor for ccRCC patients. Integration nomogram based on MLPS, tumour stage system and tumour grade system improved the current survival prediction accuracy for ccRCC patients, with area under curve value of 89.5%, 90.0%, 88.5% and 85.9% for 1-, 3-, 5- and 10-year disease-free survival prediction.

Discussion

The machine learning-based pathomics signature could act as a novel prognostic marker for patients with ccRCC. Nevertheless, prospective studies with multicentric patient cohorts are still needed for further verifications.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: The workflow of histopathology image processing and machine learning analysis in this study.
Fig. 2: Kaplan–Meier survival analysis of disease-free survival predicted by MLPS for ccRCC patients.
Fig. 3: Developed and evaluated a prognostic nomogram in the whole patient cohort.

Similar content being viewed by others

Data availability

Data supporting the findings of this study are available within the supplementary information and are also available from the authors upon reasonable request.

Code availability

The code used for slide image process and feature extraction had been made publicly available at https://qupath.github.io/ [17].

References

  1. Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2021. CA Cancer J Clin. 2021;71:7–33.

    Article  Google Scholar 

  2. Humphrey PA, Moch H, Cubilla AL, Ulbright TM, Reuter VE. The 2016 WHO classification of tumours of the urinary system and male genital organs-Part B: prostate and bladder tumours. Eur Urol. 2016;70:106–19.

    Article  Google Scholar 

  3. Liu L, Qi L, Li Y, Chen M, Li C, Cheng X, et al. Retroperitoneoscopic partial nephrectomy for moderately complex ventral hilar tumors: surgical technique and trifecta outcomes from a single institution in China. J Laparoendosc Adv Surg Tech Part A. 2017;27:812–7.

    Article  Google Scholar 

  4. Yi X, Xiao Q, Zeng F, Yin H, Li Z, Qian C, et al. Computed tomography radiomics for predicting pathological grade of renal cell carcinoma. Front Oncol. 2020;10:570396.

    Article  Google Scholar 

  5. Inamura K, Fujiwara T, Hoshida Y, Isagawa T, Jones MH, Virtanen C, et al. Two subclasses of lung squamous cell carcinoma with different gene expression profiles and prognosis identified by hierarchical clustering and non-negative matrix factorization. Oncogene. 2005;24:7105–13.

    Article  CAS  Google Scholar 

  6. Chen ST, Zhang N, Jiang LR, Gao F, Shao JL, Wang T, et al. Clinical use of a machine learning histopathological image signature in diagnosis and survival prediction of clear cell renal cell carcinoma. Int J Cancer. 2021;148:780–90.

    Article  CAS  Google Scholar 

  7. Delahunt B, Cheville JC, Martignoni G, Humphrey PA, Magi-Galluzzi C, McKenney J, et al. The International Society of Urological Pathology (ISUP) grading system for renal cell carcinoma and other prognostic parameters. Am J Surg Pathol. 2013;37:1490–504.

    Article  Google Scholar 

  8. Klimov S, Miligy IM, Gertych A, Jiang Y, Toss MS, Rida P, et al. A whole slide image-based machine learning approach to predict ductal carcinoma in situ (DCIS) recurrence risk. Breast Cancer Res. 2019;21:83.

    Article  Google Scholar 

  9. Louis DN, Gerber GK, Baron JM, Bry L, Dighe AS, Getz G, et al. Computational pathology: an emerging definition. Arch Pathol Lab Med. 2014;138:1133–8.

    Article  Google Scholar 

  10. Gertych A, Ing N, Ma Z, Fuchs TJ, Salman S, Mohanty S, et al. Machine learning approaches to analyze histological images of tissues from radical prostatectomies. Comput Med Imaging Graph. 2015;46:197–208.

    Article  Google Scholar 

  11. Ehteshami Bejnordi B, Veta M, Johannes van Diest P, van Ginneken B, Karssemeijer N, Litjens G, et al. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA. 2017;318:2199–210.

    Article  Google Scholar 

  12. Yu KH, Zhang C, Berry GJ, Altman RB, Re C, Rubin DL, et al. Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features. Nat Commun. 2016;7:12474.

    Article  CAS  Google Scholar 

  13. Kickingereder P, Isensee F, Tursunova I, Petersen J, Neuberger U, Bonekamp D, et al. Automated quantitative tumour response assessment of MRI in neuro-oncology with artificial neural networks: a multicentre, retrospective study. Lancet Oncol. 2019;20:728–40.

    Article  Google Scholar 

  14. Brinker TJ, Hekler A, Enk AH, Berking C, Haferkamp S, Hauschild A, et al. Deep neural networks are superior to dermatologists in melanoma image classification. Eur J Cancer. 2019;119:11–7.

    Article  Google Scholar 

  15. Cao R, Yang F, Ma SC, Liu L, Zhao Y, Li Y, et al. Development and interpretation of a pathomics-based model for the prediction of microsatellite instability in Colorectal Cancer. Theranostics. 2020;10:11080–91.

    Article  CAS  Google Scholar 

  16. Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, et al. The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. J Digit Imaging. 2013;26:1045–57.

    Article  Google Scholar 

  17. Bankhead P, Loughrey MB, Fernández JA, Dombrowski Y, McArt DG, Dunne PD, et al. QuPath: Open source software for digital pathology image analysis. Sci Rep. 2017;7:16878.

    Article  Google Scholar 

  18. Kulkarni PM, Robinson EJ, Sarin Pradhan J, Gartrell-Corrado RD, Rohr BR, Trager MH, et al. Deep learning based on standard H&E images of primary melanoma tumors identifies patients at risk for visceral recurrence and death. Clin Cancer Res. 2020;26:1126–34.

    Article  CAS  Google Scholar 

  19. Friedman J, Hastie T, Tibshirani R. Regularization paths for generalized linear models via coordinate descent. J Stat Softw. 2010;33:1–22.

    Article  Google Scholar 

  20. Schuettfort VM, Pradere B, Rink M, Comperat E, Shariat SF. Pathomics in urology. Curr Opin Urol. 2020;30:823–31.

    Article  Google Scholar 

  21. Jiang Y, Jin C, Yu H, Wu J, Chen C, Yuan Q, et al. Development and validation of a deep learning CT signature to predict survival and chemotherapy benefit in gastric cancer: a multicenter, retrospective study. Ann Surg. 2021;274:e1153–61.

    Article  Google Scholar 

  22. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542:115–8.

    Article  CAS  Google Scholar 

  23. Zhao K, Li Z, Yao S, Wang Y, Wu X, Xu Z, et al. Artificial intelligence quantified tumour-stroma ratio is an independent predictor for overall survival in resectable colorectal cancer. EBioMedicine. 2020;61:103054.

    Article  Google Scholar 

  24. Beer DG, Kardia SL, Huang CC, Giordano TJ, Levin AM, Misek DE, et al. Gene-expression profiles predict survival of patients with lung adenocarcinoma. Nat Med. 2002;8:816–24.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We appreciate the QuPath digital pathology software for its contribution. Open data used in this publication were generated by the National Cancer Institute Clinical Proteomic Tumor Analysis Consortium (CPTAC) and TCGA database.

Funding

This work was supported by the National Natural Science Foundation of China (81972393 and 82002665). The funding sources had no role in the design of the study; collection, analysis or interpretation of the data; writing of the report; the decision to submit for publication.

Author information

Author notes

  1. These authors contributed equally: Siteng Chen, Liren Jiang, Feng Gao.

Authors and Affiliations

  1. Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Siteng Chen, Encheng Zhang, Tao Wang, Xiang Wang & Junhua Zheng

  2. Department of Pathology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Liren Jiang & Feng Gao

  3. Department of Urology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Ning Zhang

Authors
  1. Siteng Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Liren Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Feng Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Encheng Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Tao Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ning Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Xiang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Junhua Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

NZ, XW, JZ and SC participated in the study conception and design, data collection, data analysis, reviewed the paper and approved the final draft for submission. LJ, FG, EZ and TW participated in data collection, reviewed the paper and approved the final draft for submission.

Corresponding authors

Correspondence to Ning Zhang, Xiang Wang or Junhua Zheng.

Ethics declarations

Ethics approval and consent to participate

The ethical approval of this study has been approved from the Research Ethics Committee of Shanghai General Hospital (approval number: 2021SQ121). Not further ethical approval was required since all the slice images from the CPTAC cohort and the TCGA cohort were publicly available for research purposes.

Consent to publish

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, S., Jiang, L., Gao, F. et al. Machine learning-based pathomics signature could act as a novel prognostic marker for patients with clear cell renal cell carcinoma. Br J Cancer 126, 771–777 (2022). https://doi.org/10.1038/s41416-021-01640-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41416-021-01640-2

This article is cited by

Search

Advanced search

Quick links