Skip to main content

Advertisement

SpringerLink
Log in

ASO Author Reflections: Development of Natural Language Processing-Based Machine-Learning Algorithms to Identify Pathologic Complete Response from Surgical Pathology Reports

  • Aso Author Reflections
  • Published:
Annals of Surgical Oncology Aims and scope Submit manuscript

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

References

  1. Bray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68:394–424.

    Article  PubMed  Google Scholar 

  2. Cortazar P, Zhang L, Untch M, et al. Pathological complete response and long-term clinical benefit in breast cancer: the CTNeoBC pooled analysis. Lancet. 2014;384:164–72.

    Article  PubMed  Google Scholar 

  3. Spring LM, Fell G, Arfe A, et al. Pathologic complete response after neoadjuvant chemotherapy and impact on breast cancer recurrence and survival: a comprehensive meta-analysis. Clin Cancer Res. 2020;26:2838–48.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Korn E, Sachs M, McShane L. Statistical controversies in clinical research: assessing pathologic complete response as a trial-level surrogate end point for early-stage breast cancer. Ann Oncol. 2016;27:10–5.

    Article  CAS  PubMed  Google Scholar 

  5. Wu G, Cheligeer C, Brisson AM, et al. A new method of identifying pathologic complete response following neoadjuvant chemotherapy for breast cancer patients using a population-based electronic medical record system. Ann Surg Oncol. 2022. https://doi.org/10.1245/s10434-022-12955-6

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgment

This project entitled “Building Pipeline to Transform Real-World Data to Evidence to Improve Cancer Care” was supported by the Canadian Cancer Society (CCS).

Author information

Authors and Affiliations

  1. Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada

    Guosong Wu PhD & Yuan Xu MD, PhD

  2. The Centre for Health Informatics, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada

    Guosong Wu PhD & Cheligeer Cheligeer PhD

  3. Alberta Health Services, Calgary, Alberta, Canada

    Cheligeer Cheligeer PhD

  4. Department of Surgery, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada

    Yuan Xu MD, PhD

  5. Department of Oncology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada

    Yuan Xu MD, PhD

Authors
  1. Guosong Wu PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Cheligeer Cheligeer PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yuan Xu MD, PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yuan Xu MD, PhD.

Ethics declarations

Disclosure

There are no conflicts of interest.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wu, G., Cheligeer, C. & Xu, Y. ASO Author Reflections: Development of Natural Language Processing-Based Machine-Learning Algorithms to Identify Pathologic Complete Response from Surgical Pathology Reports. Ann Surg Oncol 30, 2104–2105 (2023). https://doi.org/10.1245/s10434-022-12967-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1245/s10434-022-12967-2

Search

Navigation