Predicting recurrence and recurrence‐free survival in high‐grade endometrial cancer using machine learning-Reference-Cited by-同舟云学术

Predicting recurrence and recurrence‐free survival in high‐grade endometrial cancer using machine learning

Published:2022-07-12 Issue:6 Volume:126 Page:1096-1103
ISSN:0022-4790
Container-title:Journal of Surgical Oncology
language:en
Short-container-title:Journal of Surgical Oncology

Author:

Piedimonte Sabrina¹^ORCID,Feigenberg Tomer²,Drysdale Erik³,Kwon Janice⁴,Gotlieb Walter H.⁵,Cormier Beatrice⁶,Plante Marie⁷,Lau Susie⁵,Helpman Limor⁸,Renaud Marie‐Claude⁷,May Taymaa⁹,Vicus Danielle¹⁰

Affiliation:

1. Division of Gynecologic Oncology University of Toronto Toronto Ontario Canada

2. Trillium Health Partners, Missassauga Toronto Ontario Canada

3. Genetics and Genome Biology, AI in Medicine, SickKids Toronto Ontario Canada

4. Vancouver Coastal Health Vancouver British Columbia Canada

5. Jewish General Hospital Montreal Quebec Canada

6. Centre Hospitalier Universitaire de Montreal Montreal Quebec Canada

7. Centre Hospitalier Universitaire de Quebec Quebec City Quebec Canada

8. Juravinski Cancer Center Hamilton Quebec Canada

9. University Health Network Toronto Ontario Canada

10. Sunnybrook Health Sciences Center Toronto Ontario Canada

Publisher

Wiley

Subject

Oncology,General Medicine,Surgery

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/jso.27008

Reference19 articles.

1. Cancer Statistics, 2021

3. The difference between artificial intelligence machine learning and deep learning. 2019. Accessed May 28 2021.https://datacatchup.com/artificial-intelligence-machine-learning-and-deep-learning/

4. A phase III trial of surgery with or without adjunctive external pelvic radiation therapy in intermediate risk endometrial adenocarcinoma: a Gynecologic Oncology Group study

5. Evaluating the diagnostic performance of preoperative endometrial biopsies in patients diagnosed with high grade endometrial cancer: A study of the Society of Gynecologic Oncology (GOC) Community of Practice (CoP)

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