A prognostic system for epithelial ovarian carcinomas using machine learning-Reference-Cited by-同舟云学术

A prognostic system for epithelial ovarian carcinomas using machine learning

Published:2021-03-18 Issue:8 Volume:100 Page:1511-1519
ISSN:0001-6349
Container-title:Acta Obstetricia et Gynecologica Scandinavica
language:en
Short-container-title:Acta Obstet Gynecol Scand

Author:

Grimley Philip M.¹,Liu Zhenqiu²,Darcy Kathleen M.³,Hueman Matthew T.⁴,Wang Huan⁵,Sheng Li⁶,Henson Donald E.⁷,Chen Dechang⁷^ORCID

Affiliation:

1. Department of Pathology F. Edward Hébert School of Medicine Uniformed Services University of the Health Sciences Bethesda MD USA

2. Department of Public Health Sciences Penn State Cancer Institute Hershey PA USA

3. Department of Obstetrics & Gynecology Uniformed Services University of the Health Sciences Bethesda MD USA

4. Department of Surgical Oncology John P. Murtha Cancer Center Walter Reed National Military Medical Center Bethesda MD USA

5. Department of Biostatistics George Washington University Washington DC USA

6. Department of Mathematics Drexel University Philadelphia PA USA

7. Department of Preventive Medicine & Biostatistics F. Edward Hébert School of Medicine Uniformed Services University of the Health Sciences Bethesda MD USA

Funder

Pennsylvania State University

Publisher

Wiley

Subject

Obstetrics and Gynecology,General Medicine

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/aogs.14137

Reference50 articles.

1. ACR Appropriateness Criteria Staging and Follow-up of Ovarian Cancer

2. Distribution and case-fatality ratios by cell-type for ovarian carcinomas: A 22-year series of 562 patients with uniform current histological classification

3. Risk Factors for Epithelial Ovarian Cancer by Histologic Subtype

4. Gene expression analysis identifies two groups of ovarian high-grade serous carcinomas with different prognosis

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