Pathologic primary tumor factors associated with risk of pelvic and paraaortic lymph node involvement in patients with endometrial adenocarcinoma
1Department of Radiation Oncology, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
2Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
3Department of Biostatistics and Bioinformatics, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
DOI: 10.22514/ejgo.2023.056 Vol.44,Issue 4,August 2023 pp.37-42
Submitted: 18 December 2022 Accepted: 17 April 2023
Published: 15 August 2023
The presence of lymph node (LN) positivity in endometrial adenocarcinoma (EAC) patients guides adjuvant treatment, but recommendations regarding LN evaluation at the time of primary surgery remain variable. Primary pathologic tumor characteristics may predict risk of LN involvement in EAC patients with limited LN evaluation. Patients diagnosed between 2004–2016 with pathologic T1–T2 EAC in the National Cancer Database who had at least one lymph node sampled at the time of surgery were included. Pathologic primary tumor predictors of LN involvement were identified using logistic regression. To predict overall, pelvic only, and paraaortic and/or pelvic LN involvement, nomograms were generated. Among 57,810 EAC patients included, 4002 were node positive. On multivariable analysis, increasing pathologic tumor category (pT2 versus pT1a, odds ratio (OR) 5.43, 95% confidence interval (CI) 4.89–6.02, p < 0.001), increasing pathologic tumor grade (grade 3 versus grade 1, OR 1.62, 95% CI 1.47–1.79, p < 0.001), increase in tumor size per centimeter (OR 1.05, 95% CI 1.04–1.06, p < 0.001), and presence of lymphovascular invasion (LVI) (OR 6.33, 95% CI 5.87–6.83, p < 0.001) were predictive of overall LN positivity. The presence of LVI was a stronger predictor of paraaortic LN involvement (OR 6.43, 95% CI 5.55–7.47, p < 0.001) than pelvic LN involvement (OR 5.42, 95% CI 4.98–5.90, p < 0.001) in multivariable analysis. For patients with limited LN evaluation, pathologic tumor features can be used to estimate the risk of pelvic or paraaortic LN involvement. This information may inform adjuvant treatment decisions and guide future studies.
Endometrial adenocarcinoma; Pathologic predictors; Lymph node positive; Tumor category; Histologic grade; Lymphovascular invasion
Eric M. Anderson,Michael Luu,Mitchell Kamrava. Pathologic primary tumor factors associated with risk of pelvic and paraaortic lymph node involvement in patients with endometrial adenocarcinoma. European Journal of Gynaecological Oncology. 2023. 44(4);37-42.
 Brooks RA, Fleming GF, Lastra RR, Lee NK, Moroney JW, Son CH, et al. Current recommendations and recent progress in endometrial cancer. CA: A Cancer Journal for Clinicians. 2019; 69: 258–279.
 Bogani G, Dowdy SC, Cliby WA, Ghezzi F, Rossetti D, Mariani A. Role of pelvic and para-aortic lymphadenectomy in endometrial cancer: current evidence. Journal of Obstetrics and Gynaecology Research. 2014; 40: 301–11.
 Daraï E, Dubernard G, Bats A, Heitz D, Mathevet P, Marret H, et al. Sentinel node biopsy for the management of early stage endometrial cancer: long-term results of the SENTI-ENDO study. Gynecologic Oncology. 2015; 136: 54–59.
 Rossi EC, Kowalski LD, Scalici J, Cantrell L, Schuler K, Hanna RK, et al. A comparison of sentinel lymph node biopsy to lymphadenectomy for endometrial cancer staging (FIRES trial): a multicentre, prospective, cohort study. The Lancet Oncology. 2017; 18: 384–392.
 Benedetti Panici P, Basile S, Maneschi F, Alberto Lissoni A, Signorelli M, Scambia G, et al. Systematic pelvic lymphadenectomy vs. no lymphadenectomy in early-stage endometrial carcinoma: randomized clinical trial. Journal of the National Cancer Institute. 2008; 100: 1707–16.
 Kitchener H, Swart AMC, Qian Q, Amos C, Parmar MKB. Efficacy of systematic pelvic lymphadenectomy in endometrial cancer (MRC ASTEC trial): a randomised study. The Lancet. 2009; 373: 125–136.
 de Boer SM, Powell ME, Mileshkin L, Katsaros D, Bessette P, Haie-Meder C, et al. Adjuvant chemoradiotherapy versus radiotherapy alone in women with high-risk endometrial cancer (PORTEC-3): patterns of recurrence and post-hoc survival analysis of a randomised phase 3 trial. The Lancet Oncology. 2019; 20: 1273–1285.
 Matei D, Filiaci V, Randall ME, Mutch D, Steinhoff MM, DiSilvestro PA, et al. Adjuvant chemotherapy plus radiation for locally advanced endometrial cancer. New England Journal of Medicine. 2019; 380: 2317–2326.
 Fox John, Weisberg Sanford. An R Companion to Applied Regression. 3rd ed. SAGE Publications: New York. 2018.
 Harrell Jr FE. Regression modeling strategies: with applications to linear models, logistic and ordinal regression, and survival analysis. 2nd ed. Springer Series in Statistics: Austin. 2015.
 Creasman WT, Morrow CP, Bundy BN, Homesley HD, Graham JE, Heller PB. Surgical pathologic spread patterns of endometrial cancer: a gynecologic oncology group study. Cancer. 1987; 60: 2035–2041.
 Creasman WT, Ali S, Mutch DG, Zaino RJ, Powell MA, Mannel RS, et al. Surgical-pathological findings in type 1 and 2 endometrial cancer: an NRG Oncology/Gynecologic Oncology Group study on GOG-210 protocol. Gynecologic Oncology. 2017; 145: 519–525.
 Pollom EL, Conklin CMJ, von Eyben R, Folkins AK, Kidd EA. Nomogram to predict risk of lymph node metastases in patients with endometrioid endometrial cancer. International Journal of Gynecological Pathology. 2016; 35: 395–401.
 AlHilli MM, Podratz KC, Dowdy SC, Bakkum-Gamez JN, Weaver AL, McGree ME, et al. Risk-scoring system for the individualized prediction of lymphatic dissemination in patients with endometrioid endometrial cancer. Gynecologic Oncology. 2013; 131: 103–108.
 Sari ME, Yalcin İ, Sahin H, Meydanli MM, Gungor T. Risk factors for paraaortic lymph node metastasis in endometrial cancer. International Journal of Clinical Oncology. 2017; 22: 937–944.
 Dong Y, Cheng Y, Tian W, Zhang H, Wang Z, Li X, et al. An externally validated nomogram for predicting lymph node metastasis of presumed stage I and II endometrial cancer. Frontiers in Oncology. 2019; 9: 1218.
 Taşkın S, Şükür YE, Varlı B, Koyuncu K, Seval MM, Ateş C, et al. Nomogram with potential clinical use to predict lymph node metastasis in endometrial cancer patients diagnosed incidentally by postoperative pathological assessment. Archives of Gynecology and Obstetrics. 2017; 296: 803–809.
 Bendifallah S, Canlorbe G, Laas E, Huguet F, Coutant C, Hudry D, et al. A predictive model using histopathologic characteristics of early-stage type 1 endometrial cancer to identify patients at high risk for lymph node metastasis. Annals of Surgical Oncology. 2015; 22: 4224–4232.
Science Citation Index Expanded (SciSearch) Created as SCI in 1964, Science Citation Index Expanded now indexes over 9,500 of the world’s most impactful journals across 178 scientific disciplines. More than 53 million records and 1.18 billion cited references date back from 1900 to present.
Biological Abstracts Easily discover critical journal coverage of the life sciences with Biological Abstracts, produced by the Web of Science Group, with topics ranging from botany to microbiology to pharmacology. Including BIOSIS indexing and MeSH terms, specialized indexing in Biological Abstracts helps you to discover more accurate, context-sensitive results.
Google Scholar Google Scholar is a freely accessible web search engine that indexes the full text or metadata of scholarly literature across an array of publishing formats and disciplines.
JournalSeek Genamics JournalSeek is the largest completely categorized database of freely available journal information available on the internet. The database presently contains 39226 titles. Journal information includes the description (aims and scope), journal abbreviation, journal homepage link, subject category and ISSN.
Current Contents - Clinical Medicine Current Contents - Clinical Medicine provides easy access to complete tables of contents, abstracts, bibliographic information and all other significant items in recently published issues from over 1,000 leading journals in clinical medicine.
BIOSIS Previews BIOSIS Previews is an English-language, bibliographic database service, with abstracts and citation indexing. It is part of Clarivate Analytics Web of Science suite. BIOSIS Previews indexes data from 1926 to the present.
Journal Citation Reports/Science Edition Journal Citation Reports/Science Edition aims to evaluate a journal’s value from multiple perspectives including the journal impact factor, descriptive data about a journal’s open access content as well as contributing authors, and provide readers a transparent and publisher-neutral data & statistics information about the journal.