Preoperative risk stratification in endometrial cancer (ENDORISK) by a Bayesian network model: A development and validation study

Autoři: Casper Reijnen aff001;  Evangelia Gogou aff003;  Nicole C. M. Visser aff004;  Hilde Engerud aff005;  Jordache Ramjith aff007;  Louis J. M. van der Putten aff001;  Koen van de Vijver aff008;  Maria Santacana aff009;  Peter Bronsert aff010;  Johan Bulten aff004;  Marc Hirschfeld aff011;  Eva Colas aff013;  Antonio Gil-Moreno aff013;  Armando Reques aff015;  Gemma Mancebo aff016;  Camilla Krakstad aff005;  Jone Trovik aff005;  Ingfrid S. Haldorsen aff006;  Jutta Huvila aff018;  Martin Koskas aff019;  Vit Weinberger aff020;  Marketa Bednarikova aff021;  Jitka Hausnerova aff022;  Anneke A. M. van der Wurff aff023;  Xavier Matias-Guiu aff009;  Frederic Amant aff024;  ;  Leon F. A. G. Massuger aff001;  Marc P. L. M. Snijders aff002;  Heidi V. N. Küsters-Vandevelde aff026;  Peter J. F. Lucas aff027;  Johanna M. A. Pijnenborg aff001
Působiště autorů: Department of Obstetrics and Gynaecology, Radboud University Medical Center, Nijmegen, The Netherlands aff001;  Department of Obstetrics and Gynaecology, Canisius-Wilhelmina Hospital, Nijmegen, The Netherlands aff002;  Department of Computing Sciences, Radboud University, Nijmegen, The Netherlands aff003;  Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands aff004;  Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway aff005;  Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, Bergen, Norway aff006;  Department for Health Evidence, Radboud University Medical Center, Nijmegen, the Netherlands aff007;  Department of Pathology, Ghent University Hospital, Cancer Research Institute Ghent, Ghent, Belgium aff008;  Department of Pathology and Molecular Genetics and Research Laboratory, Hospital Universitari Arnau de Vilanova, University of Lleida, IRBLleida, CIBERONC, Lleida, Spain aff009;  Institute of Pathology, University Medical Center, Freiburg, Germany aff010;  Department of Obstetrics and Gynecology, University Medical Center, Freiburg, Germany aff011;  Institute of Veterinary Medicine, Georg-August-University, Goettingen, Germany aff012;  Biomedical Research Group in Gynecology, Vall Hebron Institute of Research, Universitat Autònoma de Barcelona, CIBERONC, Barcelona, Spain aff013;  Gynecological Department, Vall Hebron University Hospital, CIBERONC, Barcelona, Spain aff014;  Pathology Department, Vall Hebron University Hospital, CIBERONC, Barcelona, Spain aff015;  Department of Obstetrics and Gynecology, Hospital del Mar, PSMAR, Barcelona, Spain aff016;  Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Bergen, Norway aff017;  Department of Pathology, University of Turku, Turku, Finland aff018;  Obstetrics and Gynecology Department, Bichat-Claude Bernard Hospital, Paris, France aff019;  Department of Gynecology and Obstetrics, University Hospital in Brno and Masaryk University, Brno, Czech Republic aff020;  Department of Internal Medicine, Hematology and Oncology, University Hospital Brno and Masaryk University, Brno, Czech Republic aff021;  Department of Pathology, University Hospital Brno and Masaryk University, Brno, Czech Republic aff022;  Department of Pathology, Elisabeth-TweeSteden Hospital, Tilburg, The Netherlands aff023;  Department of Oncology, KU Leuven, Leuven, Belgium aff024;  Center for Gynecologic Oncology Amsterdam, Netherlands Cancer Institute and Amsterdam University Medical Center, The Netherlands aff025;  Department of Pathology, Canisius-Wilhelmina Hospital, Nijmegen, The Netherlands aff026;  Department of Data Science, University of Twente, Enschede, The Netherlands aff027
Vyšlo v časopise: Preoperative risk stratification in endometrial cancer (ENDORISK) by a Bayesian network model: A development and validation study. PLoS Med 17(5): e32767. doi:10.1371/journal.pmed.1003111
Kategorie: Research Article
doi: 10.1371/journal.pmed.1003111



Bayesian networks (BNs) are machine-learning–based computational models that visualize causal relationships and provide insight into the processes underlying disease progression, closely resembling clinical decision-making. Preoperative identification of patients at risk for lymph node metastasis (LNM) is challenging in endometrial cancer, and although several biomarkers are related to LNM, none of them are incorporated in clinical practice. The aim of this study was to develop and externally validate a preoperative BN to predict LNM and outcome in endometrial cancer patients.

Methods and findings

Within the European Network for Individualized Treatment of Endometrial Cancer (ENITEC), we performed a retrospective multicenter cohort study including 763 patients, median age 65 years (interquartile range [IQR] 58–71), surgically treated for endometrial cancer between February 1995 and August 2013 at one of the 10 participating European hospitals. A BN was developed using score-based machine learning in addition to expert knowledge. Our main outcome measures were LNM and 5-year disease-specific survival (DSS). Preoperative clinical, histopathological, and molecular biomarkers were included in the network. External validation was performed using 2 prospective study cohorts: the Molecular Markers in Treatment in Endometrial Cancer (MoMaTEC) study cohort, including 446 Norwegian patients, median age 64 years (IQR 59–74), treated between May 2001 and 2010; and the PIpelle Prospective ENDOmetrial carcinoma (PIPENDO) study cohort, including 384 Dutch patients, median age 66 years (IQR 60–73), treated between September 2011 and December 2013. A BN called ENDORISK (preoperative risk stratification in endometrial cancer) was developed including the following predictors: preoperative tumor grade; immunohistochemical expression of estrogen receptor (ER), progesterone receptor (PR), p53, and L1 cell adhesion molecule (L1CAM); cancer antigen 125 serum level; thrombocyte count; imaging results on lymphadenopathy; and cervical cytology. In the MoMaTEC cohort, the area under the curve (AUC) was 0.82 (95% confidence interval [CI] 0.76–0.88) for LNM and 0.82 (95% CI 0.77–0.87) for 5-year DSS. In the PIPENDO cohort, the AUC for 5-year DSS was 0.84 (95% CI 0.78–0.90). The network was well-calibrated. In the MoMaTEC cohort, 249 patients (55.8%) were classified with <5% risk of LNM, with a false-negative rate of 1.6%. A limitation of the study is the use of imputation to correct for missing predictor variables in the development cohort and the retrospective study design.


In this study, we illustrated how BNs can be used for individualizing clinical decision-making in oncology by incorporating easily accessible and multimodal biomarkers. The network shows the complex interactions underlying the carcinogenetic process of endometrial cancer by its graphical representation. A prospective feasibility study will be needed prior to implementation in the clinic.

Klíčová slova:

Biomarkers – Cancer treatment – Histology – Lymph nodes – Metastasis – Surgical and invasive medical procedures – Uterine cancer – Endometrial carcinoma


1. Lucas PJ, van der Gaag LC, Abu-Hanna A. Bayesian networks in biomedicine and health-care. Artif Intell Med. 2004;30(3):201–14. doi: 10.1016/j.artmed.2003.11.001 15081072.

2. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68(6):394–424. doi: 10.3322/caac.21492 30207593.

3. Matei D, Filiaci V, Randall ME, Mutch D, Steinhoff MM, DiSilvestro PA, et al. Adjuvant Chemotherapy plus Radiation for Locally Advanced Endometrial Cancer. N Engl J Med. 2019;380(24):2317–26. doi: 10.1056/NEJMoa1813181 31189035; PubMed Central PMCID: PMC6948006.

4. 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. Lancet Oncol. 2019;20(9):1273–1285. doi: 10.1016/S1470-2045(19)30395-X 31345626.

5. Frost JA, Webster KE, Bryant A, Morrison J. Lymphadenectomy for the management of endometrial cancer. Cochrane Database Syst Rev. 2017;10:CD007585. doi: 10.1002/14651858.CD007585.pub4 28968482; PubMed Central PMCID: PMC6485923.

6. A Study in the Treatment of Endometrial Cancer study (ASTEC) group, Kitchener H, Swart AM, Qian Q, Amos C, Parmar MK. Efficacy of systematic pelvic lymphadenectomy in endometrial cancer (MRC ASTEC trial): a randomised study. Lancet. 2009;373(9658):125–36. Epub 2008/12/16. doi: 10.1016/S0140-6736(08)61766-3 19070889; PubMed Central PMCID: PMC2646126.

7. Colombo N, Creutzberg C, Amant F, Bosse T, Gonzalez-Martin A, Ledermann J, et al. ESMO-ESGO-ESTRO Consensus Conference on Endometrial Cancer: diagnosis, treatment and follow-up. Ann Oncol. 2016;27(1):16–41. Epub 2015/12/02. doi: 10.1093/annonc/mdv484 26634381.

8. Trovik J, Wik E, Werner HM, Krakstad C, Helland H, Vandenput I, et al. Hormone receptor loss in endometrial carcinoma curettage predicts lymph node metastasis and poor outcome in prospective multicentre trial. Eur J Cancer. 2013;49(16):3431–41. doi: 10.1016/j.ejca.2013.06.016 23932335.

9. Bendifallah S, Canlorbe G, Collinet P, Arsene E, Huguet F, Coutant C, et al. Just how accurate are the major risk stratification systems for early-stage endometrial doi: 10.1038/bjc.2015.35 25675149 Br J Cancer. 2015;112(5):793–801. PubMed Central PMCID: PMC4453957.

10. Wissing M, Mitric C, Amajoud Z, Abitbol J, Yasmeen A, Lopez-Ozuna V, et al. Risk factors for lymph nodes involvement in obese women with endometrial carcinomas. Gynecol Oncol. 2019;155(1):27–33. doi: 10.1016/j.ygyno.2019.07.016 31349997.

11. Kang S, Kang WD, Chung HH, Jeong DH, Seo SS, Lee JM, et al. Preoperative identification of a low-risk group for lymph node metastasis in endometrial cancer: a Korean gynecologic oncology group study. J Clin Oncol. 2012;30(12):1329–34. doi: 10.1200/JCO.2011.38.2416 22412131.

12. Todo Y, Sakuragi N, Nishida R, Yamada T, Ebina Y, Yamamoto R, et al. Combined use of magnetic resonance imaging, CA 125 assay, histologic type, and histologic grade in the prediction of lymph node metastasis in endometrial carcinoma. Am J Obstet Gynecol. 2003;188(5):1265–72. doi: 10.1067/mob.2003.318 12748496.

13. Lee JY, Jung DC, Park SH, Lim MC, Seo SS, Park SY, et al. Preoperative prediction model of lymph node metastasis in endometrial cancer. Int J Gynecol Cancer. 2010;20(8):1350–5. doi: 10.1111/IGC.0b013e3181f44f5a 21051976.

14. Koskas M, Fournier M, Vanderstraeten A, Walker F, Timmerman D, Vergote I, et al. Evaluation of models to predict lymph node metastasis in endometrial cancer: A multicentre study. Eur J Cancer. 2016;61:52–60. doi: 10.1016/j.ejca.2016.03.079 27153472.

15. Cancer Genome Atlas Research N, Kandoth C, Schultz N, Cherniack AD, Akbani R, Liu Y, et al. Integrated genomic characterization of endometrial carcinoma. Nature. 2013;497(7447):67–73. doi: 10.1038/nature12113 23636398; PubMed Central PMCID: PMC3704730.

16. van der Putten LJ, Visser NC, van de Vijver K, Santacana M, Bronsert P, Bulten J, et al. L1CAM expression in endometrial carcinomas: an ENITEC collaboration study. Br J Cancer. 2016;115(6):716–24. doi: 10.1038/bjc.2016.235 27505134; PubMed Central PMCID: PMC5023774.

17. Raffone A, Travaglino A, Mascolo M, Carbone L, Guida M, Insabato L, et al. TCGA molecular groups of endometrial cancer: Pooled data about prognosis. Gynecol Oncol. 2019;155(2):374–383. doi: 10.1016/j.ygyno.2019.08.019 31472940.

18. Reijnen C, IntHout J, Massuger L, Strobbe F, Kusters-Vandevelde HVN, Haldorsen IS, et al. Diagnostic Accuracy of Clinical Biomarkers for Preoperative Prediction of Lymph Node Metastasis in Endometrial Carcinoma: A Systematic Review and Meta-Analysis. Oncologist. 2019;24(9):e880–e90. doi: 10.1634/theoncologist.2019-0117 31186375; PubMed Central PMCID: PMC6738307.

19. Wik E, Raeder MB, Krakstad C, Trovik J, Birkeland E, Hoivik EA, et al. Lack of estrogen receptor-alpha is associated with epithelial-mesenchymal transition and PI3K alterations in endometrial carcinoma. Clin Cancer Res. 2013;19(5):1094–105. doi: 10.1158/1078-0432.CCR-12-3039 23319822.

20. Huszar M, Pfeifer M, Schirmer U, Kiefel H, Konecny GE, Ben-Arie A, et al. Up-regulation of L1CAM is linked to loss of hormone receptors and E-cadherin in aggressive subtypes of endometrial carcinomas. J Pathol. 2010;220(5):551–61. doi: 10.1002/path.2673 20077528.

21. Amirkhani H, Rahmati M, Lucas PJF, Hommersom A. Exploiting Experts' Knowledge for Structure Learning of Bayesian Networks. IEEE Trans Pattern Anal Mach Intell. 2017;39(11):2154–70. doi: 10.1109/TPAMI.2016.2636828 28114005.

22. Visser NC, Bulten J, van der Wurff AA, Boss EA, Bronkhorst CM, Feijen HW, et al. PIpelle Prospective ENDOmetrial carcinoma (PIPENDO) study, pre-operative recognition of high risk endometrial carcinoma: a multicentre prospective cohort study. BMC Cancer. 2015;15:487. doi: 10.1186/s12885-015-1487-3 26123742; PubMed Central PMCID: PMC4485884.

23. Kommoss FK, Karnezis AN, Kommoss F, Talhouk A, Taran FA, Staebler A, et al. L1CAM further stratifies endometrial carcinoma patients with no specific molecular risk profile. Br J Cancer. 2018;119(4):480–6. doi: 10.1038/s41416-018-0187-6 30050154; PubMed Central PMCID: PMC6134076.

24. Bodurtha Smith AJ, Fader AN, Tanner EJ. Sentinel lymph node assessment in endometrial cancer: a systematic review and meta-analysis. Am J Obstet Gynecol. 2017;216(5):459–76 e10. Epub 2016/11/18. doi: 10.1016/j.ajog.2016.11.1033 27871836.

25. Holloway RW, Abu-Rustum NR, Backes FJ, Boggess JF, Gotlieb WH, Jeffrey Lowery W, et al. Sentinel lymph node mapping and staging in endometrial cancer: A Society of Gynecologic Oncology literature review with consensus recommendations. Gynecol Oncol. 2017;146(2):405–15. doi: 10.1016/j.ygyno.2017.05.027 28566221; PubMed Central PMCID: PMC6075736.

26. Schiavone MB, Scelzo C, Straight C, Zhou Q, Alektiar KM, Makker V, et al. Survival of Patients with Serous Uterine Carcinoma Undergoing Sentinel Lymph Node Mapping. Ann Surg Oncol. 2017;24(7):1965–71. Epub 2017/03/05. doi: 10.1245/s10434-017-5816-4 28258415; PubMed Central PMCID: PMC6092025.

27. Chan JK, Cheung MK, Huh WK, Osann K, Husain A, Teng NN, et al. Therapeutic role of lymph node resection in endometrioid corpus cancer: a study of 12,333 patients. Cancer. 2006;107(8):1823–30. doi: 10.1002/cncr.22185 16977653.

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2020 Číslo 5
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