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A Predictive Model Using Histopathologic Characteristics of Early-Stage Type 1 Endometrial Cancer to Identify Patients at High Risk for Lymph Node Metastasis

  • Gynecologic Oncology
  • Published:
Annals of Surgical Oncology Aims and scope Submit manuscript

Abstract

Background

This study aimed to develop a predictive model using histopathologic characteristics of early-stage type 1 endometrial cancer (EC) to identify patients at high risk for lymph node (LN) metastases.

Methods

The data of 523 patients who received primary surgical treatment between January 2001 and December 2012 were abstracted from a prospective multicenter database (training set). A multivariate logistic regression analysis of selected prognostic features was performed to develop a nomogram predicting LN metastases. To assess its accuracy, an internal validation technique with a bootstrap approach was adopted. The optimal threshold in terms of clinical utility, sensitivity, specificity, negative predictive values (NPVs), and positive predictive values (PPVs) was evaluated by the receiver-operating characteristics (ROC) curve area and the Youden Index.

Results

Overall, the LN metastasis rate was 12.4 % (65/523). Lymph node metastases were associated with histologic grade, tumor diameter, depth of myometrial invasion, and lymphovascular space involvement status. These variables were included in the nomogram. Discrimination of the model was 0.83 [95 % confidence interval (CI) 0.80–0.85] in the training set. The area under the curve ROC for predicting LN metastases after internal validation was 0.82 (95 % CI 0.80–0.84). The Youden Index provided a value of 0.2, corresponding to a cutoff of 140 points (total score in the algorithm). At this threshold, the model had a sensitivity of 0.73 (95 % CI 0.62–0.83), a specificity of 0.84 (95 % CI 0.82–0.85), a PPV of 0.40 (95 % CI 0.34–0.45), and an NPV of 0.95 (95 % CI 0.94–0.97).

Conclusion

The results show that the risk of LN metastases can be predicted correctly so that patients at high risk can benefit from adapted surgical treatment.

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References

  1. Jemal A, Siegel R, Xu J, Ward E. Cancer statistics, 2010. CA Cancer J Clin. 2010;60: 277–300.

    Article  PubMed  Google Scholar 

  2. Zhu L, Pickle LW, Ghosh K, et al. Predicting US- and state-level cancer counts for the current calendar year: part II: evaluation of spatiotemporal projection methods for incidence. Cancer. 2012;118:1100–9.

    Article  PubMed  Google Scholar 

  3. Colombo N, Preti E, Landoni F. Endometrial cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment, and follow-up. Ann Oncol. 2013;24(Suppl 6):33–8.

    Article  Google Scholar 

  4. Panici P, Basile S, Maneschi F. Systematic pelvic lymphadenectomy vs. no lymphadenectomy in early-stage endometrial carcinoma: randomized clinical trial. J Natl Cancer Inst. 2008;100:1707–16.

    Article  Google Scholar 

  5. ASTEC study group, Kitchener H, Swart AMC, Qian Q, Amos C, Parmar MKB. Efficacy of systematic pelvic lymphadenectomy in endometrial cancer (MRC ASTEC trial): a randomised study. Lancet. 2009;373:125–36.

    Article  Google Scholar 

  6. Creutzberg CL, van Putten WL, Koper PC, et al. Surgery and postoperative radiotherapy versus surgery alone for patients with stage-1 endometrial carcinoma: multicentre randomised trial. PORTEC Study Group. Post operative radiation therapy in endometrial carcinoma. Lancet. 2000;355:1404–11.

    Article  CAS  PubMed  Google Scholar 

  7. Randall ME, Filiaci VL, Muss H, et al. Randomized phase III trial of whole-abdominal irradiation versus doxorubicin and cisplatin chemotherapy in advanced endometrial carcinoma: a Gynecologic Oncology Group Study. J Clin Oncol. 2006;24:36–44.

    Article  CAS  Google Scholar 

  8. Todo Y, Kato H, Kaneuchi M, Watari H, Takeda M, Sakuragi N. Survival effect of para-aortic lymphadenectomy in endometrial cancer (SEPAL study): a retrospective cohort analysis. Lancet. 2010;375:1165–72.

    Article  PubMed  Google Scholar 

  9. Nugent EK, Bishop EA, Mathews CA, et al. Do uterine risk factors or lymph node metastasis more significantly affect recurrence in patients with endometrioid adenocarcinoma? Gynecol Oncol. 2012;125:94–8.

    Article  CAS  PubMed  Google Scholar 

  10. Ballester M, Dubernard G, Lécuru F, et al. Detection rate and diagnostic accuracy of sentinel-node biopsy in early stage endometrial cancer: a prospective multicentre study (SENTI-ENDO). Lancet Oncol. 2011;12:469–76.

    Article  PubMed  Google Scholar 

  11. Iasonos A, Schrag D, Raj GV, Panageas KS. How to build and interpret a nomogram for cancer prognosis. J Clin Oncol. 2008;26:1364–70.

    Article  Google Scholar 

  12. Eastham JA, Kattan MW, Scardino PT. Nomograms as predictive models. Semin Urol Oncol. 2002;20:108–15.

    Article  PubMed  Google Scholar 

  13. AlHilli MM, Podratz KC, Dowdy SC, et al. Risk-scoring system for the individualized prediction of lymphatic dissemination in patients with endometrioid endometrial cancer. Gynecol Oncol. 2013;131:103–8.

    Article  CAS  PubMed  Google Scholar 

  14. Bendifallah S, Genin AS, Naoura I, et al. A nomogram for predicting lymph node metastasis of presumed stage I and II endometrial cancer. Am J Obstet Gynecol. 2012;207:197–e1.

    PubMed  Google Scholar 

  15. Pecorelli S. Revised FIGO staging for carcinoma of the vulva, cervix, and endometrium. Int J Gynaecol. 2009;105:103–4.

    Article  Google Scholar 

  16. AlHilli MM, Podratz KC, Dowdy SC, et al. Preoperative biopsy and intraoperative tumor diameter predict lymph node dissemination in endometrial cancer. Gynecol Oncol. 2013;128:294–9.

    Article  PubMed  Google Scholar 

  17. Delpech Y, Cortez A, Coutant C, et al. The sentinel node concept in endometrial cancer: histopathologic validation by serial section and immunohistochemistry. Ann Oncol. 2007;18:1799–803.

    Article  CAS  PubMed  Google Scholar 

  18. He R, Belin T. Multiple imputation for high-dimensional mixed incomplete continuous and binary data. Stat Med. 2014;33:2251–62.

    Article  PubMed  Google Scholar 

  19. Wagenmakers E-J, Farrell S. AIC model selection using Akaike weights. Psychon Bull Rev. 2004;11:192–6.

    Article  PubMed  Google Scholar 

  20. Harrell FE Jr, Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med. 1996;15:361–87.

    Article  PubMed  Google Scholar 

  21. Steyerberg EW, Eijkemans MJ, Harrell FE Jr, Habbema JD. Prognostic modelling with logistic regression analysis: a comparison of selection and estimation methods in small data sets. Stat Med. 2000;19:1059–79.

    Article  CAS  PubMed  Google Scholar 

  22. Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 1982;143:29–36.

    Article  CAS  PubMed  Google Scholar 

  23. Coutant C, Olivier C, Lambaudie E, et al. Comparison of models to predict nonsentinel lymph node status in breast cancer patients with metastatic sentinel lymph nodes: a prospective multicenter study. J Clin Oncol. 2009;27:2800–8.

    Article  PubMed  Google Scholar 

  24. Miller ME, Hui SL, Tierney WM. Validation techniques for logistic regression models. Stat Med. 1991;10:1213–26.

    Article  CAS  PubMed  Google Scholar 

  25. Schisterman EF, Perkins NJ, Liu A, Bondell H. Optimal cut-point and its corresponding Youden Index to discriminate individuals using pooled blood samples. Epidemiol Camb Mass. 2005;16:73–81.

    Article  Google Scholar 

  26. Morrow CP, Bundy BN, Homesley HD, et al. Doxorubicin as an adjuvant following surgery and radiation therapy in patients with high-risk endometrial carcinoma, stage I and occult stage II: a Gynecologic Oncology Group Study. Gynecol Oncol. 1990;36:166–71.

    Article  CAS  PubMed  Google Scholar 

  27. Lurain JR, Rice BL, Rademaker AW, Poggensee LE, Schink JC, Miller DS. Prognostic factors associated with recurrence in clinical stage I adenocarcinoma of the endometrium. Obstet Gynecol. 1991;78:63–9.

    CAS  PubMed  Google Scholar 

  28. Keys HM, Roberts JA, Brunetto VL, et al. A phase III trial of surgery with or without adjunctive external pelvic radiation therapy in intermediate risk endometrial adenocarcinoma: a Gynecologic Oncology Group study. Gynecol Oncol. 2004;92:744–51.

    Article  PubMed  Google Scholar 

  29. Walker JL, Piedmonte MR, Spirtos NM, et al. Laparoscopy compared with laparotomy for comprehensive surgical staging of uterine cancer: Gynecologic Oncology Group Study LAP2. J Clin Oncol Off J Am Soc Clin Oncol. 2009;27:5331–6.

    Article  Google Scholar 

  30. Bendifallah S, Canlorbe G, Raimond E, et al. A clue towards improving the European Society of Medical Oncology risk group classification in apparent early stage endometrial cancer? Impact of lymphovascular space invasion. Br J Cancer. 2014. doi:10.1038/bjc.2014.237.

    Google Scholar 

  31. Frumovitz M, Slomovitz BM, Singh DK, et al. Frozen section analyses as predictors of lymphatic spread in patients with early-stage uterine cancer. J Am Coll Surg. 2004;199:388–93.

    Article  PubMed  Google Scholar 

  32. Ko EM, Funk MJ, Clark LH, Brewster WR. Did GOG99 and PORTEC1 change clinical practice in the United States? Gynecol Oncol. 2013;129:12–7.

    Article  PubMed  Google Scholar 

  33. Greer BE, Koh WJ, AbuRustum N. Uterine neoplasms Clinical Practice Guidelines in Oncology. J Natl Compr Cancer Netw JNCCN. 2009;7:498–531.

    CAS  PubMed  Google Scholar 

  34. Kattan MW. Nomograms are superior to staging and risk grouping systems for identifying high-risk patients: preoperative application in prostate cancer. Curr Opin Urol. 2003;13:111–6.

    Article  PubMed  Google Scholar 

  35. Ballester M, Dubernard G, Bats A-S, et al. Comparison of diagnostic accuracy of frozen section with imprint cytology for intraoperative examination of sentinel lymph node in early-stage endometrial cancer: results of Senti-Endo study. Ann Surg Oncol. 2012;19:3515–21.

    Article  PubMed  Google Scholar 

  36. Heagerty PJ, Zheng Y. Survival model predictive accuracy and ROC curves. Biometrics. 2005;61:92–105.

    Article  PubMed  Google Scholar 

  37. Todo Y, Okamoto K, Hayashi M, et al. A validation study of a scoring system to estimate the risk of lymph node metastasis for patients with endometrial cancer for tailoring the indication of lymphadenectomy. Gynecol Oncol. 2007;104:623–8.

    Article  PubMed  Google Scholar 

  38. Bendifallah S, Canlorbe G, Raimond E, et al. External validation of nomograms designed to predict lymphatic dissemination in patients with early-stage endometrioid endometrial cancer: a multicenter study. Am J Obstet Gynecol. 2014. doi:10.1016/j.ajog.2014.06.058.

    Google Scholar 

  39. Raimond E, Ballester M, Hudry D, et al. Impact of sentinel lymph node biopsy on the therapeutic management of early-stage endometrial cancer: results of a retrospective multicenter study. Gynecol Oncol. 2014. doi:10.1016/j.ygyno.2014.03.019.

    PubMed  Google Scholar 

  40. Klebanoff MA, Cole SR. Use of multiple imputation in the epidemiologic literature. Am J Epidemiol. 2008;168:355–7.

    Article  PubMed Central  PubMed  Google Scholar 

Download references

Acknowledgment

We thank Professeur Pierre-Yves Boelle who spent time reading and making comments on the manuscript.

Conflict of interest

None.

Author information

Authors and Affiliations

  1. Department of Obstetrics and Gynaecology, Tenon University Hospital, University Pierre and Marie Curie, Paris 6, France

    Sofiane Bendifallah MD, Geoffroy Canlorbe MD, Enora Laas MD, Emile Daraï MD, PhD & Marcos Ballester MD, PhD

  2. INSERM UMR S 707, “Epidemiology, Information Systems, Modeling”, University Pierre and Marie Curie, Paris, France

    Sofiane Bendifallah MD

  3. Department of Radiation Oncology, Tenon University Hospital, University Pierre and Marie Curie, Paris 6, France

    Florence Huguet MD, PhD

  4. Centre de Lutte Contre le Cancer Georges François Leclerc, Dijon, France

    Charles Coutant MD, PhD & Delphine Hudry MD

  5. Department of Obstetrics and Gynaecology, Institute Alix de Champagne University Hospital, Reims, France

    Olivier Graesslin MD, PhD & Emilie Raimond MD

  6. Department of Obstetrics and Gynecology, Centre Hospitalier Intercommunal, Créteil, France

    Cyril Touboul MD, PhD

  7. Department of Gynecological Surgery, Jeanne de Flandre University Hospital, Lille, France

    Pierre Collinet MD, PhD & Géraldine Bleu MD

  8. Department of Pathology, Tenon University Hospital, University Pierre and Marie Curie, Paris, France

    Annie Cortez MD

  9. INSERM UMR S 938, University Pierre et Marie Curie, Paris 6, France

    Emile Daraï MD, PhD & Marcos Ballester MD, PhD

  10. Institut Universitaire de Cancérologie (IUC), University Pierre and Marie Curie, Paris 6, France

    Emile Daraï MD, PhD & Marcos Ballester MD, PhD

Authors
  1. Sofiane Bendifallah MD
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  2. Geoffroy Canlorbe MD
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  3. Enora Laas MD
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  4. Florence Huguet MD, PhD
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  5. Charles Coutant MD, PhD
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  6. Delphine Hudry MD
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  7. Olivier Graesslin MD, PhD
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  8. Emilie Raimond MD
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  9. Cyril Touboul MD, PhD
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  10. Pierre Collinet MD, PhD
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  11. Annie Cortez MD
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  12. Géraldine Bleu MD
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  13. Emile Daraï MD, PhD
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  14. Marcos Ballester MD, PhD
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Corresponding author

Correspondence to Sofiane Bendifallah MD.

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Bendifallah, S., Canlorbe, G., Laas, E. 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. Ann Surg Oncol 22, 4224–4232 (2015). https://doi.org/10.1245/s10434-015-4548-6

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  • DOI: https://doi.org/10.1245/s10434-015-4548-6

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