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01-12-2015 | Gynecologic Oncology

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

Authors: Sofiane Bendifallah, MD, Geoffroy Canlorbe, MD, Enora Laas, MD, Florence Huguet, MD, PhD, Charles Coutant, MD, PhD, Delphine Hudry, MD, Olivier Graesslin, MD, PhD, Emilie Raimond, MD, Cyril Touboul, MD, PhD, Pierre Collinet, MD, PhD, Annie Cortez, MD, Géraldine Bleu, MD, Emile Daraï, MD, PhD, Marcos Ballester, MD, PhD

Published in: Annals of Surgical Oncology | Issue 13/2015

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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Title: A Predictive Model Using Histopathologic Characteristics of Early-Stage Type 1 Endometrial Cancer to Identify Patients at High Risk for Lymph Node Metastasis
Authors: Sofiane Bendifallah, MD
Geoffroy Canlorbe, MD
Enora Laas, MD
Florence Huguet, MD, PhD
Charles Coutant, MD, PhD
Delphine Hudry, MD
Olivier Graesslin, MD, PhD
Emilie Raimond, MD
Cyril Touboul, MD, PhD
Pierre Collinet, MD, PhD
Annie Cortez, MD
Géraldine Bleu, MD
Emile Daraï, MD, PhD
Marcos Ballester, MD, PhD
Publication date: 01-12-2015
Publisher: Springer US
Published in: Annals of Surgical Oncology / Issue 13/2015
Print ISSN: 1068-9265
Electronic ISSN: 1534-4681
DOI: https://doi.org/10.1245/s10434-015-4548-6

Keynote webinar | Spotlight on medication adherence

Springer Medicine

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

Abstract

Background

Methods

Results

Conclusion

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Background

Methods

Results

Conclusion

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