External validation study of endometrial cancer preoperative risk stratification model (ENDORISK)

Petra Vinklerová; Petra Ovesná; Jitka Hausnerová; Johanna M A Pijnenborg; Peter J F Lucas; Casper Reijnen; Stephanie Vrede; Vít Weinberger

doi:10.3389/fonc.2022.939226

External validation study of endometrial cancer preoperative risk stratification model (ENDORISK)

Front Oncol. 2022 Aug 3:12:939226. doi: 10.3389/fonc.2022.939226. eCollection 2022.

Authors

Petra Vinklerová¹, Petra Ovesná², Jitka Hausnerová³, Johanna M A Pijnenborg⁴, Peter J F Lucas⁵, Casper Reijnen⁶, Stephanie Vrede⁴, Vít Weinberger¹

Affiliations

¹ Department of Gynecology and Obstetrics, University Hospital Brno and Faculty of Medicine, Masaryk University, Brno, Czechia.
² Institute of Biostatistics and Analyses, Faculty of Medicine, Masaryk University, Brno, Czechia.
³ Department of Pathology, University Hospital Brno and Faculty of Medicine, Masaryk University, Brno, Czechia.
⁴ Department of Obstetrics and Gynecology, Radboud University Medical Center, Nijmegen, Netherlands.
⁵ Department of Data Science, University of Twente, Enschede, Netherlands.
⁶ Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, Netherlands.

Abstract

Introduction: Among industrialized countries, endometrial cancer is a common malignancy with generally an excellent outcome. To personalize medicine, we ideally compile as much information as possible concerning patient prognosis prior to effecting an appropriate treatment decision. Endometrial cancer preoperative risk stratification (ENDORISK) is a machine learning-based computational Bayesian networks model that predicts lymph node metastasis and 5-year disease-specific survival potential with percentual probability. Our objective included validating ENDORISK effectiveness in our patient cohort, assessing its application in the current use of sentinel node biopsy, and verifying its accuracy in advanced stages.

Methods: The ENDORISK model was evaluated with a retrospective cohort of 425 patients from the University Hospital Brno, Czech Republic. Two hundred ninety-nine patients were involved in our disease-specific survival analysis; 226 cases with known lymph node status were available for lymph node metastasis analysis. Patients were included undergoing either pelvic lymph node dissection (N = 84) or sentinel node biopsy (N =70) to explore the accuracy of both staging procedures.

Results: The area under the curve was 0.84 (95% confidence interval [CI], 0.77-0.9) for lymph node metastasis analysis and 0.86 (95% CI, 0.79-0.93) for 5-year disease-specific survival evaluation, indicating quite positive concordance between prediction and reality. Calibration plots to visualize results demonstrated an outstanding predictive value for low-risk cancers (grades 1-2), whereas outcomes were underestimated among high-risk patients (grade 3), especially in disease-specific survival. This phenomenon was even more obvious when patients were subclassified according to FIGO clinical stages.

Conclusions: Our data confirmed ENDORISK model's laudable predictive ability, particularly among patients with a low risk of lymph node metastasis and expected favorable survival. For high-risk and/or advanced stages, the ENDORISK network needs to be additionally trained/improved.

Keywords: Bayesian networks model; disease-specific survival; endometrial cancer; lymph node metastasis; prognosis; risk stratification; sentinel node biopsy.