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@article{2210837,
author = {Vinklerová, Petra and Ovesná, Petra and Hausnerová, Jitka and Pijnenborg, Johanna M. A. and Lucas, Peter J. F. and Casper, Reijnen and Stephanie, Vrede and Weinberger, Vít},
article_location = {Lausanne},
article_number = {August 2022},
doi = {http://dx.doi.org/10.3389/fonc.2022.939226},
keywords = {Bayesian networks model; disease-specific survival; endometrial cancer; prognosis; risk stratification; sentinel node biopsy; lymph node metastasis},
language = {eng},
issn = {2234-943X},
journal = {Frontiers in Oncology},
title = {External validation study of endometrial cancer preoperative risk stratification model (ENDORISK)},
url = {https://www.frontiersin.org/articles/10.3389/fonc.2022.939226/full},
volume = {12},
year = {2022}
}
TY - JOUR
ID - 2210837
AU - Vinklerová, Petra - Ovesná, Petra - Hausnerová, Jitka - Pijnenborg, Johanna M. A. - Lucas, Peter J. F. - Casper, Reijnen - Stephanie, Vrede - Weinberger, Vít
PY - 2022
TI - External validation study of endometrial cancer preoperative risk stratification model (ENDORISK)
JF - Frontiers in Oncology
VL - 12
IS - August 2022
SP - 1-10
EP - 1-10
PB - Frontiers
SN - 2234943X
KW - Bayesian networks model
KW - disease-specific survival
KW - endometrial cancer
KW - prognosis
KW - risk stratification
KW - sentinel node biopsy
KW - lymph node metastasis
UR - https://www.frontiersin.org/articles/10.3389/fonc.2022.939226/full
N2 - 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.
ER -
VINKLEROVÁ, Petra, Petra OVESNÁ, Jitka HAUSNEROVÁ, Johanna M. A. PIJNENBORG, Peter J. F. LUCAS, Reijnen CASPER, Vrede STEPHANIE and Vít WEINBERGER. External validation study of endometrial cancer preoperative risk stratification model (ENDORISK). \textit{Frontiers in Oncology}. Lausanne: Frontiers, 2022, vol.~12, August 2022, p.~1-10. ISSN~2234-943X. Available from: https://dx.doi.org/10.3389/fonc.2022.939226.
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