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:
https://doi.org/10.1371/journal.pmed.1003111
Souhrn
Background
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.
Conclusions
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
Zdroje
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