Preoperative risk stratification in endometrial cancer (ENDORISK) by a Bayesian network model: A development and validation study

Abstract

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

Immunohistochemical biomarkers are prognostic relevant in addition to the ESMO-ESGO-ESTRO risk classification in endometrial cancer

“…Within the European Network for Individualized Treatment of Endometrial Cancer (ENITEC), a retrospective multicenter cohort study was performed. The patients were surgically treated between February 1995 and August 2013 at one of the 10 participating ENITEC centers and were identified from a previously published cohort [9,20]. Only patients diagnosed by an expert gynaecological pathologist with complete clinical and pathological data and follow-up of at least 36 months were included, yielding 1199 patients out of ten European hospitals.…”

“…Pre-operative tumor grade and histology were used for analysis, combined with IHC staining of p53, L1CAM, ER and PR according to ENDORISK [9]. Detailed information on tissue processing and IHC analysis is shown in Supplementary S1 method.…”

“…The ESMO-ESGO-ESTRO risk classification can be used pre-operatively to guide the need for lymph node (LN) directed surgery, and post-operatively to define adjuvant treatment. Recently, we published the ENDORISK model showing improved pre-operative risk classification in EC with easy accessible biomarkers integrated in a Bayesian network [9] . This personalized network included immunohistochemical (IHC) expression of p53, L1 cell-adhesion molecule (L1CAM), estrogen receptor (ER), progesterone receptor (PR), and clinical preoperative biomarkers and was established to predict lymph node metastasis (LNM) and outcome pre-operatively.…”

Bayesian network modelling for early diagnosis and prediction of Endometriosis

“…However, this author found limited studies focusing on the signs and symptoms, most often observed in a primary care setting, associated with endometriosis as predictors for diagnosis. Furthermore, this author found Bayesian Networks had been applied as cancer prediction tools but were not utilised for the prediction of Endometriosis as a condition (Reijnen, et al, 2020; Roy, et al, 2015).…”

Immunohistochemical Markers in Endometrial Cancer

“…At the same time, research has expanded into the field of diagnostic imaging to presurgically identify risk factors that may be associated with a more probable lymph node spread of endometrial cancer [ 7 ]. Obviously, it would be desirable to identify the risk factors associated with an advanced stage and/or a higher recurrence rate by analyzing the histology and grade of the tissue obtained from tumor biopsy at the time of the first diagnosis and, therefore, before planning the therapeutic interventions that can be proposed for each woman [ 3 , 8 ].…”