External validation study of endometrial cancer preoperative risk stratification model (ENDORISK)
IntroductionAmong 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...
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Frontiers Media S.A.
2022-08-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2022.939226/full |
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| author | Petra Vinklerová Petra Ovesná Jitka Hausnerová Johanna M. A. Pijnenborg Peter J. F. Lucas Casper Reijnen Stephanie Vrede Vít Weinberger |
| author_facet | Petra Vinklerová Petra Ovesná Jitka Hausnerová Johanna M. A. Pijnenborg Peter J. F. Lucas Casper Reijnen Stephanie Vrede Vít Weinberger |
| author_sort | Petra Vinklerová |
| collection | DOAJ |
| description | IntroductionAmong 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.MethodsThe 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.ResultsThe 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.ConclusionsOur 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. |
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| id | doaj.art-6f2e7615f68f4c819aee78bed95cbfab |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-04-13T19:29:02Z |
| publishDate | 2022-08-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Oncology |
| spelling | doaj.art-6f2e7615f68f4c819aee78bed95cbfab2022-12-22T02:33:15ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2022-08-011210.3389/fonc.2022.939226939226External validation study of endometrial cancer preoperative risk stratification model (ENDORISK)Petra Vinklerová0Petra Ovesná1Jitka Hausnerová2Johanna M. A. Pijnenborg3Peter J. F. Lucas4Casper Reijnen5Stephanie Vrede6Vít Weinberger7Department of Gynecology and Obstetrics, University Hospital Brno and Faculty of Medicine, Masaryk University, Brno, CzechiaInstitute of Biostatistics and Analyses, Faculty of Medicine, Masaryk University, Brno, CzechiaDepartment of Pathology, University Hospital Brno and Faculty of Medicine, Masaryk University, Brno, CzechiaDepartment of Obstetrics and Gynecology, Radboud University Medical Center, Nijmegen, NetherlandsDepartment of Data Science, University of Twente, Enschede, NetherlandsDepartment of Radiation Oncology, Radboud University Medical Center, Nijmegen, NetherlandsDepartment of Obstetrics and Gynecology, Radboud University Medical Center, Nijmegen, NetherlandsDepartment of Gynecology and Obstetrics, University Hospital Brno and Faculty of Medicine, Masaryk University, Brno, CzechiaIntroductionAmong 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.MethodsThe 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.ResultsThe 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.ConclusionsOur 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.https://www.frontiersin.org/articles/10.3389/fonc.2022.939226/fullBayesian networks modeldisease-specific survivalendometrial cancerprognosisrisk stratificationsentinel node biopsy |
| spellingShingle | Petra Vinklerová Petra Ovesná Jitka Hausnerová Johanna M. A. Pijnenborg Peter J. F. Lucas Casper Reijnen Stephanie Vrede Vít Weinberger External validation study of endometrial cancer preoperative risk stratification model (ENDORISK) Frontiers in Oncology Bayesian networks model disease-specific survival endometrial cancer prognosis risk stratification sentinel node biopsy |
| title | External validation study of endometrial cancer preoperative risk stratification model (ENDORISK) |
| title_full | External validation study of endometrial cancer preoperative risk stratification model (ENDORISK) |
| title_fullStr | External validation study of endometrial cancer preoperative risk stratification model (ENDORISK) |
| title_full_unstemmed | External validation study of endometrial cancer preoperative risk stratification model (ENDORISK) |
| title_short | External validation study of endometrial cancer preoperative risk stratification model (ENDORISK) |
| title_sort | external validation study of endometrial cancer preoperative risk stratification model endorisk |
| topic | Bayesian networks model disease-specific survival endometrial cancer prognosis risk stratification sentinel node biopsy |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2022.939226/full |
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