Ultrasound‐based risk model for preoperative prediction of lymph‐node metastases in women with endometrial cancer: model‐development study
Open accessAbstract
Objective: To develop a preoperative risk model, using endometrial biopsy results and clinical and ultrasound variables, to predict the individual risk of lymph‐node metastases in women with endometrial cancer. Methods: A mixed‐effects logistic regression model for prediction of lymph‐node metastases was developed in 1501 prospectively included women with endometrial cancer undergoing transvaginal ultrasound examination before surgery, from 16 European centers. Missing data, including missing lymph‐node status, were imputed. Discrimination, calibration and clinical utility of the model were evaluated using leave‐center‐out cross validation. The predictive performance of the model was compared with that of risk classification from endometrial biopsy alone (high‐risk defined as endometrioid cancer Grade 3/non‐endometrioid cancer) or combined endometrial biopsy and ultrasound (high‐risk defined as endometrioid cancer Grade 3/non‐endometrioid cancer/deep myometrial invasion/cervical stromal invasion/extrauterine spread). Results: Lymphadenectomy was performed in 691 women, of whom 127 had lymph‐node metastases. The model for prediction of lymph‐node metastases included the predictors age, duration of abnormal bleeding, endometrial biopsy result, tumor extension and tumor size according to ultrasound and undefined tumor with an unmeasurable endometrium. The model's area under the curve was 0.73 (95% CI, 0.68–0.78), the calibration slope was 1.06 (95% CI, 0.79–1.34) and the calibration intercept was 0.06 (95% CI, –0.15 to 0.27). Using a risk threshold for lymph‐node metastases of 5% compared with 20%, the model had, respectively, a sensitivity of 98% vs 48% and specificity of 11% vs 80%. The model had higher sensitivity and specificity than did classification as high‐risk, according to endometrial biopsy alone (50% vs 35% and 80% vs 77%, respectively) or combined endometrial biopsy and ultrasound (80% vs 75% and 53% vs 52%, respectively). The model's clinical utility was higher than that of endometrial biopsy alone or combined endometrial biopsy and ultrasound at any given risk threshold. Conclusions: Based on endometrial biopsy results and clinical and ultrasound characteristics, the individual risk of lymph‐node metastases in women with endometrial cancer can be estimated reliably before surgery. The model is superior to risk classification by endometrial biopsy alone or in combination with ultrasound. Copyright © 2019 ISUOG. Published by John Wiley & Sons Ltd.
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Several original studies published in this Journal have used AI methodology to evaluate adnexal masses, risk of lymph node metastases in endometrial cancer, pelvic organ function, breast lesions, aneuploidy risk, fetal lung maturity, perinatal outcome, shoulder dystocia, brain damage, gestational age in late pregnancy, and classify standard fetal brain images as normal or abnormal.
“Several original studies published in this Journal have used AI methodology to evaluate adnexal masses 21 , the risk of lymph node metastases in endometrial cancer 22 , pelvic organ function 23 , 24 and breast lesions 25 , 26 , 27 , assess aneuploidy risk 28 , predict fetal lung maturity 29 , perinatal outcome 30 , shoulder dystocia 31 and brain damage 32 , estimate gestational age in late pregnan”
From Introduction to artificial intelligence in ultrasound imaging in obstetrics and gynecology (2020) · cites this paper as [22]
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Introduction to artificial intelligence in ultrasound imaging in obstetrics and gynecology
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- Authors
- Eriksson, L. S. E., Epstein, E., Testa, A. C., Fischerova, D., Valentin, L., Sladkevicius, P., Franchi, D., Frühauf, F., Fruscio, R., Haak, L. A., Opolskiene, G., Mascilini, F., Alcazar, J. L., Van Holsbeke, C., Chiappa, V., Bourne, T., Lindqvist, P. G., Van Calster, B., Timmerman, D., Verbakel, J. Y., Van den Bosch, T., & Wynants, L.
- Affiliations
- Karolinska University Hospital (SE)
- Karolinska Institutet (SE)
- Università Cattolica del Sacro Cuore (IT)
- Department of Obstetrics and Gynecology, First Faculty of Medicine Charles University Prague Czech Republic
- Lund University (SE)
- European Institute of Oncology (IT)
- Charles University (CZ)
- University of Milan (IT)
- Institute for the Care of Mother and Child Prague Czech Republic
- Vilnius University Hospital Santariskiu Klinikos (LT)
- Agostino Gemelli University Polyclinic (IT)
- Department of Obstetrics and Gynecology Clinica Universidad de Navarra Pamplona Spain
- Department of Obstetrics and Gynecology Ziekenhuis Oost‐Limburg Genk Belgium
- Fondazione IRCCS Istituto Nazionale dei Tumori (IT)
- Queen Charlotte's and Chelsea Hospital (GB)
- Department of Development and Regeneration KU Leuven Leuven Belgium
- Universitair Ziekenhuis Leuven (BE)
- University of Oxford (GB)
- Department of Obstetrics and Gynecology University Hospital Leuven Leuven Belgium
- Maastricht University (NL)
- Corresponding author
- Eriksson, L. S. E.
- ORCID
- Venue
- Ultrasound in Obstetrics & Gynecology
- Publisher
- Wiley
- Published
- 2020-09
- Volume
- 56
- Issue
- 3
- Pages
- 443-452
- ISSN
- 0960-7692 (ISSN-L)
- 1469-0705
- OpenAlex
- W2996163026
- Cited by
- 24
- References
- 39
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 7 · 2023: 6 · 2022: 4 · 2021: 1 · 2020: 2
- FWCI
- 1.91
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Ultrasound in Obstetrics and Gynecology (publishedVersion)
- PubMed (publishedVersion)
- Research Publications (Maastricht University) (publishedVersion, other-oa)
- Lirias (KU Leuven) (submittedVersion, other-oa)
- BOA (University of Milano-Bicocca) (submittedVersion)
- Lund University Publications (Lund University) (submittedVersion)
- Spiral (Imperial College London) (submittedVersion)
- Research Publications (Maastricht University) (submittedVersion, other-oa)
- Funders
- Radiumhemmets Forskningsfonder (154112)
- Topics
- Health Sciences › Medicine › Obstetrics and Gynecology › Endometrial and Cervical Cancer Treatments
- Health Sciences › Medicine › Reproductive Medicine › Ovarian cancer diagnosis and treatment
- Health Sciences › Medicine › Obstetrics and Gynecology › Uterine Myomas and Treatments
- Keywords
- endometrial cancer, lymph node metastasis, preoperative risk prediction, transvaginal ultrasound, endometrial biopsy, risk model, lymph node dissection, metastasis, tumor size, clinical utility, endometrioid carcinoma, deep myometrial invasion, cervical stromal invasion, extrauterine spread, discrimination and calibration, mixed-effects logistic regression
- MeSH terms
- Adult, Aged, Aged, 80 and over, Female, Humans, Lymph Nodes, Lymphatic Metastasis, Middle Aged, Neoplasm Staging, Prospective Studies, Risk Factors, Sensitivity and Specificity, Ultrasonography, Cohort Studies, Linear Models, Endometrial Neoplasms, Carcinoma, Endometrioid
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