Multicenter study develops a predictive model to identify low-risk patients prior to surgery, suggesting reduced need for lymphadenectomy.
Abstract : Objective Systematic pelvic and para-aortic lymphadenectomy is recommended for surgical staging in presumed early-stage epithelial ovarian cancer, although lymph node metastases occur in only a minority of patients and the procedure is associated with significant morbidity. We aimed to develop and validate a predictive model based on variables available prior to lymphadenectomy within a two-step surgical strategy for identifying patients at very low risk of lymph node metastasis. Methods We conducted a multicenter retrospective study including patients with presumed early-stage high-grade epithelial ovarian cancer treated in 12 French centers between January 2018 and December 2023. All patients underwent comprehensive surgical staging including systematic pelvic and para-aortic lymphadenectomy. Three prediction algorithms were evaluated: logistic regression, random forest, and extreme gradient boosting (XGBoost). Model performance was assessed using repeated stratified 5×5 cross-validation. A predefined probability threshold of 0.98 defined very low risk of nodal metastasis (≤2%). Results A total of 219 patients were included. Lymph node metastases were identified in 26 patients (12%). Among the evaluated algorithms, XGBoost achieved the best performance. The final model included five preoperative variables: cytology, bilateral ovarian involvement, age ≥60 years, BMI ≥25, and CA-125 ≥100 IU/mL. Using the predefined threshold, the model classified 42% of patients as very low risk of lymph node metastasis with only one false-negative prediction. Conclusions This multicenter study supports the feasibility of a simple preoperative model to identify a substantial subgroup of patients with apparent early-stage ovarian cancer at very low risk of lymph node metastasis. If externally validated, this approach may help refine patient selection and reduce unnecessary lymphadenectomy.
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Descargues et al. (2026) studied this question.
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