Predicting Lymph Node Involvement in Borderline Ovarian Tumors with a Quantitative Model and Nomogram: A
Menglei Zhang1,2, Fangyue Zhou1, Yuan He3
1Department of Gynecology, Obstetrics and Gynecology Hospital of Fudan University, Shanghai, 200011, People's Republic of China.
Cancer Management and Research
|February 24, 2021
Summary
This study developed a predictive model to identify lymph node involvement in borderline ovarian tumors (BOT). Non-serous BOT may not need lymphadenectomy, while serous BOT risk is accurately estimated by the model.
Area of Science:
- Gynecologic Oncology
- Surgical Pathology
- Predictive Modeling
Background:
- Borderline ovarian tumors (BOT) present a diagnostic challenge.
- Accurate staging is crucial for optimal patient management.
- Predicting lymph node involvement (LNI) aids in surgical decision-making.
Purpose of the Study:
- To develop and validate a predictive model for lymph node involvement (LNI) in patients with borderline ovarian tumors (BOT).
- To identify clinicopathological factors associated with LNI in BOT.
- To guide surgical staging decisions for BOT patients.
Main Methods:
- Retrospective analysis of clinical data from 248 BOT patients undergoing lymphadenectomy (2001-2018).
- Multivariate logistic regression to identify independent risk factors for LNI.
- Development of a prediction model and nomogram incorporating identified risk factors.
Main Results:
- LNI was observed in 13.5% of serous BOT and 0% of non-serous BOT.
- Predictors of LNI included largest tumor diameter (≥12.2cm), ovarian surface lesions, and pelvic/abdominal lesions.
- The prediction model demonstrated high discriminatory ability (AUC 0.951 training, 0.848 validation).
Conclusions:
- Non-serous BOT may not necessitate lymphadenectomy during surgical staging.
- The developed model and nomogram accurately estimate LNI risk in serous BOT.
- This tool supports clinical decisions regarding the extent of surgical staging for BOT.
