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Predicting Lymph Node Involvement in Borderline Ovarian Tumors with a Quantitative Model and Nomogram: A Retrospective Cohort Study

Authors Zhang M, Zhou F, He Y, Tao X, Hua K, Ding J

Received 19 October 2020

Accepted for publication 9 January 2021

Published 16 February 2021 Volume 2021:13 Pages 1529—1539


Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 2

Editor who approved publication: Prof. Dr. Xueqiong Zhu

Menglei Zhang,1,2,* Fangyue Zhou,1,* Yuan He,3 Xiang Tao,4 Keqin Hua,1,2 Jingxin Ding1,2

1Department of Gynecology, Obstetrics and Gynecology Hospital of Fudan University, Shanghai, 200011, People’s Republic of China; 2Shanghai Key Laboratory of Female Reproductive Endocrine Related Diseases, Shanghai, 200011, People’s Republic of China; 3Public Health School of Fudan University, Shanghai, 200032, People’s Republic of China; 4Department of Pathology, Obstetrics and Gynecology Hospital of Fudan University, Shanghai, 200011, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Jingxin Ding
Department of Gynecology, The Obstetrics and Gynecology Hospital of Fudan University, 128 Shen-Yang Road, Shanghai, 200011, People’s Republic of China
Tel +86-13918206746
Fax +86-21-63455090

Purpose: This study aimed to establish a predictive model for lymph node involvement (LNI) in patients with borderline ovarian tumor (BOT) using clinicopathological factors.
Patients and Methods: We collected clinical data from consecutive patients who underwent lymphadenectomy for BOT between 2001 and 2018 and analyzed their clinicopathological features. Multivariate logistic regression was used to identify all independent risk factors associated with LNI; these were then incorporated into the prediction model.
Results: In total, we included 248 patients with BOT who were undergoing lymphadenectomy. These were divided into a training cohort (n=174) and a validation cohort (n=74). When considering histopathological data, 16 and 5 patients were identified to have LNI in the training and validation cohorts, respectively. Overall, 13.5% (21/156) patients with serous BOT had LNI while 0% (0/92) patients with non-serous BOT had LNI. We identified several predictors of LNI: the largest tumor being ≥ 12.2cm in diameter, the presence of lesions on the ovarian surface, and the presence of pelvic or abdominal lesions. We created a prediction model and nomogram that incorporated these three risk factors for serous BOT. The model achieved good discriminatory abilities of 0.951 and 0.848 when predicting LNI in the training and validation cohorts, respectively. The LNI-predicting nomogram had an area under curve (AUC) of 0.951 and generated well-fitted calibration curves.
Conclusion: Non-serous BOT may not require lymphadenectomy as part of surgical staging. The individual risk of LNI in patients with serous BOT can be accurately estimated using our prediction model and nomogram. The use of LNI criteria provides a practical way to support the clinician in making an optimal decision relating to surgical scope for patients with BOT.

Keywords: lymph node involvement, borderline ovarian tumor, lymphadenectomy, prediction model, nomogram

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