Liu Y, Lu XN, Guo HM, Bao C, Zhang J, Jin YN. Development and validation of a circulating tumor DNA-based optimization-prediction model for short-term postoperative recurrence of endometrial cancer. World J Clin Cases 2024; 12(18): 3385-3394 [DOI: 10.12998/wjcc.v12.i18.3385]
Corresponding Author of This Article
Yu-Ni Jin, MM, Associate Chief Physician, Department of Gynaecology, The First Affiliated Hospital of Kunming Medical University, No. 295 Xichang Road, Kunming 650032, Yunnan Province, China. yunnijin2024@126.com
Research Domain of This Article
Obstetrics & Gynecology
Article-Type of This Article
Retrospective Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
World J Clin Cases. Jun 26, 2024; 12(18): 3385-3394 Published online Jun 26, 2024. doi: 10.12998/wjcc.v12.i18.3385
Development and validation of a circulating tumor DNA-based optimization-prediction model for short-term postoperative recurrence of endometrial cancer
Yuan Liu, Xiao-Ning Lu, Hui-Ming Guo, Chan Bao, Juan Zhang, Yu-Ni Jin
Yuan Liu, Xiao-Ning Lu, Hui-Ming Guo, Chan Bao, Juan Zhang, Yu-Ni Jin, Department of Gynaecology, The First Affiliated Hospital of Kunming Medical University, Kunming 650032, Yunnan Province, China
Author contributions: Liu Y and Jin YN designed the research and wrote the first manuscript; Lu XN, Guo HM, Bao C and Zhang J conceived the research and analyzed the data; Liu Y and Jin YN conducted the analysis and provided guidance for the research; All authors reviewed and approved the final manuscript.
Institutional review board statement: This study was reviewed and approved by the First Affiliated Hospital of Kunming Medical University.
Informed consent statement: Patients did not need to provide informed consent for the study, as anonymous clinical data were used for analysis.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Data sharing statement: The data used in the above analysis are available upon reasonable request from the corresponding authors at yunnijin2024@126.com.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Yu-Ni Jin, MM, Associate Chief Physician, Department of Gynaecology, The First Affiliated Hospital of Kunming Medical University, No. 295 Xichang Road, Kunming 650032, Yunnan Province, China. yunnijin2024@126.com
Received: March 7, 2024 Revised: April 23, 2024 Accepted: May 10, 2024 Published online: June 26, 2024 Processing time: 103 Days and 0.8 Hours
Abstract
BACKGROUND
Endometrial cancer (EC) is a common gynecological malignancy that typically requires prompt surgical intervention; however, the advantage of surgical management is limited by the high postoperative recurrence rates and adverse outcomes. Previous studies have highlighted the prognostic potential of circulating tumor DNA (ctDNA) monitoring for minimal residual disease in patients with EC.
AIM
To develop and validate an optimized ctDNA-based model for predicting short-term postoperative EC recurrence.
METHODS
We retrospectively analyzed 294 EC patients treated surgically from 2015-2019 to devise a short-term recurrence prediction model, which was validated on 143 EC patients operated between 2020 and 2021. Prognostic factors were identified using univariate Cox, Lasso, and multivariate Cox regressions. A nomogram was created to predict the 1, 1.5, and 2-year recurrence-free survival (RFS). Model performance was assessed via receiver operating characteristic (ROC), calibration, and decision curve analyses (DCA), leading to a recurrence risk stratification system.
RESULTS
Based on the regression analysis and the nomogram created, patients with postoperative ctDNA-negativity, postoperative carcinoembryonic antigen 125 (CA125) levels of < 19 U/mL, and grade G1 tumors had improved RFS after surgery. The nomogram’s efficacy for recurrence prediction was confirmed through ROC analysis, calibration curves, and DCA methods, highlighting its high accuracy and clinical utility. Furthermore, using the nomogram, the patients were successfully classified into three risk subgroups.
CONCLUSION
The nomogram accurately predicted RFS after EC surgery at 1, 1.5, and 2 years. This model will help clinicians personalize treatments, stratify risks, and enhance clinical outcomes for patients with EC.
Core Tip: This study introduces a predictive nomogram for endometrial cancer recurrence postsurgery, incorporating circulating tumor DNA, carcinoembryonic antigen 125, and tumor grade. The model, validated through receiver operating characteristic and decision curve analysis, accurately forecasts short-term recurrence-free survival and aids in risk stratification.
