Int J Med Sci 2021; 18(13):2828-2834. doi:10.7150/ijms.60718 This issue

Research Paper

Prediction of lymphovascular space invasion in patients with endometrial cancer

Sang Il Kim, MD1, Joo Hee Yoon, PhD1, Sung Jong Lee, PhD2, Min Jong Song, PhD3, Jin Hwi Kim, PhD4, Hae Nam Lee, PhD5, Gyul Jung, MD6, Ji Geun Yoo, MD6✉

1. Department of Obstetrics and Gynecology, St. Vincent's hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
2. Department of Obstetrics and Gynecology, Seoul St. Mary's hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
3. Department of Obstetrics and Gynecology, Yeouido St. Mary's hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
4. Department of Obstetrics and Gynecology, Uijeongbu St. Mary's hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
5. Department of Obstetrics and Gynecology, Buchen St. Mary's hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
6. Department of Obstetrics and Gynecology, Daejeon St. Mary's hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.

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Citation:
Kim SI, Yoon JH, Lee SJ, Song MJ, Kim JH, Lee HN, Jung G, Yoo JG. Prediction of lymphovascular space invasion in patients with endometrial cancer. Int J Med Sci 2021; 18(13):2828-2834. doi:10.7150/ijms.60718. Available from https://www.medsci.org/v18p2828.htm

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Abstract

Graphic abstract

Objective: Predict the presence of lymphovascular space invasion (LVSI), using uterine factors such as tumor diameter (TD), grade, and depth of myometrial invasion (MMI). Develop a predictive model that could serve as a marker of LVSI in women with endometrial cancer (EC).

Methods: Data from 888 patients with endometrioid EC who were treated between January 2009 and December 2018 were reviewed. The patients' data were retrieved from six institutions. We assessed the differences in the clinicopathological characteristics between patients with and without LVSI. We performed logistic regression analysis to determine which clinicopathological characteristics were the risk factors for positive LVSI status and to estimate the odds ratio (OR) for each covariate. Using the risk factors and OR identified through this process, we created a model that could predict LVSI and analyzed it further using receiver operating characteristic curve analysis.

Results: In multivariate logistic regression analysis, tumor size (P = 0.027), percentage of MMI (P < 0.001), and presence of cervical stromal invasion (P = 0.002) were identified as the risk factors for LVSI. Based on the results of multivariate logistic regression analysis, we developed a simplified LVSI prediction model for clinical use. We defined the “LVSI index” as “TD×%MMI×tumor grade×cervical stromal involvement.” The area under curve was 0.839 (95% CI= 0.809-0.869; sensitivity, 74.1%; specificity, 80.5%; negative predictive value, 47.3%; positive predictive value, 8.6%; P < 0.001), and the optimal cut-off value was 200.

Conclusion: Using the modified risk index of LVSI, it is possible to predict the presence of LVSI in women with endometrioid endometrial cancer. Our prediction model may be an appropriate tool for integration into the clinical decision-making process when assessed either preoperatively or intraoperatively.

Keywords: endometrial cancer, lymphovascular space invasion, LVSI