Objective To determine whether gross tumor volume (GTV) and the maximum diameter of resectable cervical cancer at magnetic resonance imaging (MRI) could predict lymph node metastasis (LNM) and lymphovascular space invasion (LVSI).
Materials and Methods A total of 315 consecutive patients with cervical cancer were retrospectively identified. Gross tumor volume and the maximum diameter of tumor were evaluated on MRI. Univariate and multivariate logistic regression analyses were performed to determine whether tumor size could predict LNM and LVSI. Cutoffs of GTV, maximum diameter, and the International Federation of Gynecology and Obstetrics (FIGO) classification of tumor were first investigated in 255 patients (group A) and then validated in an independent cohort of 60 patients (group B) using area under the receiver operating characteristic curve (AUC) analysis for predicting the presence of LNM and LVSI.
Results Univariate analysis showed that GTV and the maximum diameter of tumor could predict LNM and LVSI (all P < 0.0001). Multivariate analyses indicated GTV as an independent risk factor of LNM and LVSI (all P < 0.0001). In group A, GTV, the maximum diameter, and the FIGO stage could identify LNM (AUC, 0.813, 0.741, and 0.69, respectively) and LVSI (AUC, 0.806, 0.751, and 0.684, respectively). In group B, GTV, the maximum diameter, and the FIGO stage could help identify LNM (AUC, 0.902, 0.825, and 0.759, respectively) and LVSI (AUC, 0.771, 0.748, and 0.700, respectively).
Conclusions Gross tumor volume and the maximum diameter of resectable cervical cancer at MRI demonstrated capability in predicting LNM and LVSI, which were more accurate than FIGO stage.
- Tumor size
- Cervical cancer
- Lymph node metastasis
- Lymphovascular space invasion
This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.
Statistics from Altmetric.com
This study was supported by the Project of Huimin Technology of Chengdu (No. 2015-HM01-00082-SF and No. 2015-HM01-00164-SF).
The authors declare no conflicts of interest or industry supports of the project in this study.
Guang-wen Chen, Hong Pu, and Guo-hui Xu contributed equally to this study.
If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.