Li Suyu, Chen Yusha, Huang Xizhen, Chen Xiaoying, Li Xiaoyang, Zhou Guangrun, Huang Liyuan, Huang Qiuyuan, Chen Lingsi, Xie Zhonghang, Zheng Xiangqin
Department of Radiation Oncology, College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University, Fujian Maternity and Child Health Hospital, Fuzhou, China.
Cervical Disease Diagnosis and Treatment Health Center, Fujian Maternity and Child Health Hospital, College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University, Fuzhou, China.
Front Oncol. 2025 Mar 6;15:1505512. doi: 10.3389/fonc.2025.1505512. eCollection 2025.
Lymph-vascular space invasion (LVSI) is a crucial prognostic factor in cervical cancer (CC), and its assessment is essential for developing personalized treatment strategies.
The primary objective of this study was to focused on constructing LVSI predictive model based on clinical indicators and evaluating its predictive performance across different FIGO staging cohorts.
We included 691 patients, with 348 patients having 2009 FIGO stage IB1-IIA2 CC assigned to Cohort 1, and 343 patients with 2018 FIGO stage IB1-IIIC1r CC assigned to Cohort 2. In Cohort 1, univariable and multivariable regression analyses, along with Mallows' Cp, R squared-R, and LASSO, were used to select variables forming Model 1. Model 2 included the FIGO stage. We compared the contribution of different FIGO stages to the LVSI prediction model in both cohorts. The final LVSI prediction model for the entire cohort was constructed using selected variables and risk stratification was established. The models were evaluated through internal validations using ROC curves, C-index, Clinical Impact Curve (CIC), and Decision Curve.
Five variables were incorporated into Model 1: age, Pathology, Depth of Stromal Invasion (DSI), SCC-Ag, and Lactate Dehydrogenase (LDH). Model 2 was established by incorporating the FIGO staging system. Compared with the two models, there was no significant difference in ROC, ΔC-index and ΔNRI. Adding FIGO clinical staging did not significantly improve predictive value. Model 1's variable were included in the nomogram for the combined cohort. The AUC for the model-development cohort and validation cohort was 0.754 (95% CI: 0.711, 0.798) and 0.789 (95% CI: 0.727, 0.852), respectively. In both cohorts, risk stratification effectively distinguished the high-risk group, which had a significantly higher proportion of positive cases compared to the low and middle-risk groups (p < 0.01).
Our nomogram predictive model demonstrates robust LVSI prediction performance across different staging systems.
淋巴管间隙浸润（LVSI）是宫颈癌（CC）的一个关键预后因素，对其进行评估对于制定个性化治疗策略至关重要。
本研究的主要目的是基于临床指标构建LVSI预测模型，并评估其在不同国际妇产科联盟（FIGO）分期队列中的预测性能。
我们纳入了691例患者，其中348例2009年FIGO分期为IB1-IIA2期的CC患者被分配到队列1，343例2018年FIGO分期为IB1-IIIC1r期的CC患者被分配到队列2。在队列1中，采用单变量和多变量回归分析，以及马洛斯Cp、决定系数R和套索回归来选择构成模型1的变量。模型2纳入了FIGO分期。我们比较了不同FIGO分期对两个队列中LVSI预测模型的贡献。使用选定变量构建了整个队列的最终LVSI预测模型，并建立了风险分层。通过使用ROC曲线、C指数、临床影响曲线（CIC）和决策曲线进行内部验证来评估模型。
五个变量被纳入模型1：年龄、病理类型、间质浸润深度（DSI）、鳞状细胞癌抗原（SCC-Ag）和乳酸脱氢酶（LDH）。通过纳入FIGO分期系统建立了模型2。与这两个模型相比，ROC、ΔC指数和Δ净重新分类指数（NRI）没有显著差异。添加FIGO临床分期并没有显著提高预测价值。模型1的变量被纳入了联合队列的列线图中。模型开发队列和验证队列的曲线下面积（AUC）分别为0.754（95%置信区间：0.711，0.798）和0.789（95%置信区间：0.727，0.852）。在两个队列中，风险分层有效地区分了高危组，与低危和中危组相比，高危组的阳性病例比例显著更高（p<0.01）。
我们的列线图预测模型在不同分期系统中均表现出强大的LVSI预测性能。
