Wang Zihan, Chu Ran, Wu Namei, Yuan Ming, Song Xiao, Tian Wei, Yang Chunrun, Wan Jipeng, Wang Guoyun
Department of Gynecology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, P.R. China.
Department of Obstetrics and Gynecology, Qilu Hospital of Shandong University Dezhou Hospital, Dezhou, Shandong, P.R. China.
Ther Adv Med Oncol. 2025 Jul 24;17:17588359251359461. doi: 10.1177/17588359251359461. eCollection 2025.
The combination patterns of pathological intermediate-risk factors and the choice of adjuvant therapy for early-stage cervical cancer (CC) remain controversial.
To develop and validate nomogram-based prediction models incorporating pathological intermediate-risk factors to predict survival outcomes and optimize adjuvant therapy strategies in early-stage CC patients.
A multicenter retrospective study.
A total of 1104 patients with stage IB-IIA CC who underwent primary surgical treatment and had no pathological high-risk factors were retrospectively enrolled from three tertiary medical centers in China between January 2005 and December 2017. Patients were randomly assigned to development and validation cohorts (approximately 3:1 ratio). Prognostic models for disease-free survival (DFS) and overall survival (OS) were developed by Cox proportional hazards regression and visualized using nomograms.
In this study, four prognostic models were developed incorporating different combinations of five key variables: lymphovascular space involvement (LVSI), stromal invasion (SI), tumor size (TS), histological type, and adjuvant therapy. Among these, Model 4 (LVSI + SI + TS + histological type + adjuvant therapy) demonstrated the highest discriminatory performance, with C-indices of 0.79 for both DFS and OS in the development cohort, and 0.84 for DFS and 0.77 for OS in the validation cohort. Model 4 also effectively stratified patients into prognostic risk groups in both cohorts, with high-risk patients exhibiting significantly worse DFS (development cohort: < 0.0001; validation cohort: = 0.0011) and OS (development cohort: < 0.0001; validation cohort: = 0.0036) compared to low-risk patients.
The nomogram models developed in this study may provide individualized prognostic predictions for early-stage CC patients, potentially facilitating personalized decision-making regarding adjuvant therapy, though further validation in diverse patient cohorts and prospective studies is needed.
早期宫颈癌（CC）病理中间风险因素的组合模式及辅助治疗的选择仍存在争议。
建立并验证基于列线图的预测模型，纳入病理中间风险因素，以预测早期CC患者的生存结局并优化辅助治疗策略。
一项多中心回顾性研究。
2005年1月至2017年12月期间，从中国三个三级医疗中心回顾性纳入1104例接受了初次手术治疗且无病理高危因素的IB-IIA期CC患者。患者被随机分配至开发队列和验证队列（比例约为3:1）。通过Cox比例风险回归建立无病生存（DFS）和总生存（OS）的预后模型，并使用列线图进行可视化展示。
在本研究中，纳入五个关键变量（脉管间隙浸润[LVSI]、间质浸润[SI]、肿瘤大小[TS]、组织学类型和辅助治疗）不同组合建立了四个预后模型。其中，模型4（LVSI + SI + TS + 组织学类型 +辅助治疗）显示出最高的区分性能，在开发队列中DFS和OS的C指数均为0.79，在验证队列中DFS的C指数为0.84，OS的C指数为0.77。模型4在两个队列中也有效地将患者分层为预后风险组，与低风险患者相比，高风险患者在DFS（开发队列：<0.0001；验证队列：= 0.0011）和OS（开发队列：<0.0001；验证队列：= 0.0036）方面表现明显更差。
本研究中开发的列线图模型可为早期CC患者提供个体化的预后预测，可能有助于辅助治疗的个性化决策，不过需要在不同患者队列和前瞻性研究中进一步验证。
