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Updated: Sep 13, 2025
Author Spotlight: Advancing Early Detection and Treatment of Gastrointestinal Tumors
Published on: February 16, 2024
Development and validation of prognostic prediction models for early-stage cervical cancer patients based on
Zihan Wang1, Ran Chu1, Namei Wu1
1Department of Gynecology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, P.R. China.
Background:
The combination patterns of pathological intermediate-risk factors and the choice of adjuvant therapy for early-stage cervical cancer (CC) remain controversial.
Objectives:
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.
Design:
A multicenter retrospective study.
Methods:
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.
Results:
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: p < 0.0001; validation cohort: p = 0.0011) and OS (development cohort: p < 0.0001; validation cohort: p = 0.0036) compared to low-risk patients.
Conclusion:
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.
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