Development and validation of prognostic prediction models for early-stage cervical cancer patients based on pathological intermediate-risk factors.

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:  < 0.0001; validation cohort:  = 0.0011) and OS (development cohort:  < 0.0001; validation cohort:  = 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.