ArticleTherapeutic advances in medical oncology2025
Development and validation of prognostic prediction models for early-stage cervical cancer patients based on pathological intermediate-risk factors.
Article in Therapeutic advances in medical oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Impact of adjuvant therapy after radical hysterectomy for early-stage cervical cancer on mortality and recurrence: a systematic review and meta-analysis.Frontiers in oncology · 2026Pooled it
- Machine learning-based prediction of long-term prognosis in cervical adenocarcinoma: a retrospective cohort study.BMC cancer · 2026Article
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9 authors.
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Abstract
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: 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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