Prognostic model for survival in patients with early stage cervical cancer

Background: In the management of early stage cervical cancer, knowledge about the prognosis is critical. Although many factors have an impact on survival, their relative importance remains controversial. This study aims to develop a prognostic model for survival in early stage cervical cancer patients and to reconsider grounds for adjuvant treatment.

Methods: A multivariate Cox regression model was used to identify the prognostic weight of clinical and histological factors for disease-specific survival (DSS) in 710 consecutive patients who had surgery for early stage cervical cancer (FIGO [International Federation of Gynecology and Obstetrics] stage IA2-IIA). Prognostic scores were derived by converting the regression coefficients for each prognostic marker and used in a score chart. The discriminative capacity was expressed as the area under the curve (AUC) of the receiver operating characteristic.

Results: The 5-year DSS was 92%. Tumor diameter, histological type, lymph node metastasis, depth of stromal invasion, lymph vascular space invasion, and parametrial extension were independently associated with DSS and were included in a Cox regression model. This prognostic model, corrected for the 9% overfit shown by internal validation, showed a fair discriminative capacity (AUC, 0.73). The derived score chart predicting 5-year DSS showed a good discriminative capacity (AUC, 0.85).

Conclusions: In patients with early stage cervical cancer, DSS can be predicted with a statistical model. Models, such as that presented here, should be used in clinical trials on the effects of adjuvant treatments in high-risk early cervical cancer patients, both to stratify and to include patients.