Prognostic Model for Survival and Recurrence in Patients with Early-Stage Cervical Cancer: A Korean Gynecologic Oncology Group Study (KGOG 1028)
- Author:
E Sun PAIK
1
;
Myong Cheol LIM
;
Moon-Hong KIM
;
Yun Hwan KIM
;
Eun Seop SONG
;
Seok Ju SEONG
;
Dong Hoon SUH
;
Jong-Min LEE
;
Chulmin LEE
;
Chel Hun CHOI
Author Information
- Publication Type:Original Article
- From:Cancer Research and Treatment 2020;52(1):320-333
- CountryRepublic of Korea
- Language:0
-
Abstract:
Purpose:We aimed to develop and validate individual prognostic models in a large cohort of cervical cancer patients that were primarily treated with radical hysterectomy.
Materials and Methods:We analyzed 1,441 patients with early-stage cervical cancer treated between 2000 and 2008 from the Korean Gynecologic Oncology Group multi-institutional cohort: a train cohort (n=788) and a test cohort (n=653). Models predicting the risk for overall survival (OS), disease- free survival (DFS), lymphatic recurrence and hematogenous recurrence were developed using Cox analysis and stepwise backward selection and best-model options. The prognostic performance of each model was assessed in an independent patient cohort. Model-classified risk groups were compared to groups based on traditional risk factors.
Results:Independent risk factors for OS, DFS, lymphatic recurrence, and hematogenous recurrence were identified for prediction model development. Different combinations of risk factors were shown for each outcome with best predictive value. In train cohort, area under the curve (AUC) at 2 and 5 years were 0.842/0.836 for recurrence, and 0.939/0.882 for OS. When applied to a test cohort, the model also showed accurate prediction result (AUC at 2 and 5 years were 0.799/0.723 for recurrence, and 0.844/0.806 for OS, respectively). The Kaplan-Meier plot by proposed model-classified risk groups showed more distinctive survival differences between each risk group.
Conclusion:We developed prognostic models for OS, DFS, lymphatic and hematogenous recurrence in patients with early-stage cervical cancer. Combining weighted clinicopathologic factors, the proposed model can give more individualized predictions in clinical practice.
