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Postoperative Nomogram Predicting Risk of Recurrence After Radical Hysterectomy for Early-Stage Cervical Cancer

Cited 38 time in Web of Science Cited 40 time in Scopus
Authors

Kim, Mi-Kyung; Jo, Hoenil; Kong, Hyoun-Joong; Kim, Hee Chan; Kim, Yong-Man; Kang, Soon-Beom; Lee, Hyo Pyo; Mok, Jung-Eun; Song, Yong-Sang; Kim, Jae Weon

Issue Date
2010-12
Publisher
LIPPINCOTT WILLIAMS & WILKINS
Citation
INTERNATIONAL JOURNAL OF GYNECOLOGICAL CANCER; Vol.20 9; 1581-1586
Keywords
Cervical cancerRadical hysterectomyPrognosisNomogram
Abstract
Objective: The aim of this study was to develop a nomogram for predicting the 5-year disease-free survival (DFS) after radical hysterectomy for early-stage cervical cancer. Patients and Methods: An institutional database of 275 consecutive patients treated at Seoul National University Hospital for stage I to stage IIA cervical cancer was used to develop a nomogram based on Cox proportional hazards regression model. The developed nomogram was internally validated with bootstrapping, and performance was assessed by concordance index and a calibration curve. External validation was also performed using an independent data set of patients from Asan Medical Center. Results: From Cox regression analysis, disease stage, number of positive lymph nodes, parametrial involvement, and depth of invasion were identified as independent risk factors for disease recurrence (P < 0.05). The nomogram incorporating these factors appeared to be accurate and predicted the outcomes better than the International Federation of Gynecology and Obstetrics stage alone (concordance index, 0.858 compared with 0.719; P = 0.001). When applied to a separate validation set, the nomogram also showed similar predictive accuracy (concordance index, 0.879). Conclusion: We have developed a nomogram that can predict the recurrence risk in patients with early-stage cervical cancer after surgery, which was internally and externally validated.
ISSN
1048-891X
Language
English
URI
https://hdl.handle.net/10371/76794
DOI
https://doi.org/10.1111/IGC.0b013e3181f7b353
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