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Identification of a patient group at low risk for parametrial invasion in early-stage cervical cancer

Cited 19 time in Web of Science Cited 22 time in Scopus
Authors
Jung, Dae-Chul; Kim, Mi-Kyung; Kang, Sokbom; Seo, Sang-Soo; Park, Noh-Hyun; Park, Sang-Yoon; Kim, Jae Weon; Kang, Soon-Beom; Song, Yong-Sang; Cho, Jeong Yeon
Issue Date
2010-12
Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
Citation
GYNECOLOGIC ONCOLOGY; Vol.119 3; 426-430
Keywords
Cervical cancerSurgeryPrognostic factorParametrial invasionParametriumMagnetic resonance imaging
Abstract
Aim. Using parameters obtained from magnetic resonance imaging (MRI), we constructed a prediction model for parametrial invasion (PMI) of cervical cancer and validated the model in different sets of patients. Patients and methods. Retrospectively, 251 patients with cervical cancer stages IA2-IIA, who had received a radical hysterectomy, were assigned to training and validation cohorts. After the development of the scoring index using logistic coefficient analysis, the performance of the prediction model was assessed using independent validation sets. Results. In the training cohort (n=167), multivariate analysis indicated that the patient`s stage, the cephalocaudal tumor diameter measured by MRI, and finding of PMI as obtained by MRI were independent predictors of PMI (P=0.010, <0.001, and 0.020, respectively). These predictors were internally validated by a rigorous bootstrapping method with statistical significance. The scoring index was created based on logistic coefficients, and the maximal score yielding a negative likelihood ratio less than 0.05 was selected as a cutoff. The cutoff was translated into the following criteria identifying a very low-risk group for PMI: (1) FIGO stage IA2-IBI, (2) no MRI finding suggesting PM!, and (3) cephalocaudal tumor diameter less than 1.0 cm by MRI. The negative predictive value (NPV) was 98.5% (95% confidence interval [CI]=91.7% to 100%). In the external validation cohort (n=84), the NPV was 100% (95% CI=90% to 100%). Conclusion. The current prediction model showed reliable performance for the identification of patients at low risk for PMI. It may be useful for stratification of patients and evaluation of results in future trials. (C) 2010 Elsevier Inc. All rights reserved.
ISSN
0090-8258
Language
English
URI
http://hdl.handle.net/10371/76797
DOI
https://doi.org/10.1016/j.ygyno.2010.08.005
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College of Medicine/School of Medicine (의과대학/대학원)Obstetrics & Gynecology (산부인과전공)Journal Papers (저널논문_산부인과학전공)
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