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


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-IB1, (2) no MRI finding suggesting PMI, 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%).


CONCLUSIONS
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.
Jung
Dae-Chul
DC
Research Institute and Hospital, National Cancer Center, Goyang, Republic of Korea.
Kim
Mi-Kyung
MK
Kang
Sokbom
S
Seo
Sang-Soo
SS
Cho
Jeong Yeon
JY
Park
Noh-Hyun
NH
Song
Yong-Sang
YS
Park
Sang-Yoon
SY
Kang
Soon-Beom
SB
Kim
Jae Weon
JW
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D005260:Female; D006801:Humans; D016015:Logistic Models; D008279:Magnetic Resonance Imaging; D008875:Middle Aged; D008954:Models, Biological; D015999:Multivariate Analysis; D009361:Neoplasm Invasiveness; D009367:Neoplasm Staging; D011237:Predictive Value of Tests; D012307:Risk Factors; D002583:Uterine Cervical Neoplasms / Q000473:pathology*