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1148 Predicting the risk of distant recurrence of cervical cancer after concurrent chemoradiation: A validation study of KGOG 1024
  1. W Shin1,
  2. J Kim2 and
  3. S Kang2
  1. 1Chungnam National University Hospital, obstetrics and gynecology, Korea, Rep. of South
  2. 2National Cancer Center, Goyang-si, Korea, Rep. of South


Introduction/Background*This study was aimed to validate the performance of the KGOG-1024 risk model predicting the risk of distant failure after chemoradiation in patients with locally advanced cervical cancer.

Methodology In the retrospective cohort of 297 patients who received concurrent chemoradiation for advanced cervical cancer, an individual risk was calculated using the KGOG 1024 risk model for each patients. The cohort was categorized into three risk groups (low, intermediate, and high-risk groups) according to a calculated risk. The mean of the calculated and the observed risk were compared within each group.

Result(s)*Low, intermediate, and high-risk groups were classified into 79, 143, and 68 patients. (27.2%, 49.3%, and 23.5%) Risk of recurrence prediction was quite similar between calculated and observed. (26.7% calculated and 25.6% observed) In low risk group, FIGO stage (p=0.004) and old age (p=0.04) was related of the risk of recurrence using univariate analysis.

Abstract 1148 Table 1

N = 297

Abstract 1148 Table 2

Competing regression analysis of distant-recurrence free survival in locally advanced cervical cancer according to the risk groups identified by the KGOG-1024 risk criteria.

Abstract 1148 Table 3

Calculated and observed 5-year probability of of distant recurrence according to risk groups identified by the KGOG-1024 risk criteria.

Abstract 1148 Table 4

Additional risk variables associated with distant recurrence-free survival within low-risk group identified by the KGOG-1024 criteria (univariate analysis).

Conclusion*The KGOG-1024 risk assessment model accurately predicted a distant recurrence after chemoradiation in patients with locally advanced cervical cancer, especially in intermediate-risk group. The model may be helpful in identifying patients who may benefit from adjuvant systemic treatment after chemoradiation.

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