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Jpn. J. Clin. Oncol. · Nov 2007
Tumor volume and uterine body invasion assessed by MRI for prediction of outcome in cervical carcinoma treated with concurrent chemotherapy and radiotherapy.
- Hunjung Kim, Woochul Kim, Mijo Lee, Eunseop Song, and John J K Loh.
- Department of Radiation Oncology, Dongguk University Gyongju Hospital, 1090-1 Sukjang-Dong, Gyongju-Si, Kyong-Buk 780-350, Korea. thinkonco@paran.com.
- Jpn. J. Clin. Oncol. 2007 Nov 1; 37 (11): 858-66.
ObjectiveThe aim of this study was to evaluate the prognostic significance of primary tumor volume and uterine body invasion assessed by pre-treatment MRI for uterine cervical cancer patient treated with concurrent chemotherapy and radiotherapy.MethodsA retrospective analysis of 106 patients with IB-IIIB cervical carcinoma was performed. Potential prognostic factors were stage, clinical tumor diameter, histology, age, pelvic lymph node, vaginal extension, parametrial invasion, tumor volume and uterine body invasion status. Multivariate analyses were performed to identify the prognostic factor for overall survival (OS) and disease-free survival (DFS).ResultsThe 5-year OS, DFS rate were 59.7 and 56.6%. Using multivariate analyses, a large tumor volume (>/=30 ml; P = 0.012) and uterine body invasion (P = 0.020) and positive pelvic lymph node (LN) enlargement (P = 0.040) showed a significantly unfavorable influence on OS. Using these three factors, patients were divided into four subgroups: the OS rates of patients with risk 0 (volume <30 ml, no uterine body invasion, and negative LN), risk 1 (one of these three factors), risk 2 (two of these three factors) and risk 3 (volume >/=30 ml, uterine body invasion, and positive LN) were 96.3, 77.5, 53.0 and 14.8%, respectively (P < 0.0001).ConclusionsTumor volume and uterine body invasion determined by MRI were significant prognostic factors for patients with cervical carcinoma. Pelvic lymph node enlargement diagnosed by CT also proved to be a significant prognostic factor in OS. Using these three parameters, we devised a practical and effective model to predict OS.
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