OBJECTIVE: To evaluate factors influencing survival and compare current classification systems in women treated for malignant gestational trophoblastic tumor. METHODS: A consecutive series of 454 women treated between 1968-1992 was reviewed retrospectively to identify potential clinical prognostic factors using univariate analysis of life tables. All patients were evaluated using clinical classification, World Health Organization, and recently modified International Federation of Gynecology and Obstetrics (FIGO) staging systems, applied retrospectively. Multivariate Cox regression analysis was used to model potential independent prognostic factors within subsets of the patient population. RESULTS: Factors identified by univariate analysis as potential prognostic influences included age, duration of disease, type of antecedent pregnancy, clinicopathologic diagnosis, site of metastases, number of metastatic sites and foci, tumor size, and prior therapy. The pre-therapy hCG level was not significantly associated with survival (P < .04). Multivariate Cox modeling consistently identified prior therapy, type of antecedent pregnancy, number of metastatic sites, and duration of disease as independent prognostic factors. Clinicopathologic diagnosis and hCG level were of borderline significance only in some models of the total patient population. All classification systems were able to identify low- and high-risk subsets of patients with approximately equal efficiency. The addition of FIGO substages enhanced discrimination between prognostic groups in patients with stage III disease. CONCLUSIONS: Existing systems for the classification of malignant gestational trophoblastic tumor are based in part on factors that are not independently prognostic, such as hCG level or tumor size. These systems discriminate between low- and high-risk patients with approximately equal efficiency. The clinical classification system is currently preferred for determining initial therapy in women with malignant gestational trophoblastic tumors.
OBJECTIVE: To evaluate factors influencing survival and compare current classification systems in women treated for malignant gestational trophoblastic tumor. METHODS: A consecutive series of 454 women treated between 1968-1992 was reviewed retrospectively to identify potential clinical prognostic factors using univariate analysis of life tables. All patients were evaluated using clinical classification, World Health Organization, and recently modified International Federation of Gynecology and Obstetrics (FIGO) staging systems, applied retrospectively. Multivariate Cox regression analysis was used to model potential independent prognostic factors within subsets of the patient population. RESULTS: Factors identified by univariate analysis as potential prognostic influences included age, duration of disease, type of antecedent pregnancy, clinicopathologic diagnosis, site of metastases, number of metastatic sites and foci, tumor size, and prior therapy. The pre-therapy hCG level was not significantly associated with survival (P < .04). Multivariate Cox modeling consistently identified prior therapy, type of antecedent pregnancy, number of metastatic sites, and duration of disease as independent prognostic factors. Clinicopathologic diagnosis and hCG level were of borderline significance only in some models of the total patient population. All classification systems were able to identify low- and high-risk subsets of patients with approximately equal efficiency. The addition of FIGO substages enhanced discrimination between prognostic groups in patients with stage III disease. CONCLUSIONS: Existing systems for the classification of malignant gestational trophoblastic tumor are based in part on factors that are not independently prognostic, such as hCG level or tumor size. These systems discriminate between low- and high-risk patients with approximately equal efficiency. The clinical classification system is currently preferred for determining initial therapy in women with malignant gestational trophoblastic tumors.
Authors: Y K Eysbouts; P B Ottevanger; L F A G Massuger; J IntHout; D Short; R Harvey; B Kaur; N J Sebire; N Sarwar; F C G J Sweep; M J Seckl Journal: Ann Oncol Date: 2017-08-01 Impact factor: 32.976