Prognostic scoring systems in patients with follicular thyroid cancer: a comparison of different staging systems in predicting the patient outcome

Abstract

Background: The use of prognostic scoring systems is important for predicting the survival of individuals with thyroid carcinoma. Relatively few studies have addressed this issue for patients with follicular thyroid cancer. The goal of this retrospective study was to establish the best and most pertinent prognostic scoring system to predict survival in patients with follicular thyroid cancer.

Methods: We selected 86 patients with follicular thyroid cancer treated at University of California, San Francisco (UCSF) hospitals from January 1954 to April 1998. The mean follow-up time was 11.5 years. There were 60 women (70%) and 26 men (30%), with a mean age if 48.6 years. Prognostic scoring systems included tumor, node, metastases (TNM), European Organization for Research and Treatment of Cancer (EORTC), Age, Grade, Extent, Size (AGES), Age, Metastases, Extent, Size (AMES), and the Metastases, Age, Completeness of resection, Invasion, Size (MACIS). Survival time was calculated using the Kaplan-Meier method. Using Cox proportional hazards analysis, the relative importance of each scoring method was determined by calculating the proportion of variation in survival time explained (PVE).

Results: Kaplan-Meier analysis indicated that all scoring systems were significant predictors of survival time (p < 0.0001). The PVE associated with each system was (from highest to lowest) 0.48 for MACIS, 0.46 for AGES, 0.44 for EORTC, 0.40 for AMES, and 0.33 for TNM. These results indicate that the MACIS scoring system accounted for a great proportion of explained variance in survival and is a more precise predictor of survival compared to the other scoring systems.

Conclusions: TNM, EORTC, AGES, AMES, and MACIS, all provided useful prognostic information about the survival in our 86 patients with follicular thyroid cancers. The MACIS classification, however, was the most accurate predictor using PVE as a method of evaluation. Future scoring systems considering additional prognostic factors, may obtain a higher PVE.