Abstract

IntroductionThe International Thymic Malignancy Interest Group (ITMIG) classifies thymoma recurrences on the basis of the topographic location, but its effectiveness in prognosis prediction has not been well investigated yet. Aims of this study are to analyze survival outcome of patients surgically treated for thymoma recurrence according to the ITMIG recurrence classification and to investigate possible alternatives. MethodsFrom January 1, 1990, to January 7, 2017, data on 135 surgically treated patients for thymoma recurrence from three high-volume centers were collected and retrospectively analyzed. Patients were classified according to the ITMIG classification as local, regional, and distant. The ITMIG classification and alternative classifications were correlated to overall survival (OS). ResultsAccording to the ITMIG classification, recurrence was local in 17 (12.5%), regional in 97 (71.8%), and distant in 21 (15.7%) patients, with single localization in 38 (28.2%) and multiple localizations in 97 (71.8%). The 5- and 10-year OS were 79.9% and 49.7% in local, 68.3% and 52.6% in regional, and 66.3% and 35.4% in distant recurrences, respectively, but differences were not statistically significant (p = 0.625). A significant difference in survival was present considering single versus multiple localizations: 5- and 10-year OS of 86.2% and 81.2% versus 61.3% and 31.5% (p = 0.005, hazard ratio = 7.22, 95% confidence interval: 0.147–0.740), respectively. Combining the localization number with the recurrence site, ITMIG locoregional single recurrence had a statistically significant better survival compared with patients with ITMIG locoregional multiple recurrence or ITMIG distant recurrence (p = 0.028). Similarly, a significant difference was present considering intrathoracic single versus intrathoracic multiple versus distant recurrence (p = 0.024). ConclusionsThe ITMIG classification for thymoma recurrence did not have significant survival differences comparing local, regional, and distant recurrences. Integrating this classification with the number of the localizations may improve its effectiveness in prognosis prediction.

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