Epidemiology, Incidence, and Survival of Rhabdomyosarcoma Subtypes: SEER and ICES Database Analysis


Amer K. M. , Thomson J. E. , Congiusta D., Dobitsch A., Chaudhry A., Li M., ...Daha Fazla

JOURNAL OF ORTHOPAEDIC RESEARCH, cilt.37, ss.2226-2230, 2019 (SCI İndekslerine Giren Dergi) identifier identifier identifier

  • Cilt numarası: 37 Konu: 10
  • Basım Tarihi: 2019
  • Doi Numarası: 10.1002/jor.24387
  • Dergi Adı: JOURNAL OF ORTHOPAEDIC RESEARCH
  • Sayfa Sayıları: ss.2226-2230

Özet

Rhabdomyosarcoma is the most common soft-tissue sarcoma in children and adolescents and accounts for 3% of all pediatric tumors. Subtypes include alveolar, spindle cell, embryonal, mixed-type, pleomorphic, and rhabdomyosarcoma with ganglionic differentiation. The National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) database was queried for patients diagnosed with any type of rhabdomyosarcoma between 1973 and 2014. Patient demographics, tumor characteristics, and incidence were studied with chi(2) analysis. Survival was modeled with Kaplan-Meier survival curves and Cox proportional hazards models were used to assess the effect of age and gender on survival. Pleomorphic subtype had higher grade and larger sized tumors compared to other subtypes (p < 0.05). Pleomorphic and alveolar rhabdomyosarcoma had the worst overall survival with a 26.6% and 28.9% 5-year survival, respectively. Embryonal rhabdomyosarcoma had the highest 5-year survival rate (73.9%). Tumor size was negatively correlated with survival months, indicating patients with larger tumors had shorter survival times (p < 0.05). Presence of higher-grade tumors and metastatic disease at presentation were negatively correlated with survival months (p < 0.05). No significant differences in the survival were found between gender or race between all of the subtypes (p > 0.05). This study highlights key differences in the demographic and survival rates of the different types of rhabdomyosarcoma that can be used for more tailored patient counseling. We also demonstrate that large, population-level databases provide sufficient data that can be used in the analysis of rare tumors.