Asian Pacific Journal of Cancer Prevention

Asian Pacific Journal of Cancer Prevention (아시아태평양암예방학회)
권호 표지
DOI QR코드

DOI QR Code

Optimization of Predictors of Ewing Sarcoma Cause-specific Survival: A Population Study

  • Published : 2014.05.30
Download PDF
⟨ Previous Next ⟩

Abstract

Background: This study used receiver operating characteristic curve to analyze Surveillance, Epidemiology and End Results (SEER) Ewing sarcoma (ES) outcome data. The aim of this study was to identify and optimize ES-specific survival prediction models and sources of survival disparities. Materials and Methods: This study analyzed socio-economic, staging and treatment factors available in the SEER database for ES. 1844 patients diagnosed between 1973-2009 were used for this study. For the risk modeling, each factor was fitted by a Generalized Linear Model to predict the outcome (bone and joint specific death, yes/no). The area under the receiver operating characteristic curve (ROC) was computed. Similar strata were combined to construct the most parsimonious models. Results: The mean follow up time (S.D.) was 74.48 (89.66) months. 36% of the patients were female. The mean (S.D.) age was 18.7 (12) years. The SEER staging has the highest ROC (S.D.) area of 0.616 (0.032) among the factors tested. We simplified the 4-layered risk levels (local, regional, distant, un-staged) to a simpler non-metastatic (I and II) versus metastatic (III) versus un-staged model. The ROC area (S.D.) of the 3-tiered model was 0.612 (0.008). Several other biologic factors were also predictive of ES-specific survival, but not the socio-economic factors tested here. Conclusions: ROC analysis measured and optimized the performance of ES survival prediction models. Optimized models will provide a more efficient way to stratify patients for clinical trials.

Keywords

References

  1. Applebaum MA, Goldsby R, Neuhaus J, Dubois SG (2012). Clinical features and outcomes in patients with Ewing sarcoma and regional lymph node involvement. Pediatr Blood Cancer, 59, 617-20. https://doi.org/10.1002/pbc.24053
  2. Cheung R (2012). Poor treatment outcome of neuroblastoma and other peripheral nerve cell tumors may be related to under usage of radiotherapy and socio-economic disparity: a US SEER data analysis. Asian Pac J Cancer Prev, 13, 4587-91. https://doi.org/10.7314/APJCP.2012.13.9.4587
  3. Cheung R, Altschuler MD, D'Amico AV, et al (2001). ROCoptimization may improve risk stratification of prostate cancer patients. Urology, 57, 286-90. https://doi.org/10.1016/S0090-4295(00)00911-0
  4. Cheung R, Altschuler MD, D'Amico AV, et al (2001). Using the receiver operator characteristic curve to select pretreatment and pathologic predictors for early and late post-prostatectomy PSA failure. Urology, 58, 400-5. https://doi.org/10.1016/S0090-4295(01)01209-2
  5. D'Amico AV, Desjardin A, Chung A, et al (1998). Assessment of outcome prediction models for patients with localized prostate carcinoma managed with radical prostatectomy or external beam radiation therapy. Cancer, 82, 1887-96. https://doi.org/10.1002/(SICI)1097-0142(19980515)82:10<1887::AID-CNCR11>3.0.CO;2-P
  6. Friedman DL, Whitton J, Leisenring W, et al (2010). Subsequent neoplasms in 5-year survivors of childhood cancer: the Childhood Cancer Survivor Study. J Natl Cancer Inst, 102, 1083-95. https://doi.org/10.1093/jnci/djq238
  7. Hanley JA, McNeil BJ (1982). The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiol, 143, 29-36. https://doi.org/10.1148/radiology.143.1.7063747
  8. Herzog CE (2005). Overview of sarcomas in the adolescent and young adult population. J Pediatr Hematol Oncol, 27, 215-8. https://doi.org/10.1097/01.mph.0000161762.53175.e4
  9. Jawad MU, Haleem AA, Scully SP (2011). Malignant sarcoma of the pelvic bones: treatment outcomes and prognostic factors vary by histopathology. Cancer, 117, 1529-41. https://doi.org/10.1002/cncr.25684
  10. Mukherjee D, Chaichana KL, Adogwa O, et al (2011). Association of extent of local tumor invasion and survival in patients with malignant primary osseous spinal neoplasms from the surveillance, epidemiology, and end results (SEER) database. World Neurosurg, 76, 580-5. https://doi.org/10.1016/j.wneu.2011.05.016
  11. Mukherjee D, Chaichana KL, Gokaslan ZL, et al (2011). Survival of patients with malignant primary osseous spinal neoplasms: results from the Surveillance, Epidemiology, and End Results (SEER) database from 1973 to 2003. J Neurosurg Spine, 14, 143-50. https://doi.org/10.3171/2010.10.SPINE10189
  12. Schrager J, Patzer RE, Mink PJ, Ward KC, Goodman M (2011). Survival outcomes of pediatric osteosarcoma and Ewing's sarcoma: a comparison of surgery type within the SEER database, 1988-2007. J Registry Manag, 38, 153-61.
  13. Sultan I, Rihani R, Hazin R, Rodriguez-Galindo C (2010). Second malignancies in patients with Ewing Sarcoma Family of Tumors: A population-based study. Acta Oncol, 49, 237-44. https://doi.org/10.3109/02841860903253538
  14. Worch J, Cyrus J, Goldsby R, et al (2011). Racial differences in the incidence of mesenchymal tumors associated with EWSR1 translocation. Cancer Epidemiol Biomarkers Prev, 20, 449-53. https://doi.org/10.1158/1055-9965.EPI-10-1170
  15. Worch J, Matthay KK, Neuhaus J, Goldsby R, DuBois SG (2010). Ethnic and racial differences in patients with Ewing sarcoma. Cancer, 116, 983-8. https://doi.org/10.1002/cncr.24865

Cited by

  1. Wnt/β-catenin pathway in bone cancers vol.35, pp.10, 2014, https://doi.org/10.1007/s13277-014-2433-8
  2. Optimization of Decision Support Tool using Medication Regimens to Assess Rehospitalization Risks vol.5, pp.3, 2014, https://doi.org/10.4338/ACI-2014-04-RA-0040
  3. Analysis of SEER Adenosquamous Carcinoma Data to Identify Cause Specific Survival Predictors and Socioeconomic Disparities vol.17, pp.1, 2016, https://doi.org/10.7314/APJCP.2016.17.1.347
  4. Analysis of SEER Glassy Cell Carcinoma Data: Underuse of Radiotherapy and Predicators of Cause Specific Survival vol.17, pp.1, 2016, https://doi.org/10.7314/APJCP.2016.17.1.353
  5. Resveratrol in management of bone and spinal cancers pp.1478-6427, 2017, https://doi.org/10.1080/14786419.2017.1389936