DOI QR코드

DOI QR Code

Analysis of SEER Glassy Cell Carcinoma Data: Underuse of Radiotherapy and Predicators of Cause Specific Survival

  • Cheung, Rex
  • Published : 2016.02.05

Abstract

Background: This study used receiver operating characteristic curve to analyze Surveillance, Epidemiology and End Results (SEER) for glassy cell carcinoma data to identify predictive models and potential disparities in outcome. Materials and Methods: This study analyzed socio-economic, staging and treatment factors. For risk modeling, each factor was fitted by a generalized linear model to predict the cause specific survival. Area under the receiver operating characteristic curves (ROCs) were computed. Similar strata were combined to construct the most parsimonious models. A random sampling algorithm was used to estimate modeling errors. Risk of glassy cell carcinoma death was computed for the predictors for comparison. Results: There were 79 patients included in this study. The mean follow up time (S.D.) was 37 (32.8) months. Female patients outnumbered males 4:1. The mean (S.D.) age was 54.4 (19.8) years. SEER stage was the most predictive factor of outcome (ROC area of 0.69). The risks of cause specific death were, respectively, 9.4% for localized, 16.7% for regional, 35% for the un-staged/others category, and 60% for distant disease. After optimization, separation between the regional and unstaged/others category was removed with a higher ROC area of 0.72. Several socio-economic factors had small but measurable effects on outcome. Radiotherapy had not been used in 90% of patients with regional disease. Conclusions: Optimized SEER stage was predictive and useful in treatment selection. Underuse of radiotherapy may have contributed to poor outcome.

Keywords

Glassy cell carcinoma;radiotherapy;SEER registry;under usage;cause specific survival

References

  1. Bhatia S (2011). Disparities in cancer outcomes: lessons learned from children with cancer. Pediatr Blood Cancer, 56, 994-1002. https://doi.org/10.1002/pbc.23078
  2. Cheung MC, Zhuge Y, Yang R, et al (2010). Incidence and outcomes of extremity soft-tissue sarcomas in children. J Surg Res, 163, 282-9. https://doi.org/10.1016/j.jss.2010.04.033
  3. Cheung MR (2014a). Optimization of predictors of Ewing sarcoma cause-specific survival: a population study. Asian Pac J Cancer Prev, 15, 4143-5. https://doi.org/10.7314/APJCP.2014.15.10.4143
  4. Cheung MR (2014b). Surveying and optimizing the predictors for ependymoma specific survival using SEER data. Asian Pac J Cancer Prev, 15, 867-70. https://doi.org/10.7314/APJCP.2014.15.2.867
  5. Cheung MR (2014c). Under-use of radiotherapy in stage III bronchioaveolar lung cancer and socio-economic disparities in cause specific survival: a population study. Asian Pac J Cancer Prev, 15, 4091-4. https://doi.org/10.7314/APJCP.2014.15.9.4091
  6. Cheung R (2014d). Epidemiology and radiotherapy of hepatocellular carcinoma. Int J Cancer Clin Res, 1, 1.
  7. Cheung R (2015a). Smoking, air pollution and cancer: global epidemiology, public health and genomics. Ann Transl Med Epidemiol, 2.
  8. Cheung R 2015b. Topics on radiotherapy, global cancer epidemiology and public health. Lambert Academic Publishing.
  9. Cheung R 2015 (In press). Determining best contours in radiotherapy treatment in a modern era, and asian american medical epidemiology: Public Health Point of View.
  10. Cheung R, Altschuler MD, D'Amico AV, et al (2001a). ROCoptimization may improve risk stratification of prostate cancer patients. Urology, 57, 286-90. https://doi.org/10.1016/S0090-4295(00)00911-0
  11. Cheung R, Altschuler MD, D'Amico AV, et al (2001b). 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
  12. 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
  13. Garg MM, Arora VK (2012). Clear cell adenosquamous carcinoma of the cervix: a case report with discussion of the differential diagnosis. Int J Gynecol Pathol, 31, 294-6. https://doi.org/10.1097/PGP.0b013e31823b6f37
  14. Hanley JA, McNeil BJ (1982). The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 143, 29-36. https://doi.org/10.1148/radiology.143.1.7063747
  15. McDowell HP, Foot AB, Ellershaw C, et al (2010). Outcomes in paediatric metastatic rhabdomyosarcoma: results of The International Society of Paediatric Oncology (SIOP) study MMT-98. Eur J Cancer, 46, 1588-95. https://doi.org/10.1016/j.ejca.2010.02.051
  16. Ognjanovic S, Linabery AM, Charbonneau B, et al (2009). Trends in childhood rhabdomyosarcoma incidence and survival in the United States, 1975-2005. Cancer, 115, 4218-26. https://doi.org/10.1002/cncr.24465
  17. Pappo AS, Krailo M, Chen Z, et al (2010). Infrequent tumor initiative of the Children's Oncology Group: initial lessons learned and their impact on future plans. J Clin Oncol, 28, 5011-6. https://doi.org/10.1200/JCO.2010.31.2603
  18. Perez EA, Kassira N, Cheung MC, et al (2011). Rhabdomyosarcoma in children: a SEER population based study. J Surg Res, 170, 243-51. https://doi.org/10.1016/j.jss.2011.03.001
  19. Sultan I, Qaddoumi I, Yaser S, et al (2009). Comparing adult and pediatric rhabdomyosarcoma in the surveillance, epidemiology and end results program, 1973 to 2005: an analysis of 2,600 patients. J Clin Oncol, 27, 3391-7. https://doi.org/10.1200/JCO.2008.19.7483
  20. Takahashi Y, Sasaki H, Mogami H, et al (2011). Adjuvant combined paclitaxel and carboplatin chemotherapy for glassy cell carcinoma of the uterine cervix: report of three cases with clinicopathological analysis. J Obstet Gynaecol Res, 37, 1860-3. https://doi.org/10.1111/j.1447-0756.2011.01643.x
  21. Zhu HT, Li SX (2011). Glassy cell carcinoma of cervix: a clinicopathologic analysis of 5 cases. Zhonghua Bing Li Xue Za Zhi, 40, 523-7 (in Chinese).