Abstract
This study used receiver operating characteristic curve to analyze Surveillance, Epidemiology and End Results (SEER) adenosquamous carcinoma data to identify predictive models and potential disparities in outcome. This study analyzed socio-economic, staging and treatment factors available in the SEER database for adenosquamous carcinoma. For the risk modeling, each factor was fitted by a generalized linear model to predict the cause specific survival. An area under the receiver operating characteristic curve (ROC) was computed. Similar strata were combined to construct the most parsimonious models. A total of 20,712 patients diagnosed from 1973 to 2009 were included in this study. The mean follow up time (S.D.) was 54.2 (78.4) months. Some 2/3 of the patients were female. The mean (S.D.) age was 63 (13.8) years. SEER stage was the most predictive factor of outcome (ROC area of 0.71). 13.9% of the patients were un-staged and had risk of cause specific death of 61.3% that was higher than the 45.3% risk for the regional disease and lower than the 70.3% for metastatic disease. Sex, site, radiotherapy, and surgery had ROC areas of about 0.55-0.65. Rural residence and race contributed to socioeconomic disparity for treatment outcome. Radiotherapy was underused even with localized and regional stages when the intent was curative. This under use was most pronounced in older patients. Anatomic stage was predictive and useful in treatment selection. Under-staging may have contributed to poor outcome.
Highlights
SEER registry has massive amount of data available for analysis, manipulating this data pipeline could be challenging
This study explored a long list of socio-economic, staging and treatment factors that were available in the SEER database (Cheung, 2014a; 2014b; 2014c; 2014b; 2014e; Cheung, 2015a; 2015b; Cheung, 2015 (In press))
There were 60% adenosquamous carcinoma patients listed from SEER database were adults
Summary
SEER registry has massive amount of data available for analysis, manipulating this data pipeline could be challenging. This study explored a long list of socio-economic, staging and treatment factors that were available in the SEER database (Cheung, 2014a; 2014b; 2014c; 2014b; 2014e; Cheung, 2015a; 2015b; Cheung, 2015 (In press)). All statistics and programming were performed in Matlab (www.mathworks.com) (Cheung, 2014a; 2014b; 2014c; 2014b; 2014e; Cheung, 2015a; 2015b; Cheung, 2015 (In press)). Similar strata were fused to make more efficient models if the ROC performance did not degrade (Cheung et al, 2001a; Cheung et al, 2001b) It implemented binary fusion and optimization to streamline the risk stratification by combining risk strata when possible (Cheung, 2014a; 2014b; 2014c; 2014b; 2014e; Cheung, 2015a; 2015b; Cheung, 2015 (In press)). SCOPE provides SEER-adapted programs for user friendly exploratory studies, univariate recoding and parsing (Cheung, 2014a; 2014b; 2014c; 2014b; 2014e; Cheung, 2015a; 2015b; Cheung, 2015 (In press))
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