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Rex Cheung - One of the best experts on this subject based on the ideXlab platform.
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Analysis of SEER Adenosquamous Carcinoma Data to Identify Cause Specific Survival Predictors and Socioeconomic Disparities.
Asian Pacific journal of cancer prevention : APJCP, 2016Co-Authors: Rex CheungAbstract:Background: 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. Materials and Methods: 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. Results: 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. Conclusions: Anatomic stage was predictive and useful in treatment selection. Under-staging may have contributed to poor outcome.
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Analysis of SEER Glassy Cell Carcinoma Data: Underuse of Radiotherapy and Predicators of Cause Specific Survival.
Asian Pacific journal of cancer prevention : APJCP, 2016Co-Authors: Rex CheungAbstract: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.
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Racial and Socioeconomic Disparities in Malignant Carcinoid Cancer Cause Specific Survival: Analysis of the Surveillance, Epidemiology and End Results National Cancer Registry
Asian Pacific journal of cancer prevention : APJCP, 2013Co-Authors: Rex CheungAbstract:Background: This study hypothesized living in a poor neighborhood decreased the Cause Specific Survival in individuals suffering from carcinoid carcinomas. Surveillance, Epidemiology and End Results (SEER) carcinoid carcinoma data were used to identify potential socioeconomic disparities in outcome. Materials and Methods: This study analyzed socioeconomic, staging and treatment factors available in the SEER database for carcinoid carcinomas. The Kaplan-Meier method was used to analyze time to events and the Kolmogorov-Smirnov test to compare Survival curves. The Cox proportional hazard method was employed for multivariate analysis. Areas under the receiver operating characteristic curves (ROCs) were computed to screen the predictors for further analysis. Results: There were 38,546 patients diagnosed from 1973 to 2009 included in this study. The mean follow up time (S.D.) was 68.1 (70.7) months. SEER stage was the most predictive factor of outcome (ROC area of 0.79). 16.4% of patients were un-staged. Race/ethnicity, rural urban residence and county level family income were significant predictors of Cause Specific Survival on multivariate analysis, these accounting for about 5% of the difference in actuarial Cause Specific Survival at 20 years of follow up. Conclusions: This study found poorer Cause Specific Survival of carcinoid carcinomas of individuals living in poor and rural neighborhoods.
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Impact of socioeconomic disparities on Cause-Specific Survival of retinoblastoma.
Molecular and clinical oncology, 2013Co-Authors: Rex CheungAbstract:Retinoblastoma (RB) is a rare disease of infancy and early childhood. This study investigated the effects of socioeconomic factors on the Cause-Specific Survival of RB. Data from patients diagnosed with RB between 1973 and 2009 were obtained from the Surveillance, Epidemiology and End Results (SEER) database. The study included 1,456 patients with a the mean follow-up time (SD) of 128.75 (113.74) months and a mean age (SD) of 1.4 (2.6) years. This study analyzed socioeconomic, staging and treatment factors available in the SEER database for RB. Kaplan-Meier analysis was used to analyze time-to-failure data. The two-sample Kolmogorov-Smirnov test was used for univariate analysis and the Cox proportional hazards model was used for multivariate analysis. The area under the receiver operating characteristic (ROC) curve was computed for predictors. SEER stage was the most significant predictive pretreatment factor. The identified socioeconomic barriers included ethnicity and rural-urban residence status that led to a 3% decrease in RB Cause-Specific Survival. Thus, eliminating barriers to treatment is crucial for reducing the outcome disparities.
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Racial and social economic factors impact on the Cause Specific Survival of pancreatic cancer: a SEER survey.
Asian Pacific journal of cancer prevention : APJCP, 2013Co-Authors: Rex CheungAbstract:Background: This study used Surveillance, Epidemiology and End Results (SEER) pancreatic cancer data to identify predictive models and potential socio-economic disparities in pancreatic cancer outcome. Materials and Methods: For risk modeling, Kaplan Meier method was used for Cause Specific Survival analysis. The Kolmogorov-Smirnov’s test was used to compare Survival curves. The Cox proportional hazard method was applied for multivariate analysis. The area under the ROC curve was computed for predictors of absolute risk of death, optimized to improve efficiency. Results: This study included 58,747 patients. The mean follow up time (S.D.) was 7.6 (10.6) months. SEER stage and grade were strongly predictive univariates. Sex, race, and three socio-economic factors (county level family income, rural-urban residence status, and county level education attainment) were independent multivariate predictors. Racial and socio-economic factors were associated with about 2% difference in absolute Cause Specific Survival. Conclusions: This study s found significant effects of socio-economic factors on pancreas cancer outcome. These data may generate hypotheses for trials to eliminate these outcome disparities.
Min Rex Cheung - One of the best experts on this subject based on the ideXlab platform.
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Optimization of Predictors of Ewing Sarcoma Cause-Specific Survival: A Population Study
Asian Pacific journal of cancer prevention : APJCP, 2014Co-Authors: Min Rex CheungAbstract: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.
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Under-use of radiotherapy in stage III bronchioaveolar lung cancer and socio-economic disparities in Cause Specific Survival: a population study.
Asian Pacific journal of cancer prevention : APJCP, 2014Co-Authors: Min Rex CheungAbstract:BACKGROUND This study used the receiver operating characteristic curve (ROC) to analyze Surveillance, Epidemiology and End RESULTS (SEER) bronchioaveolar carcinoma data to identify predictive models and potential disparity in outcomes. MATERIALS AND METHODS Socio-economic, staging and treatment factors were assessed. For the risk modeling, each factor was fitted by a Generalized Linear Model to predict Cause Specific Survival. The area under the ROC was computed. Similar strata were combined to construct the most parsimonious models. A random sampling algorithm was used to estimate modeling errors. Risk of Cause Specific death was computed for the predictors for comparison. RESULTS There were 7,309 patients included in this study. The mean follow up time (S.D.) was 24.2 (20) months. Female patients outnumbered male ones 3:2. The mean (S.D.) age was 70.1 (10.6) years. Stage was the most predictive factor of outcome (ROC area of 0.76). After optimization, several strata were fused, with a comparable ROC area of 0.75. There was a 4% additional risk of death associated with lower county family income, African American race, rural residency and lower than 25% county college graduate. Radiotherapy had not been used in 2/3 of patients with stage III disease. CONCLUSIONS There are socio-economic disparities in Cause Specific Survival. Under-use of radiotherapy may have contributed to poor outcome. Improving education, access and rates of radiotherapy use may improve outcome.
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Socio-economic factors affect the outcome of soft tissue sarcoma: an analysis of SEER data.
Asian Pacific journal of cancer prevention : APJCP, 2014Co-Authors: Min Rex Cheung, Josephine Kang, Daniel Ouyang, Vincent YeungAbstract:Background: This study analyzed whether socio-economic factors affect the Cause Specific Survival of soft tissue sarcoma (STS). Methods: Surveillance, Epidemiology and End Results (SEER) soft tissue sarcoma (STS) data were used to identify potential socio-economic disparities in outcome. Time to Cause Specific death was computed with Kaplan-Meier analysis. Kolmogorov-Smirnov tests and Cox proportional hazard analysis were used for univariate and multivariate tests, respectively. The areas under the receiver operating curve were computed for predictors for comparison. Results: There were 42,016 patients diagnosed STS from 1973 to 2009. The mean follow up time (S.D.) was 66.6 (81.3) months. Stage, site, grade were significant predictors by univariate tests. Race and rural-urban residence were also important predictors of outcome. These five factors were all statistically significant with Cox analysis. Rural and African-American patients had a 3-4% disadvantage in Cause Specific Survival. Conclusions: Socio-economic factors influence Cause Specific Survival of soft tissue sarcoma. Ensuring access to cancer care may eliminate the outcome disparities.
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Using SEER Data to Quantify Effects of Low Income Neighborhoods on Cause Specific Survival of Skin Melanoma
Asian Pacific journal of cancer prevention : APJCP, 2013Co-Authors: Min Rex CheungAbstract:Background This study used receiver operating characteristic (ROC) curves to screen Surveillance, Epidemiology and End Results (SEER) skin melanoma data to identify and quantify the effects of socioeconomic factors on Cause Specific Survival. Methods 'SEER Cause-Specific death classification' was used as the outcome variable. The area under the ROC curve was to select best pretreatment predictors for further multivariate analysis with socioeconomic factors. Race and other socioeconomic factors including rural-urban residence, county level % college graduate and county level family income were used as predictors. Univariate and multivariate analyses were performed to identify and quantify the independent socioeconomic predictors. Results This study included 49,666 patients. The mean follow up time (SD) was 59.4 (17.1) months. SEER staging (ROC area of 0.80) was the most predictive factor. Race, lower county family income, rural residence, and lower county education attainment were significant univariates, but rural residence was not significant under multivariate analysis. Living in poor neighborhoods was associated with a 2-4% disadvantage in actuarial Cause Specific Survival. Conclusions Racial and socioeconomic factors have a significant impact on the Survival of melanoma patients. This generates the hypothesis that ensuring access to cancer care may eliminate these outcome disparities.
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African American race and low income neighborhoods decrease Cause Specific Survival of endometrial cancer: a SEER analysis.
Asian Pacific journal of cancer prevention : APJCP, 2013Co-Authors: Min Rex CheungAbstract:Background: This study analyzed Surveillance, Epidemiology and End Results (SEER) data to assess if socio-economic factors (SEFs) impact on endometrial cancer Survival. Materials and Methods: Endometrial cancer patients treated from 2004-2007 were included in this study. SEER Cause Specific Survival (CSS) data were used as end points. The areas under the receiver operating characteristic (ROC) curve were computed for predictors. Time to event data were analyzed with Kaplan-Meier method. Univariate and multivariate analyses were used to identify independent risk factors. Results: This study included 64,710 patients. The mean follow up time (S.D.) was 28.2 (20.8) months. SEER staging (ROC area of 0.81) was the best pretreatment predictor of CSS. Histology, grade, race/ethnicity and county level family income were also significant pretreatment predictors. African American race and low income neighborhoods decreased the CSS by 20% and 3% respectively at 5 years. Conclusions: This study has found significant endometrial Survival disparities due to SEFs. Future studies should focus on eliminating socio-economic barriers to good outcomes.
Jane L. Meisel - One of the best experts on this subject based on the ideXlab platform.
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Triple-negative breast cancer has worse overall Survival and Cause-Specific Survival than non-triple-negative breast cancer.
Breast cancer research and treatment, 2016Co-Authors: Jing Yang, Limin Peng, Aysegul A. Sahin, Lei Huo, Kevin C. Ward, Ruth O'regan, Mylin A. Torres, Jane L. MeiselAbstract:The current American Joint Committee on Cancer (AJCC) staging manual uses tumor size, lymph node, and metastatic status to stage breast cancer across different subtypes. We examined the prognosis of triple-negative breast cancer (TNBC) versus non-TNBC within the same stages and sub-stages to evaluate whether TNBC had worse prognosis than non-TNBC. We reviewed the National Cancer Institute Surveillance, Epidemiology, and End Results (SEER) data and identified 158,358 patients diagnosed with breast cancer from 2010 to 2012. The overall Survival (OS) time and breast cancer Cause-Specific Survival time were compared between patients with TNBC and non-TNBC in each stage and sub-stages. The results were validated using a dataset of 2049 patients with longer follow-up from our institution. Compared with patients with non-TNBC, patients with TNBC had worse OS and breast cancer Cause-Specific Survival time in every stage and sub-stage in univariate and multivariate analyses adjusting for age, race, tumor grade, and surgery and radiation treatments in the SEER data. The worse OS time in patients with TNBC was validated in our institutional dataset. Patients with TNBC have worse Survival than patients with non-TNBC. The new AJCC staging manual should consider breast cancer biomarker information.
Jing Yang - One of the best experts on this subject based on the ideXlab platform.
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Triple-negative breast cancer has worse overall Survival and Cause-Specific Survival than non-triple-negative breast cancer.
Breast cancer research and treatment, 2016Co-Authors: Jing Yang, Limin Peng, Aysegul A. Sahin, Lei Huo, Kevin C. Ward, Ruth O'regan, Mylin A. Torres, Jane L. MeiselAbstract:The current American Joint Committee on Cancer (AJCC) staging manual uses tumor size, lymph node, and metastatic status to stage breast cancer across different subtypes. We examined the prognosis of triple-negative breast cancer (TNBC) versus non-TNBC within the same stages and sub-stages to evaluate whether TNBC had worse prognosis than non-TNBC. We reviewed the National Cancer Institute Surveillance, Epidemiology, and End Results (SEER) data and identified 158,358 patients diagnosed with breast cancer from 2010 to 2012. The overall Survival (OS) time and breast cancer Cause-Specific Survival time were compared between patients with TNBC and non-TNBC in each stage and sub-stages. The results were validated using a dataset of 2049 patients with longer follow-up from our institution. Compared with patients with non-TNBC, patients with TNBC had worse OS and breast cancer Cause-Specific Survival time in every stage and sub-stage in univariate and multivariate analyses adjusting for age, race, tumor grade, and surgery and radiation treatments in the SEER data. The worse OS time in patients with TNBC was validated in our institutional dataset. Patients with TNBC have worse Survival than patients with non-TNBC. The new AJCC staging manual should consider breast cancer biomarker information.
Sylvia Kurz - One of the best experts on this subject based on the ideXlab platform.
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EPID-15. RACIAL AND SOCIOECONOMIC DISPARITIES DIFFERENTIALLY AFFECT OVERALL AND Cause-Specific Survival IN GLIOBLASTOMA
Neuro-Oncology, 2020Co-Authors: Elisa Liu, Erik P. Sulman, Sylvia KurzAbstract:Abstract BACKGROUND The prognostic role of racial and socioeconomic factors in patients with glioblastoma is controversially debated. We aimed to evaluate how these factors may affect Survival outcomes in an overall and Cause-Specific manner using large, national cancer registry cohort data in the temozolomide chemoradiation era. METHODS The National Cancer Institute’s Surveillance, Epidemiology, and End Results database was queried for patients diagnosed with glioblastoma between 2005 and 2016. Overall Survival was assessed using Cox proportional hazard models using disease intrinsic and extrinsic factors. Cause-Specific mortality was assessed using cumulative incidence curves and modeled using multivariate cumulative risk regression. RESULTS A total of 28,954 patients met the prespecified inclusion criteria and were included in this analysis. The following factors were associated with all-Cause mortality: age, calendar year of diagnosis, sex, treatment receipt, tumor size, tumor location, extent of resection, median household income, and race. Asian/Pacific Islanders and Hispanic Whites had lower mortality compared to Non-Hispanic Whites. Cause-Specific mortality was associated with both racial and socioeconomic groups. After adjusting for treatment and tumor-related factors, Asian/Pacific and black patients had lower glioblastoma-Specific mortality. However, lower median household income and black race were associated with significantly higher non-glioblastoma mortality. CONCLUSION Despite the aggressive nature of glioblastoma, racial and socioeconomic factors influence glioblastoma-Specific and non-glioblastoma associated mortality. Our study shows that patient race has an impact on glioblastoma-associated mortality independently of tumor and treatment related factors. Importantly, socioeconomic and racial differences largely contribute to non-glioblastoma mortality, including death from other cancers, cardio- and cerebrovascular events.
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racial and socioeconomic disparities differentially affect overall and Cause Specific Survival in glioblastoma
Journal of Neuro-oncology, 2020Co-Authors: Elisa K Liu, Erik P. Sulman, Sylvia KurzAbstract:The prognostic role of racial and socioeconomic factors in patients with glioblastoma is controversially debated. We aimed to evaluate how these factors may affect Survival outcomes in an overall and Cause-Specific manner using large, national cancer registry cohort data in the temozolomide chemoradiation era. The National Cancer Institute’s Surveillance, Epidemiology, and End Results database was queried for patients diagnosed with glioblastoma between 2005 and 2016. Overall Survival was assessed using Cox proportional hazard models using disease intrinsic and extrinsic factors. Cause-Specific mortality was assessed using cumulative incidence curves and modeled using multivariate cumulative risk regression. A total of 28,952 patients met the prespecified inclusion criteria and were included in this analysis. The following factors were associated with all-Cause mortality: age, calendar year of diagnosis, sex, treatment receipt, tumor size, tumor location, extent of resection, median household income, and race. Asian/Pacific Islanders and Hispanic Whites had lower mortality compared to Non-Hispanic Whites. Cause-Specific mortality was associated with both racial and socioeconomic groups. After adjusting for treatment and tumor-related factors, Asian/Pacific and black patients had lower glioblastoma-Specific mortality. However, lower median household income and black race were associated with significantly higher non-glioblastoma mortality. Despite the aggressive nature of glioblastoma, racial and socioeconomic factors influence glioblastoma-Specific and non-glioblastoma associated mortality. Our study shows that patient race has an impact on glioblastoma-associated mortality independently of tumor and treatment related factors. Importantly, socioeconomic and racial differences largely contribute to non-glioblastoma mortality, including death from other cancers, cardio- and cerebrovascular events.