Receiver operating characteristic curve analysis of SEER medulloblastoma and primitive neuroectodermal tumor (PNET) outcome data: identification and optimization of predictive models.

PURPOSE: This study used receiver operating characteristic curves to analyze Surveillance, Epidemiology and End RESULTS (SEER) medulloblastoma (MB) and primitive neuroectodermal tumor (PNET) outcome data. The aim of this study was to identify and optimize predictive outcome models. MATERIALS AND METHODS: Patients diagnosed from 1973 to 2009 were selected for analysis of socio-economic, staging and treatment factors available in the SEER database for MB and PNET. For the risk modeling, each factor was fitted by a generalized linear model to predict the outcome (brain cancer 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. A Monte Carlo algorithm was used to estimate the modeling errors. RESULTS: There were 3,702 patients included in this study. The mean follow up time (S.D.) was 73.7 (86.2) months. Some 40% of the patients were female and the mean (S.D.) age was 16.5 (16.6) years. There were more adult MB/PNET patients listed from SEER data than pediatric and young adult patients. Only 12% of patients were staged. The SEER staging has the highest ROC (S.D.) area of 0.55 (0.05) among the factors tested. We simplified the 3-layered risk levels (local, regional, distant) to a simpler non-metastatic (I and II) versus metastatic (III) model. The ROC area (S.D.) of the 2-tiered model was 0.57 (0.04). CONCLUSIONS: ROC analysis optimized the most predictive SEER staging model. The high under staging rate may have prevented patients from selecting definitive radiotherapy after surgery.

Optimization of predictors of Ewing sarcoma cause-specific survival: a population study.

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.

Under-use of radiotherapy in stage III bronchioaveolar lung cancer and socio-economic disparities in cause specific survival: a population study.

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.

Socio-economic factors affect the outcome of soft tissue sarcoma: an analysis of SEER data.

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.

Association between urinary cadmium and all cause, all cancer and prostate cancer specific mortalities for men: an analysis of national health and nutrition examination survey (NHANES III) data.

AIM: This study employed public use National Health and Nutrition Examination Survey (NHANES III) data to investigate the association between urinary cadmium (UDPSI) and all cause, all cancer and prostate cancer mortalities in men. PATIENTS AND METHODS: NHANES III household adult, laboratory and mortality data were merged. The sampling weight used was WTPFEX6, with SDPPSU6 applied for the probability sampling unit and SDPSTRA6 to designate the strata for the survey analysis. RESULTS: For prostate cancer death, the significant univariates were UDPSI, age, weight, and drinking. Under multivariate logistic regression, the significant covariates were age and weight. For all cause mortality in men, the significant covariates were UDPSI, age, and poverty income ratio. For all cancer mortality in men, the significant covariates were UDPSI, age, black and Mexican race. CONCLUSIONS: UDPSI was a predictor of all cause and all cancer mortalities in men as well as prostate cancer mortality.

Surveying and optimizing the predictors for ependymoma specific survival using SEER data.

PURPOSE: This study used receiver operating characteristic curve to analyze Surveillance, Epidemiology and End Results (SEER) ependymoma data to identify predictive models and potential disparity in outcome. MATERIALS AND METHODS: This study analyzed socio-economic, staging and treatment factors available in the SEER database for ependymoma. For the risk modeling, each factor was fitted by a Generalized Linear Model to predict the outcome ('brain and other nervous systems' specific death in yes/no). The area under the receiver operating characteristic curve (ROC) was computed. Similar strata were combined to construct the most parsimonious models. A random sampling algorithm was used to estimate the modeling errors. Risk of ependymoma death was computed for the predictors for comparison. RESULTS: A total of 3,500 patients diagnosed from 1973 to 2009 were included in this study. The mean follow up time (S.D.) was 79.8 (82.3) months. Some 46% of the patients were female. The mean (S.D.) age was 34.4 (22.8) years. Age was the most predictive factor of outcome. Unknown grade demonstrated a 15% risk of cause specific death compared to 9% for grades I and II, and 36% for grades III and IV. A 5-tiered grade model (with a ROC area 0.48) was optimized to a 3-tiered model (with ROC area of 0.53). This ROC area tied for the second with that for surgery. African-American patients had 21.5% risk of death compared with 16.6% for the others. Some 72.7% of patient who did not get RT had cerebellar or spinal ependymoma. Patients undergoing surgery had 16.3% risk of death, as compared to 23.7% among those who did not have surgery. CONCLUSION: Grading ependymoma may dramatically improve modeling of data. RT is under used for cerebellum and spinal cord ependymoma and it may be a potential way to improve outcome.

Low income and rural county of residence increase mortality from bone and joint sarcomas.

BACKGROUND: This is a part of a larger effort to characterize the effects on socio-economic factors (SEFs) on cancer outcome. Surveillance, Epidemiology and End Result (SEER) bone and joint sarcoma (BJS) data were used to identify potential disparities in cause specific survival (CSS). MATERIALS AND METHODS: This study analyzed SEFs in conjunction with biologic and treatment factors. Absolute BJS specific risks were calculated and the areas under the receiver operating characteristic (ROC) curve were computed for predictors. Actuarial survival analysis was performed with Kaplan-Meier method. Kolmogorov-Smirnov's 2-sample test was used to for comparing two survival curves. Cox proportional hazard model was used for multivariate analysis. RESULTS: There were 13501 patients diagnosed BJS from 1973 to 2009. The mean follow up time (SD) was 75.6 (90.1) months. Staging was the highest predictive factor of outcome (ROC area of 0.68). SEER stage, histology, primary site and sex were highly significant pre-treatment predictors of CSS. Under multivariate analysis, patients living in low income neighborhoods and rural areas had a 2% and 5% disadvantage in cause specific survival respectively. CONCLUSIONS: This study has found 2-5% decrement of CSS of BJS due to SEFs. These data may be used to generate testable hypothesis for future clinical trials to eliminate BJS outcome disparities.

Lack of health insurance increases all cause and all cancer mortality in adults: an analysis of National Health and Nutrition Examination Survey (NHANES III) data.

BACKGROUND: Public use National Health and Nutrition Examination Survey (NHANES III) and NHANES III linked mortality data were here applied to investigate the association between health insurance coverage and all cause and all cancer mortality in adults. PATIENTS AND METHODS: NHANES III household adult, laboratory and mortality data were merged. Only patients examined in the mobile examination center (MEC) were included in this study. The sampling weight employed was WTPFEX6, SDPPSU6 being used for the probability sampling unit and SDPSTRA6 to designate the strata for the survey analysis. All cause and all cancer mortalities were used as binary outcomes. The effect of health insurance coverage status on all cause and all cancer mortalities were analyzed with potential socioeconomic, behavioral and health status confounders. RESULTS: There were 2398 sample persons included in this study. The mean age was 40 years and the mean (S.E.) follow up was 171.85 (3.12) person months from the MEC examination. For all cause mortality, the odds ratios (significant p-values) of the covariates were: age, 1.0095 (0.000); no health insurance coverage (using subjects with health insurance), 1.71 (0.092); black race (using non-Hispanic white subjects as the reference group) 1.43, (0.083); Mexican-Americans, 0.60 (0.089); DMPPIR, 0.82, (0.000); and drinking hard liquor, 1.014 (0.007). For all cancer mortality, the odds ratio (significant p-values) of the covariates were: age, 1.0072 (0.00); no health insurance coverage, using with health coverage as the reference group, 2.91 (0.002); black race, using non-Hispanic whites as the reference group, 1.64 (0.047); Mexican Americans, 0.33 (0.008) and smoking, 1.017 (0.118). CONCLUSION: There was a 70% increase in risk of all cause death and almost 300% of all cancer death for people without any health insurance coverage.

African American race and low income neighborhoods decrease cause specific survival of endometrial cancer: a SEER analysis.

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.

Serum hepatitis a antibody positivity correlates with higher pancreas cancer mortality in adults: implications for hepatitis vaccination in high risk areas.

BACKGROUND: This study used pre-hepatitis A vaccination era data in U.S. to study the relationship between serum hepatitis A antibody positivity with pancreas cancer mortality in adults. PATIENTS AND METHODS: Public use National Health and Nutrition Examination Survey (NHANES III) data were employed. NHANES III uses complex probabilistic methods to sample nationally representative samples. Household adult laboratory and mortality data were merged. Sample persons who were available to be examined in the Mobile Examination Center (MEC) were included in this study. All results were obtained by using specialized survey software taking into account the primary sampling unit and stratification variables and the weights assigned to the sample persons examined in the MEC. Thus they are representative of the U.S. population. RESULTS: The mean risk (95%CI) of death in the study population for pancreas cancer was 0.0014 (-0.000069 -.0029); their mean age (95%CI) at the mobile examination center (MXPAXTMR) was 473.43 (463.85-482.10); the follow up in months from their medical examination (permth_exm) was 170.12 (164.17-176.07). The odds ratios (S.E.) of the statistically significant univariables were: age, 1.007 (1.005-1.009); serum anti-hepatitis antibody status, 0.038 (0.004-0.376); and drinking hard liquor, 1.014 (1.004-1.023). The coefficients (S.E.) of the statistically significant variables after multivariate analysis were 0.006 (0.002-0.010) for age and -2.528 (-4.945--0.111) for serum anti-hepatitis A antibody negativity (using serum anti-hepatitis A antibody positivity as a reference). CONCLUSION: Serum hepatitis A antibody positivity correlates with higher pancreas cancer mortality in adults.

Blood lead concentration correlates with all cause, all cancer and lung cancer mortality in adults: a population based study.

BACKGROUND: This study used National Health and Nutrition Examination Survey III to study the relationship between blood lead concentration and all cause, all cancer and lung cancer mortality in adults. PATIENTS AND METHODS: Public use National Health and Nutrition Examination Survey (NHANES III) data were used. NHANES III uses stratified, multistage probabilistic methods to sample nationally representative samples. Household adult, laboratory and mortality data were merged. Sample persons who were available to be examined in aMobile Examination Center (MEC) were included in this study. Specialized survey analysis software was used. RESULTS: A total of 3,482 sample participants with complete information for all variables were included in this analysis. For all cause death, the odds ratios (S.E.) for statistically significant variables were body mass index, 1.03 (1.01- 1.06); age 1.01 (1.01-1.01); blood lead concentration, 1.05 (1.01-1.08); poverty income ratio, 0.823 (0.76-0 .89); and drinking hard liquor, 1.01 (1.00-1.02). For all cancer mortality, the odds ratios (S.E.) of the statistically significant variables were: age, 1.01 (1.01-1.01); blood lead concentration, 1.07 (1.04-1.12), black race, using non-Hispanic white as reference, 1.69 (1.12-2.56); and smoking, 1.02 (1.01-1.04). For lung cancer mortality, the odds ratios (S.E.) of the statistically significant variables were: age, 1.01 (1.01-1.01); blood lead concentration, 1.09 (1.05-1.13); Mexican Americans, using non-Hispanic white as reference, 0.33 (0.129-0.850); other races, 1.80 (0.53-6.18); and smoking, 1.03 (1.02-1.05). CONCLUSION: Blood lead concentration correlated with all cause, all cancer, and lung cancer mortality in adults.

Using SEER data to quantify effects of low income neighborhoods on cause specific survival of skin melanoma.

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.

Assessing the impact of socio-economic variables on breast cancer treatment outcome disparity.

BACKGROUND: We studied Surveillance, Epidemiology and End Results (SEER) breast cancer data of Georgia USA to analyze the impact of socio-economic factors on the disparity of breast cancer treatment outcome. MATERIALS AND METHODS: This study explored socio-economic, staging and treatment factors that were available in the SEER database for breast cancer from Georgia registry diagnosed in 2004-2009. An area under the receiver operating characteristic curve (ROC) was computed for each predictor to measure its discriminatory power. The best biological predictors were selected to be analyzed with socio-economic factors. Survival analysis, Kolmogorov- Smirnov 2-sample tests and Cox proportional hazard modeling were used for univariate and multivariate analyses of time to breast cancer specific survival data. RESULTS: There were 34,671 patients included in this study, 99.3% being females with breast cancer. This study identified race and education attainment of county of residence as predictors of poor outcome. On multivariate analysis, these socio-economic factors remained independently prognostic. Overall, race and education status of the place of residence predicted up to 10% decrease in cause specific survival at 5 years. CONCLUSIONS: Socio-economic factors are important determinants of breast cancer outcome and ensuring access to breast cancer treatment may eliminate disparities.

Influence of obesity on biochemical and clinical failure after external-beam radiotherapy for localized prostate cancer.

BACKGROUND: Several reports have shown that obesity is associated with increased risk of biochemical failure after radical prostatectomy. However, limited information is available regarding the impact of obesity on prostate cancer progression after radiotherapy. The current study sought to determine whether obesity was an independent predictor of biochemical failure (BF) and clinical recurrence (CF) among patients treated with external-beam radiotherapy (EBRT). METHODS: A retrospective analysis was performed on 873 patients receiving EBRT as the sole treatment for localized prostate cancer between 1988 and 2001. The Kaplan-Meier method, log-rank test, and Cox proportional hazards analyses were performed. RESULTS: Of the 873 patients, 18% were mildly obese and 5% were moderately to severely obese. Obesity was related to younger age at diagnosis (P < .001), more recent year of diagnosis (P = .03), and race (P = .03), with African-American men having the highest obesity rates. During a mean follow-up of 96 months, 295 patients experienced BF and 127 had CF. On multivariate analysis, controlling for clinical and treatment characteristics, increased body mass index (BMI) significantly predicted BF (hazards ratio [HR] = 1.04; 95% confidence interval [95% CI], 1.02-1.07) with a positive trend by BMI category (P = .001). Similar results were found when the outcome was CF; BMI remained an independent predictor of progression (HR = 1.05; 95% CI, 1.01-1.09), with a statistically significant trend by increased BMI category (P = .03). CONCLUSIONS: The current findings validate the important role of obesity, not only on BF but also on CF, and suggest a link to the biologic basis of tumor progression that can be therapeutically exploited.

Rise in serum PSA of 1.5 ng/mL above 24-month nadir after external beam radiotherapy is predictive of biochemical failure.

OBJECTIVES: To determine whether a rise in the serum prostate-specific antigen (PSA) concentration 24 months or later after completion of external beam radiotherapy (EBRT) for prostate cancer could predict for biochemical failure. METHODS: We evaluated the records of 1006 patients who had undergone full-dose EBRT alone as primary treatment for T1-T4NxM0 prostate cancer at our institution between April 1987 and January 1998. Patients who had biochemical failure--as determined by the American Society for Therapeutic Radiology and Oncology (ASTRO) definition--prior to 24 months after EBRT were excluded. PSA increases of four different magnitudes (0.5, 0.8, 1.0, and 1.5 ng/mL above the 24-month nadir) were evaluated for their ability to predict ASTRO-defined biochemical failure. RESULTS: A total of 745 patients met the analysis criteria. The rate of ASTRO-defined biochemical failure in patients with a PSA increase of 0.5, 0.8, 1.0, and 1.5 ng/mL above the 24-month nadir was 56%, 64%, 66%, and 71%, respectively. An increase of 1.5 ng/mL or more had a sensitivity of 80% and a specificity of 88% in the prediction of biochemical failure, with an accuracy of 86%. CONCLUSIONS: A PSA increase of 1.5 ng/mL or more above the 24-month nadir can be used to predict for ASTRO-defined failure after EBRT and may be used to identify patients at risk early-on.

Identification of factors predicting response to adjuvant radiation therapy in patients with positive margins after radical prostatectomy.

PURPOSE: Radical prostatectomy (RP) is a highly effective treatment for patients with prostate cancer. However, patients with positive surgical margins after radical prostatectomy have less than ideal outcomes with 5-year progression rates between 36% and 50%. Postoperative radiation therapy (RT) is often advocated for improving these outcomes. We identified predictors of response to adjuvant RT given for positive margins after RP. MATERIALS AND METHODS: We retrospectively reviewed the clinical records of men who underwent RP between 1987 and 1999 at our institution and who received adjuvant RT for positive surgical margins. Only patients in whom prostate specific antigen (PSA) was undetectable after RP as well as before the initiation of RT were included. Numerous clinicopathological variables, including pre-RP PSA, pathological stage, margin length and location, and extracapsular extension or seminal vesicle involvement, were assessed for their adverse effect on the biochemical recurrence rate after adjuvant RT. RESULTS: A total of 62 men met our inclusion criteria. Median age at surgery was 60.7 +/- 6.1 years and median PSA at presentation was 9.0 ng/ml (range 1.4 to 64.9). The median RT dose was 60.0 +/- 3.6 Gy. RT was started a median of 5.0 +/- 3.6 months after RP. The 5 and 10-year biochemical disease-free survival rates for the whole group were 90.2% and 87.9%, respectively. Of all parameters tested only Gleason score 4 + 3 or greater (p = 0.037) and pre-RP PSA greater than 10.9 ng/ml (p = 0.040) were predictive of biochemical recurrence after adjuvant RT on univariate analysis. On multivariate analysis only pre-RP PSA greater than 10.9 ng/ml remained an independent predictor (p = 0.031). CONCLUSIONS: In the setting of true adjuvant RT in patients with positive margins after RP and undetectable PSA those with predominant Gleason grade 4 or greater, or PSA greater than 10.9 ng/ml at presentation are at increased risk for recurrence after adjuvant RT.