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Lung neuroendocrine tumors (NETs) have few known predictors of survival. We investigated associations of sociodemographic, clinicopathologic, and treatment factors with overall survival (OS) and lung cancer-specific survival (LCSS) for incident lung NET cases (typical or atypical histology) in the California Cancer Registry (CCR) from 1992 to 2019. OS was estimated with the Kaplan-Meier method and compared by sociodemographic and disease factors univariately with the log-rank test. We used sequential Cox proportional hazards regression for multivariable OS analysis. LCSS was estimated using Fine-Gray competing risks regression. There were 6038 lung NET diagnoses (5569 typical, 469 atypical carcinoid); most were women (70%) and non-Hispanic White (73%). In our multivariable model, sociodemographic factors were independently associated with OS, with better survival for women (hazard ratio (HR) 0.62, 95% confidence interval (CI) 0.57-0.68, P < 0.001), married (HR 0.76, 95% CI 0.70-0.84, P < 0.001), and residents of high socioeconomic status (SES) neighborhoods (HRQ5vsQ1 0.73, 95% CI 0.62-0.85, P < 0.001). Compared to cases with private insurance, OS was worse for cases with Medicare (HR 1.24, 95% CI 1.10-1.40, P < 0.001) or Medicaid/other public insurance (HR 1.45, 95% CI 1.24-1.68, P < 0.001). In our univariate model, non-Hispanic Black Californians had worse OS than other racial/ethnic groups, but differences attenuated after adjusting for stage at diagnosis. In our LCSS models, we found similar associations between sex and marital status on survival, but no differences in outcomes by SES or insurance. By race/ethnicity, American Indian cases had worse LCSS. In summary, beyond disease-related and treatment variables, sociodemographic factors were independently associated with survival in lung NETs.
Pubmed ID: 37882324
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SEER collects cancer incidence data from population-based cancer registries covering approximately 47.9 percent of the U.S. population. The SEER registries collect data on patient demographics, primary tumor site, tumor morphology, stage at diagnosis, and first course of treatment, and they follow up with patients for vital status.There are two data products available: SEER Research and SEER Research Plus. This was motivated because of concerns about the increasing risk of re-identifiability of individuals. The Research Plus databases require more rigorous process for access that includes user authentication through Institutional Account or multiple-step request process for Non-Institutional users.
View all literature mentionsTHIS RESOURCE IS NO LONGER IN SERVICE. Documented on March 17, 2022. Islander is a comprehensive online database containing genomic islands discovered in completely sequenced bacterial genomes by the algorithm. Islands are transmitted between prokaryotic strains and therefore play a major role in genome evolution. An island often encodes an integrase gene that specifies the island''s position in the host genome. Usually integrases specify tRNA genes, and the island splits the the tRNA gene when it integrates. However the island also carries sequence that replaces the split-off portion, restoring an intact tRNA gene. Thus an island is often marked by a tRNA gene at one end, and a fragment of that gene at the other end. The islands in this database were identified using this principle through the following procedure: :1. Search for tRNA and tmRNA genes using tRNAscan-SE and BRUCE on whole prokaryotic genomes. :2. Search for significant hits to each tRNA and tmRNA gene using BLAST against the source genome. :3. Narrow down hits to those containing integrase genes (required for site-specific integration into the host genome). :4. Remove false positives (e.g., tRNA gene fragment not from end of gene, or in wrong orientation). :5. Enter into mysql database, display on website using Perl CGI pages.
View all literature mentionsA Software resource for statistical analysis and presentation of graphics.
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