Dataset Information

Clinical Significance of Various Classification Standards of Age Groups in Predicting Survival of Patients with Glioblastoma.

ABSTRACT: BACKGROUND The present study aimed to assess the association of various age groups with survival in patients with glioblastoma. MATERIAL AND METHODS The Surveillance, Epidemiology, and End Results (SEER) database was used to extracted data on new diagnoses of glioblastoma between 2005 and 2015. Four age models were constructed according to the age at diagnosis. RESULTS A total of 28 734 patients with glioblastoma (16 823 men and 11 911 women) were enrolled in the study. In multivariate analysis, variables including sex, race, tumor, and clinical information were identified as confounding factors to adjust 4 age models. In model 1, ages 39-58, 59-78, and 79+ years were risk factors of survival compared with age 0-18 years. In model 2, ages 18-65, 66-79, and 80+ years were prognostic factors of shorter survival compared with ages 0-17 years. In model 3, ages 45-59, 60-74, and 75+ years were associated with poor prognosis, while ages 18-44 years was associated with favorable clinical outcomes compared with ages 0-17 years. In model 4, ages 18-53, 54-64, and 65+ years were associated with poor prognosis. CONCLUSIONS The differences in prognoses in different age groups of glioblastoma patients suggest that clinicians should incorporate age into routine clinical assessments and develop appropriate treatment strategies.

SUBMITTER: Zhou X

PROVIDER: S-EPMC7282532 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Clinical Significance of Various Classification Standards of Age Groups in Predicting Survival of Patients with Glioblastoma.

Zhou Xingwang X Niu Xiaodong X Mao Qing Q Liu Yanhui Y

Medical science monitor : international medical journal of experimental and clinical research 20200530

BACKGROUND The present study aimed to assess the association of various age groups with survival in patients with glioblastoma. MATERIAL AND METHODS The Surveillance, Epidemiology, and End Results (SEER) database was used to extracted data on new diagnoses of glioblastoma between 2005 and 2015. Four age models were constructed according to the age at diagnosis. RESULTS A total of 28 734 patients with glioblastoma (16 823 men and 11 911 women) were enrolled in the study. In multivariate analysis, ...[more]

PMID: 32471970

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Project description:BackgroundSocial media are important for monitoring perceptions of public health issues and for educating target audiences about health; however, limited information about the demographics of social media users makes it challenging to identify conversations among target audiences and limits how well social media can be used for public health surveillance and education outreach efforts. Certain social media platforms provide demographic information on followers of a user account, if given, but they are not always disclosed, and researchers have developed machine learning algorithms to predict social media users' demographic characteristics, mainly for Twitter. To date, there has been limited research on predicting the demographic characteristics of Reddit users.ObjectiveWe aimed to develop a machine learning algorithm that predicts the age segment of Reddit users, as either adolescents or adults, based on publicly available data.MethodsThis study was conducted between January and September 2020 using publicly available Reddit posts as input data. We manually labeled Reddit users' age by identifying and reviewing public posts in which Reddit users self-reported their age. We then collected sample posts, comments, and metadata for the labeled user accounts and created variables to capture linguistic patterns, posting behavior, and account details that would distinguish the adolescent age group (aged 13 to 20 years) from the adult age group (aged 21 to 54 years). We split the data into training (n=1660) and test sets (n=415) and performed 5-fold cross validation on the training set to select hyperparameters and perform feature selection. We ran multiple classification algorithms and tested the performance of the models (precision, recall, F1 score) in predicting the age segments of the users in the labeled data. To evaluate associations between each feature and the outcome, we calculated means and confidence intervals and compared the two age groups, with 2-sample t tests, for each transformed model feature.ResultsThe gradient boosted trees classifier performed the best, with an F1 score of 0.78. The test set precision and recall scores were 0.79 and 0.89, respectively, for the adolescent group (n=254) and 0.78 and 0.63, respectively, for the adult group (n=161). The most important feature in the model was the number of sentences per comment (permutation score: mean 0.100, SD 0.004). Members of the adolescent age group tended to have created accounts more recently, have higher proportions of submissions and comments in the r/teenagers subreddit, and post more in subreddits with higher subscriber counts than those in the adult group.ConclusionsWe created a Reddit age prediction algorithm with competitive accuracy using publicly available data, suggesting machine learning methods can help public health agencies identify age-related target audiences on Reddit. Our results also suggest that there are characteristics of Reddit users' posting behavior, linguistic patterns, and account features that distinguish adolescents from adults.

Project description:Glioblastoma (GBM) is the most common form of brain tumor and has a uniformly poor prognosis. Development of prognostic biomarkers in easily accessible serum samples have the potential to improve the outcomes of patients with GBM through personalized therapy planning. In this study pre-treatment serum samples from 30 patients newly diagnosed with GBM were evaluated using a 40-protein multiplex ELISA platform. Analysis of potentially relevant gene targets using The Cancer Genome Atlas database was done using the Glioblastoma Bio Discovery Portal (GBM-BioDP). A ten-biomarker subgroup of clinically relevant molecules was selected using a functional grouping analysis of the 40 plex genes with two genes selected from each group on the basis of degree of variance, lack of co-linearity with other biomarkers and clinical interest. A Multivariate Cox proportional hazard approach was used to analyze the relationship between overall survival (OS), gene expression, and resection status as covariates. Thirty of 40 of the MSD molecules mapped to known genes within TCGA and separated the patient cohort into two main clusters centered predominantly around a grouping of classical and proneural versus the mesenchymal subtype as classified by Verhaak. Using the values for the 30 proteins in a prognostic index (PI) demonstrated that patients in the entire cohort with a PI below the median lived longer than those patients with a PI above the median (HR 1.8, p=0.001) even when stratified by both age and MGMT status. This finding was also consistent within each Verhaak subclass and highly significant (range p=0.0001-0.011). Additionally, a subset of ten proteins including, CRP, SAA, VCAM1, VEGF, MDC, TNFA, IL7, IL8, IL10, IL16 were found to have prognostic value within the TCGA database and a positive correlation with overall survival in GBM patients who had received gross tumor resection followed by conventional radiation therapy and temozolomide treatment concurrent with the addition of valproic acid. These findings demonstrate that proteomic approaches to the development of prognostic assays for treatment of GBM may hold potential clinical value.

Dataset Information

Clinical Significance of Various Classification Standards of Age Groups in Predicting Survival of Patients with Glioblastoma.

Publications

Clinical Significance of Various Classification Standards of Age Groups in Predicting Survival of Patients with Glioblastoma.

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