2024 World Conference on Lung Cancer (WCLC) - ePosters-EP.06G.14 Discovery of Immune-Related Long Noncoding RNA Signatures in Lung Adenocarcinoma

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The document outlines the development of a prognostic model for lung adenocarcinoma by David Hu from UCLA. It includes several key components, emphasizing methodology, data visualization, model construction, and evaluation metrics.<br /><br />Initially, the document mentions the visualization techniques used to understand the differential expression of genes in lung adenocarcinoma, such as heatmaps and volcano plots. These tools help identify key genes that might contribute to patient outcomes.<br /><br />Following this, attention is given to the construction and evaluation of the prognostic model, which involves model selection strategies that likely include statistical or machine learning techniques aimed at predicting patient outcomes based on gene expression data. It also references survival curves, which are essential for visualizing the time until an event, like death or progression, occurs in different patient groups.<br /><br />The document provides a comparison of model performance using indices such as the C-index and hazard ratios (HR). These metrics are critical for understanding the accuracy and reliability of the prognostic model in predicting outcomes. The C-index measures the concordance between predicted and observed outcomes, while the HR provides insights into the relative risk of events between groups.<br /><br />Furthermore, the model's performance is evaluated at different time intervals—1, 3, and 5 years—using the area under the curve (AUC) metric. AUC values at these intervals assess the model's predictive ability over shorter and longer-term periods, which is crucial for clinical applicability in guiding treatment decisions.<br /><br />Overall, the document provides a comprehensive framework for developing and evaluating a prognostic model tailored for lung adenocarcinoma, focusing on gene expression data to predict patient outcomes and facilitate personalized medicine approaches.

Asset Subtitle

David Hu

Meta Tag

Speaker David Hu

Topic Pathology and Biomarkers

Keywords

prognostic model

lung adenocarcinoma

David Hu

UCLA

data visualization

gene expression

C-index

hazard ratios

survival curves

area under the curve