2023 World Conference on Lung Cancer (Posters)-P1.22. Nomogram Prediction for the Detection of Epidermal Growth Factor Receptor Mutation in Lung Adenocarcinoma Patients in Indonesia

P1.22. Nomogram Prediction for the Detection of Epidermal Growth Factor Receptor Mutation in Lung Adenocarcinoma Patients in Indonesia - PDF(Slides)

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Researchers from Udayana University in Indonesia have developed a predictive nomogram for the detection of epidermal growth factor receptor (EGFR) mutation in lung adenocarcinoma patients. Lung cancer is a significant health issue globally, including in Indonesia, with a high incidence rate. EGFR mutation is a crucial biomarker for predicting response to EGFR-targeted therapies in lung cancer patients. However, there are challenges in detecting EGFR mutations in resource-limited settings like Indonesia.<br /><br />This study aimed to address the problem of inadequate EGFR mutation examination and the lack of predictive modeling applicable to the Indonesian population. The researchers conducted a literature review to identify predictive factors associated with EGFR mutation in patients with lung adenocarcinoma. They then collected data from 80 patients with EGFR-mutated lung adenocarcinomas and 80 patients with EGFR-wild-type tumors in Bali, Indonesia.<br /><br />Based on the data collected, the researchers created a nomogram that included factors such as gender, smoking history, tumor diameter, tumor location, bubble-like lucency, and air-bronchogram. The nomogram showed good calibration and discrimination power for predicting EGFR mutation. External validation of the model also confirmed its accuracy.<br /><br />The researchers concluded that their nomogram could effectively predict EGFR mutation in lung adenocarcinoma patients in Bali, Indonesia. However, they acknowledged the need for further studies to consider other potential factors and conduct external validations to enhance the predictive power of the nomogram. This research is significant as it provides a valuable tool for predicting EGFR mutation and guiding treatment decisions for lung adenocarcinoma patients in resource-limited settings.

Asset Subtitle

Edwin Njoto

Meta Tag

Speaker Edwin Njoto

Topic Pathology & Biomarkers: Genetic Biomarkers

Keywords

Udayana University

Indonesia

predictive nomogram

epidermal growth factor receptor

EGFR mutation

lung adenocarcinoma

lung cancer

biomarker

predictive modeling

resource-limited settings