Work

Non-Invasive Classification of EGFR Mutation from Thoracic CT Images Using Radiomics Features and LightGBM

Year: 2025

Type: conference-paper

Abstract: Cancer caused 9.7 million deaths in 2022, including 1.8 million from lung cancer the leading cause of cancer death.EGFR mutation testing is essential for lung cancer treatment planning, but it is invasive and visual identification from chest CT images is difficult.This paper proposes a computer-aided diagnosis system to identify EGFR mutation status.Lung tumor regions were automatically extracted and radiomics features were obtained.Dimensionality reduction was performed using null importance, variance inflation factor, and recursive feature elimination.The method was applied to 143 cases and achieved an accuracy of 59.1%, a true positive rate of 54.3% and a false positive rate of 36.1%.The results suggest that CAD (Computer-Aided Diagnosis) systems can improve the non-invasive detection of EGFR mutations in lung cancer.

Source: Proceedings of International Conference on Artificial Life and Robotics

DOI: 10.5954/icarob.2025.gs2-2

Authors: Reo Takahashi, Tohru Kamiya, Takashi Terasawa, Takatoshi Aoki

Institutions: Kyushu Institute of Technology, University of Occupational and Environmental Health Japan

Language: English

Topic: Radiomics and Machine Learning in Medical Imaging

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: bronze

OpenAlex ID: W4409913794