CT Image Diagnostic Support System for Predicting EGFR Gene Mutations in Lung Cancer

Abstract

Lung cancer is the most common type of cancer and is the leading cause of cancer-related deaths in Japan. Regarding lung cancer diagnosis, a pivotal aspect in lung cancer treatment is tumor detection and selection of appropriate cancer treatment. Computer Tomography (CT) images are usually used to detect tumors. After tumor detection, identifying mutations in the Epidermal Growth Factor Receptor (EGFR) gene is essential for cancer treatment selection. The EGFR gene is a key factor associated with cancer cell proliferation its mutation appears both inside and around the tumors. This paper proposes a lung cancer diagnostic system designed to streamline the process from tumor detection to EGFR gene mutation identification. The proposed system consists of three modules: an input interface module, an automated lung tumor segmentation module, and an EGFR mutation prediction module. The system is characterized in that all modules are consistently based on image processing. Consequently, the proposed system enables users to acquire the diagnosis results of tumor detection as well as EGFR gene mutation prediction by just providing the input CT image data and the patients' clinical features. Our experimental results confirm that the system achieves performance levels comparable to existing research, both in terms of lung tumor segmentation accuracy and the precision of EGFR mutation predictions.