OPTIMIZED RADIOMICS-BASED MACHINE LEARNING APPROACH FOR LUNG CANCER SUBTYPE CLASSIFICATION

Lung cancer is a major global health concern and a leading cause of cancer-related deaths. Accurate diagnosis and treatment of lung cancer are crucial for improving patient outcomes. Subtyping lung cancer provides essential information about its molecular characteristics, clinical behavior, and prognosis, thereby guiding treatment planning. Radiomics, a novel discipline, offers a promising approach to characterize the tumor microenvironment by extracting quantitative imaging features from medical images. Radiomics aims to comprehensively and non-invasively characterize tumors and their microenvironment, enabling the identification of tumor subtypes, prediction of therapy response, and enhancement of patient outcomes. This study evaluates the effectiveness of a Particle Swarm Optimization-Random Forest (PSO-RF) classifier for subtype categorization of lung cancer based on radiomics using computed tomography (CT) images. The study utilizes three datasets, extracting 1093 radiomic features and reducing them to 20 significant features through extra tree feature selection. Optimized parameters of the PSO-RF classifier are determined using 10-fold cross-validation and compared to traditional machine learning classifiers and reported works. Results demonstrate that the PSO-RF classifier outperforms other methods, achieving an accuracy of 92%, precision of 92.5%, recall of 92%, and F 1-score of 0.92 in the Lung1 dataset. Training on Dataset 3 and validating the Lung3 dataset confirm the generalizability of the model, yielding an accuracy of 87% and an AUC of 0.91 across diverse scenarios. These findings highlight the efficacy of radiomics in identifying lung cancer subtypes and demonstrate the potential of the PSO-RF classifier. The incorporation of radiomics into clinical practice has the potential to greatly improve patient outcomes by customizing treatment approaches according to unique tumor characteristics. The demonstrated effectiveness of the PSO-RF classifier makes it a valuable resource for diagnosing and categorizing different subtypes of lung cancer.

Automated summary

Lung cancer is a global health concern, and radiomics, a novel discipline that extracts quantitative imaging features from medical images, shows promise in characterizing tumor subtypes. This study evaluates the effectiveness of a PSO-RF classifier based on radiomics using CT images, demonstrating its superiority compared to other methods.