Feasibility Study of MRI and Multimodality CT/MRI Radiomics for Lung Nodule Classification

Abstract

Lung cancer is the most common and lethal form of cancer, and its early diagnosis is key to the patient's survival. CT is the reference imaging scan for lung cancer screening; however, it presents the drawback of exposing the patient to ionizing radiation. Recent studies have shown the relevance of MRI in lung nodules diagnosis. In this work, we aimed to evaluate whether radiomics features from MRI are well-suited for lung nodules characterization and if the combination of CT and MRI features can yield better results than the features from the individual modalities. For such, we segmented paired CT and MRI nodules from 33 lung nodules patients, extracted 89 radiomics features from each modality, and combined it into a multimodality feature set. Those features were then used for classifying the nodules into benign and malignant by a set of machine learning algorithms, assessing the AUC across 30 trials. Our results show that MRI radiomics features are suitable for characterizing lung lesions, yielding AUC values up to 17% higher than their CT counterparts, and shedding light on MRI as a viable image modality for decision support systems. Conversely, our multimodality approach did not improve performance compared to the single-modality models, suggesting that the direct combination of multimodality features might not be an adequate strategy for dealing with multimodality medical images.