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The accurate identification and characterization of pulmonary nodules at low-dose chest computed tomography (CT) images is an essential requirement for the implementation of effective lung cancer screening. Manual detection of lung nodules by the radiologist is a sequential and time-consuming process. Different nodule detection approaches are described elaborately in this work. Computer-aided diagnosis system acts as an assistance for the radiologists, by making final decision quickly with higher accuracy and greater confidence. Scholars have proposed that a large number of high-dimensional quantitative features can be mined and combined with statistical models to comprehensively classify medical tumor images, namely, radiomics. Many researches have already demonstrated that radiomics approaches are potentially useful for lung nodule classification, risk stratification, individualized management and prediction of outcome. Recently, it is observed that deep learning (DL)-based approaches are applied extensively for nodule detection and characterization. Therefore, emphasis has been given to convolutional neural network (CNN)-based DL approaches by describing different CNN-based networks. Hence, the development of non-invasive strategies to accurately classify and risk stratify screen-detected pulmonary nodules in order to individualize clinical management remains a high priority area of research. The goal of this review is to summarize the current literature regarding functional imaging for screening detected lung nodule management with CT and discuss its clinical application along with future goals and challenges.

Early-stage lung cancer detection from radiomics to deep learning in thoracic CT images: a narrative review with contemporary clinical recommendations