Narrative review of radiomics for classifying pulmonary nodules and potential impact on lung cancer screening

Lung cancer screening has proven to be a useful tool for identifying early stage lung cancers, however, the overall accuracy can sometimes lead to false positive and negatives that have potential adverse effects on patient outcomes. Advancement in computational methods have allowed for quantification of pulmonary nodule imaging features, referred to as radiomics, which have the potential to increase lung cancer screening accuracy and improve patient management. The initial part of this review covers common radiomic features and the challenges in deriving them. The second part of this review systematically evaluates literature relating to radiomics and lung cancer finding articles in areas that might have the potential to change management in lung cancer screening. Pertinent literature included initial nodule classification as benign or malignant, classifying subsolid nodules as invasive or noninvasive, and prediction of tumor recurrence after surgical resection. The reviewed articles evaluating use of radiomics are mostly limited due to small sample sizes and lack of a validation cohort. These studies show potential for radiomic features to improve pulmonary nodule classification and change the way patients are managed, however, comparison between studies is limited due to variabilities in the way these features are derived. To make these features useful will require further research and standardization of the workflows that derive these features.