Image Analysis of Small Pulmonary Nodules Identified by Computed Tomography

Abstract

Detection of small pulmonary nodules has markedly increased as computed tomography (CT) technology has advanced and interpretation evolved from viewing small CT images on film to magnified images on large, high-resolution computer monitors. Despite these advances, determining the etiology of a lung nodule short of major surgery remains problematic. Initial nodule size is a major criterion in evaluating the risk for malignancy, and the majority of CT detected nodules are <10 mm in diameter. Also, the likelihood that the nodule is a lung cancer increases with increasing age and smoking history, and such clinical information needs to be integrated into algorithms that guide the workup of such nodules. Baseline and annual repeat screening results are also very helpful in developing and assessing the usefulness of such algorithms. Based on CT morphology, subtypes of nodules have been identified; today nodules are routinely classified as being solid, part-solid, or nonsolid. It has been shown that part-solid nodules have a higher frequency of being malignant than solid or nonsolid ones. Other nodule characteristics such as spiculation are useful, although granulomas and fibrosis also have such features, so these characteristics have not been as useful as nodule-growth assessment. Depending on the aggressiveness of the lung cancer and the size of the nodule when it is initially seen, a follow-up CT scan 1-3 months after the first CT scan can identify those nodules with growth at a malignant rate. Software has been developed by all CT scanner manufacturers for such growth assessment, but the inherent variability of such assessments needs further development. Nodule-growth assessment based on 2-dimensional approaches is limited; therefore, software has been developed for the 3-dimensional assessment of growth. Different approaches for such growth assessment have been developed, either using automated computer segmentation techniques or hybrid methods that allow the radiologist to adjust such segmentation. There are, however, inherent reasons for variability in such measurements that need to be carefully considered, and this, together with continued technologic advances and integration of the relevant clinical information, will allow for individualization of the algorithms for the workup of small pulmonary nodules.