Automated characterization of pulmonary nodules in thoracic CT images using a segmentation-based classification system

PURPOSE: Clinical guidelines for follow-up of pulmonary nodules depend on nodule type and therefore accurate characterization of nodules is important. A novel computer-aided diagnosis (CAD) system to distinguish solid, part-solid and non-solid nodules is presented and evaluated on a large data set from a lung cancer screening trial. METHOD AND MATERIALS: The automated characterization system is based on a previously published nodule segmentation algorithm. Four different parameter settings were used to extract the solid part, non-solid part and solid core of the lesion. For each segmentation, volume, mass, average density, 5th percentile and 95th percentile of densities inside the segmentation were used as features. A k-nearest-neighbor classifier was used to classify nodules. The accuracy of the system to differentiate between solid and subsolid nodules, between solid, part-solid and non-solid nodules and between part-solid and non-solid nodules was evaluated. A data set consisting of 137 low-dose chest CT scans (16x0.75mm, 120-140 kVp, 30 mAs) with 52 solid, 50 part-solid and 50 non-solid nodules was collected from a screening trial. The nodule type recorded in the screening database was used as the reference standard. Experiments were performed in leave-one-nodule-out cross-validation. RESULTS: The accuracy of CAD to differentiate between solid and subsolid nodules was 0.88. Differentiation into solid, part-solid and non-solid nodules gave an accuracy of 0.72. CAD had an accuracy of 0.71 in differentiating part-solid from non-solid nodules. CONCLUSION: Automated characterization of pulmonary nodules shows good performance. This can aid radiologists to decide on appropriate workup in clinical practice.