Improved computer-aided detection of pulmonary nodules by combining a solid and subsolid nodule CAD system

C. Jacobs, E.M. van Rikxoort, T. Twellmann, P. de Jong, C. Schaefer-Prokop, M. Prokop and B. van Ginneken

in: Annual Meeting of the Radiological Society of North America, 2012

Abstract

PURPOSE : Most nodule CAD systems perform poorly for detection of subsolid pulmonary nodules since they are primarily developed for detection of solid nodules. To overcome this problem, a combination of CAD systems for solid and subsolid nodule detection is proposed and evaluated on a database containing a large amount of subsolid and solid nodules. METHOD AND MATERIALS: Low-dose chest CT scans (16x0.75mm, 120-140 kVp, 30 mAs) were selected from the database of the Dutch-Belgian NELSON lung cancer screening trial in which either a subsolid nodule or a solid nodule with a diameter between 7mm and 30mm was present. In this way, 109 scans with 33 non-solid, 37 part-solid, and 52 large solid nodules meeting the criteria were included. In total, the scans contained 327 solid, 42 part-solid and 33 non-solid nodules. All chest CT scans were processed using a solid nodule CAD system and subsolid CAD system (Diagnostic Image Analysis Group, Nijmegen, The Netherlands, Fraunhofer MEVIS, Bremen, Germany). Both systems were trained on an independent dataset. FROC analysis was performed to evaluate the performance of the solid nodule CAD system, the subsolid CAD system and the combination of the both CAD systems. The CAD systems were combined based on operating points on the FROC curve. Since the prevalence of subsolid nodules in a screening population is approximately four times lower than of solid nodules, for each operating point of the solid nodule CAD system we selected an operating point of the subsolid CAD system with a four times lower false-positive rate. Subsequently, findings of both CAD systems are merged and this generates the output of the combined CAD. RESULTS: FROC analysis showed that at an average of four false positives per scan, the solid nodule CAD system reached a sensitivity of 59% and the subsolid nodule CAD system reached 27%. At this false positive level the combination of the two systems led to a sensitivity of 66%. At an average of 8 false positives per scan, the sensitivities were 69%, 30% and 76%, respectively. CONCLUSION: The combination of a solid nodule CAD system and subsolid CAD system substantially increases the sensitivity for detection of pulmonary nodules. CLINICAL RELEVANCE/APPLICATION: Given the high malignancy rates of subsolid nodules, it is important that computer-aided detection systems detect both solid and subsolid nodules.