Performance levels for computerized detection of nodules in different size and pattern groups on thin-slice CT

Abstract

We developed a computer-aided diagnostic (CAD) scheme for detection of lung nodules in CT, and investigated its performance levels for nodules in different size and pattern groups. Our database consisted of 117 thin-slice CT scans with 153 nodules. There were 68 (44.4%) small, 52 (34.0%) medium-sized, and 33 (21.6%) large nodules; 101 (66.0%) solid and 52 (34.0%) nodules with ground glass opacity (GGO) in the database. Our CAD scheme consisted of lung segmentation, selective nodule enhancement, initial nodule detection, accurate nodule segmentation, and feature extraction and analysis techniques. We employed a case-based four-fold cross-validation method to evaluate the performance levels of our CAD scheme. We detected 87% of nodules (small: 74%, medium-sized: 98%, large: 94%; solid: 85%, GGO: 90%) with 6.5 false positives per scan; 82% of nodules (small: 68%, medium-sized: 94%, large: 91%; solid: 78%, GGO: 89%) with 2.8 false positives per scan; and 77% of nodules (small: 63%, medium-sized: 90%, large: 89%; solid: 71%, GGO: 89%) with 1.5 false positives per scan. Our CAD scheme achieved a higher sensitivity for GGO nodules than for solid nodules, because most of small nodules were solid. In conclusion, our CAD scheme achieved a low false positive rate and a relatively high detection rate for nodules with a large variation in size and pattern.