"Computer Aided Detection shows added value to Bone Suppression Imaging for the detection of lung nodules in chest radiographs"

S. Schalekamp, B. van Ginneken, M. Brink, B. Heggelman, M. Spee, I. Somers, N. Karssemeijer and C. Schaefer-Prokop

in: WCTI, 2013

Abstract

PURPOSE: To assess the added value of computer aided detection to bone suppression imaging on observer performance in detecting lung nodules in chest radiographs. MATERIALS AND METHODS: Posteroanterior (PA) and lateral digital chest radiographs of 111 (average age 65; m:f 66:45) patients with a CT proven solitary nodule (median diameter 15mm), and 189 (average age 63; m:f 111:78) controls were read by 6 radiologists and 6 residents. Institutional review board approval was obtained. Observers read the PA and lateral chest radiographs without and with Computer Aided Detection (CAD) (ClearRead +Detect 5.2, Riverain Technologies, Miamisburg, Ohio) within one reading session, and provided locations with scores of suspicion for the presence of a nodule. CAD marks were displayed as lesion contours and accompanied by a likelihood of suspiciousness. Bone suppressed images (BSI) (ClearRead Bone Suppresion 2.4, Riverain Technologies, Miamisburg, Ohio) were available at all time. Multi reader multi case (MRMC) localization receiver operating characteristics (AFROC) were used for statistical analysis. Besides, reader scores and CAD scores were independently combined, only using the reader mark locations. Significance of difference was set at P < 0.05. RESULTS: Sensitivity of the CAD system was 74% at 1.0 FP/image. LROC analysis showed improved detection with use of BSI plus CAD compared to chest radiographs with BSI (AUC = 0.848 versus 0.858; p= 0.02). Operating at a specificity of 90%, sensitivity increased with CAD from 66% to 69% (p=0.005). An independent combination of reader with CAD showed an AUC of 0.857 and a sensitivity of 70% at a specificity of 90%. CAD detected 148 of the 313 nodules initially missed by the observers. CONCLUSION: Computer aided detection showed added value to bone suppressed images for the detection of lung nodules in chest radiographs. CAD was able to detect 47% of the nodules missed by the observers. Independent combination of reader with CAD showed similar performance as when CAD was used as a second reader.