Purpose: Purpose of our study was to evaluate the added value of computer aided detection for lung nodules in chest radiographs when radiologists have bone suppressed images available. Methods: Written informed consent was waived by the IRB. Selection of study images and study setup was reviewed and approved by the institutional review boards. 300 posteroranterior (PA) and lateral chest radiographs, (189 negative radiographs and 111 patients with a solitary nodule) were selected from image archives in four institutions. PA images were processed by a commercially available computer aided detection (CAD) system (ClearRead +Detect 5.2, Riverain Technologies, Miamisburg, Ohio), and PA bone suppressed images (BSI) were generated (ClearRead BSI). Five radiologists and three residents evaluated the radiographs with BSI available first without CAD and secondly after inspection of the CAD marks. Readers marked suspicious locations and provided a confidence score for that location to be a nodule. Location based ROC analysis was performed using JAFROC analysis. Area under the curve (AUC) functioned as figure of merit and p-values were computed with the Dorfman-Berbaum-Metz method. Results: Average nodule size was 16 mm. CAD standalone reached a sensitivity of 74% at 1.0 false positive per image. Without CAD average AUC for observers was 0.812. With CAD performance significantly improved to an AUC of 0.841 (P = .0001). CAD detected 127 of 239 nodules that were missed after evaluation of the radiographs together with BSI pooled over all observers. Only 57 of these detections were eventually marked by the observers after review of CAD candidates. Conclusion: CAD improved radiologistsA-A?A 1/2 performance for the detection of lung nodules on chest radiographs, even when baseline performance was optimized by providing lateral and bone suppressed images. Still the majority of true positive CAD candidates is being dismissed by the observers.