• Steven Schalekamp, Bram van Ginneken, Emmeline Koedam, Miranda M Snoeren, Audrey M Tiehuis, Rianne Wittenberg, Nico Karssemeijer, and Cornelia M Schaefer-Prokop.
• From the Department of Radiology, Route 767, Radboud University Medical Center, Internal Postal Code 766, Postbus 9101, 6500 HB Nijmegen, the Netherlands (S.S., B.v.G., E.K., M.M.S., N.K., C.M.S.); and Department of Radiology, Meander Medical Center, Amersfoort, the Netherlands (A.M.T., R.W., C.M.S.).
• Radiology. 2014 Jul 1; 272 (1): 252-61.

PurposeTo evaluate the added value of computer-aided detection (CAD) for lung nodules on chest radiographs when radiologists have bone-suppressed images (BSIs) available.Materials And MethodsWritten informed consent was waived by the institutional review board. Selection of study images and study setup was reviewed and approved by the institutional review boards. Three hundred posteroanterior (PA) and lateral chest radiographs (189 radiographs with negative findings and 111 radiographs with a solitary nodule) in 300 subjects were selected from image archives at four institutions. PA images were processed by using a commercially available CAD, and PA BSIs were generated. Five radiologists and three residents evaluated the radiographs with BSIs available, first, without CAD and, second, after inspection of the CAD marks. Readers marked locations suspicious for a nodule and provided a confidence score for that location to be a nodule. Location-based receiver operating characteristic analysis was performed by using jackknife alternative free-response receiver operating characteristic analysis. Area under the curve (AUC) functioned as figure of merit, and P values were computed with the Dorfman-Berbaum-Metz method.ResultsAverage nodule size was 16.2 mm. Stand-alone CAD reached a sensitivity of 74% at 1.0 false-positive mark 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 BSIs pooled over all observers. Only 57 of these detections were eventually marked by the observers after review of CAD candidates.ConclusionCAD improved radiologists' performance for the detection of lung nodules on chest radiographs, even when baseline performance was optimized by providing lateral radiographs and BSIs. Still, most of the true-positive CAD candidates are dismissed by observers.© RSNA, 2014.

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