Evaluation of a Real-time Interactive Pulmonary Nodule Analysis System on Chest Digital Radiographic Images

A Prospective Study

      Rationale and Objectives

      We sought to assess the performance of a real-time interactive pulmonary nodule analysis system for evaluation of chest digital radiographic (DR) images in a routine clinical environment.

      Materials and Methods

      A real-time interactive pulmonary nodule analysis system for chest DR image softcopy reading (IQQA-Chest; EDDA Technology, Princeton Junction, NJ) was used in daily practice with a Picture Archiving and Communication System in a National Cancer Institute−designated cancer teaching hospital. Patients referred for follow-up of known cancer underwent digital chest radiography. Posteroanterior and lateral DR images were first read by resident radiologists along with experienced chest radiologists using a Picture Archiving and Communication System work station. The computer-assisted detection (CAD) program was subsequently applied to the posteroanterior DR images, and changes (if any) in diagnosis were recorded. For reference standard, a follow-up chest radiograph at least 6 months following the initial examination or a follow-up computed tomographic scan of the chest within 3 months was used to establish diagnostic accuracy.


      Of 324 DR examinations, follow-up imaging according to our parameters was available for 214 patients (67%). Lung nodules were found and subsequently confirmed in 35 patients (10%) without CAD. Using CAD, nodules were found and subsequently confirmed in 51 patients (15%), improving sensitivity from 63.8% (95% confidence interval [CI], 0.49%−0.76%) to 92.7% (95% CI, 0.82%−0.98%) (P < .0001, McNemar). Nodules were subsequently proved to be malignant in five of the 16 additional cases (31%). False-positive readings increased from three to six cases; specificity decreased from 98.1% (95% CI, 0.95%−0.99%) to 96.2% (95% CI, 0.92%−0.98%) (not significant). There were 153 true-negative cases (71.4%).


      This study suggests that the interpretation of chest radiographs for lung nodules can be improved using an automated CAD nodule detection system. This improvement in reader performance comes with a minimal number of false-positive interpretations.

      Key Words

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        • Samei E.
        • Flynn M.J.
        • Peterson E.
        • Eyler W.R.
        Subtle lung nodules: Influence of local anatomic variations on detection.
        Radiology. 2003; 228: 76-84
        • Quekel L.G.
        • Kessels A.G.
        • Goei R.
        • van Engelshoven J.M.
        Miss rate of lung cancer on the chest radiograph in clinical practice.
        Chest. 1999; 115: 720-724
        • Shah P.K.
        • Austin J.H.
        • White C.S.
        • et al.
        Missed non–small cell lung cancer: Radiographic findings of potentially resectable lesions evident only in retrospect.
        Radiology. 2003; 226: 235-241
        • Swensen S.J.
        • Jett J.R.
        • Sloan J.A.
        • et al.
        Screening for lung cancer with low dose spiral computed tomography.
        Am J Respir Crit Care Med. 2002; 165: 508-513
        • Doi K.
        Current status and future potential of computer-aided diagnosis in medical imaging.
        Br J Radiol. 2005; 78: S3-S19
        • Li F.
        • Arimura H.
        • Suzuk K.
        • et al.
        Computer-aided detection of peripheral lung cancers missed at CT: ROC analyses without and with localization.
        Radiology. 2005; 237: 684-690
        • Abe H.
        • MacMahon H.
        • Engelmann R.
        • et al.
        Computer-aided diagnosis in chest radiography: Results of large-scale observer tests at the 1996–2001 RSNA scientific assemblies.
        Radiographics. 2003; 23: 255-265
        • Das M.
        • Muehlenbruch G.
        • Mahnken A.H.
        • et al.
        Small pulmonary nodules: Effect of two computer-aided detection systems on radiologist performance.
        Radiology. 2006; 241: 564-571
        • MacMahon H.
        • Engelmann R.
        • Behlen F.
        • et al.
        Computer aided diagnosis of pulmonary nodules: Results of a large scale observer test.
        Radiology. 1999; 213: 723-726
        • Kakeda S.
        • Moriya J.
        • Sato H.
        • et al.
        Improved detection of lung nodules on chest radiographs using a commercial computer-aided diagnosis system.
        AJR Am J Roentgenol. 2004; 182: 505-510
        • Kobayashi T.
        • Xu X.W.
        • MacMahon H.
        • Metz C.E.
        • Doi K.
        Effect of a computer-aided diagnosis scheme on radiologists' performance in detection of lung nodules on radiographs.
        Radiology. 1996; 199: 843-848
        • Song W.
        • Fan L.
        • Xie Y.
        • Qian J.Z.
        • Jin Z.
        A study of inter-observer variations of pulmonary nodule marking and characterizing on DR images.
        Proc SPIE Med Imaging. 2005; 5749: 272-280
        • Van Beek E.
        • Mullan B.
        • Stanford W.
        • Thompson B.
        Evaluation of a real-time interactive pulmonary nodule analysis system on chest digital radiographic images: A prospective study.
        in: Proceedings of the RSNA 92nd Scientific Assembly and Annual Meeting. RSNA, Chicago, IL2006: 570
        • Wu N.
        • Gamsu G.
        • Czum J.
        • et al.
        Detection of small pulmonary nodules using direct digital radiography and picture archiving and communication systems.
        J Thorac Imaging. 2006; 21: 27-31