Evaluation of a real-time interactive pulmonary nodule analysis system on chest digital radiographic images: a prospective study.
OBJECTIVE
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.


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.


RESULTS
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%).


CONCLUSIONS
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.
van Beek
Edwin J R
EJ
Department of Radiology, Carver College of Medicine, University of Iowa, C751 GH, 200 Hawkins Drive, Iowa City, IA 52242-1077, USA. edwin-vanbeek@uiowa.edu
Mullan
Brian
B
Thompson
Brad
B
D016009:Chi-Square Distribution; D003936:Diagnosis, Computer-Assisted / Q000295:instrumentation*; D006801:Humans; D008175:Lung Neoplasms / Q000000981:diagnostic imaging* / Q000556:secondary; D011237:Predictive Value of Tests; D011446:Prospective Studies; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000295:instrumentation*; D013902:Radiography, Thoracic; D011873:Radiology Information Systems; D012680:Sensitivity and Specificity; D012984:Software; D003074:Solitary Pulmonary Nodule / Q000000981:diagnostic imaging* / Q000556:secondary; D014584:User-Computer Interface