Automated Detection of Lung Nodules in Multidetector CT: Influence of Different Reconstruction Protocols on Performance of a Software Prototype

Purpose: To evaluate the accuracy of software for computer-aided detection (CAD) of lung nodules using different reconstruction slice thickness protocols in multidetector CT. Materials and Methods: Raw image data sets for 15 patients who had undergone 16-row multidetector CT (MDCT) for known pulmonary nodules were reconstructed at a reconstruction thickness of 5.0, 2.0 and 1.0mm with a reconstruction increment of 1.5,1.0 and 0.5mm, respectively. The "Nodule Enhanced Viewing" (NEV) tool of LungCare for computer-aided detection of lung nodules was applied to the reconstructed images. The reconstructed images were also blinded and then evaluated by 2 radiologists (A and B). Data from the evaluating radiologists and CAD was then compared to an independent reference standard established using the consensus of 2 independent experienced chest radiologists. The eligible nodules were grouped according to their size (diameter > 10, 5-10, < 5 mm) for assessment. Statistical analysis was performed using the receiver operating characteristic (ROC) curve analysis, t-test and two-rater Cohen's Kappa co-efficient. Results: A total of 103 nodules were included in the reference standard by the consensus panel. The performance of CAD was marginally lower than that of readers at a 5.0-mm reconstruction thickness (AUC=0.522, 0.517 and 0.497 for A, B and CAD, respectively). In the case of 2.0-mm reconstruction slices, the performance of CAD was better than that of the readers (AUC = 0.524,0.524 and 0.614 for A, B and CAD, respectively). CAD was found to be significantly superior to radiologists in the case of 1.0-mm reconstruction slices (AUC=0.537, 0.531 and 0.675 for A, B and CAD, respectively). The sensitivity at a reconstruction thickness of 1.0mm was determined to be 66.99%, 68.93% and 80.58% for A, B and CAD, respectively. The time required for detection was shortest for CAD at reconstruction slices of 1.0 mm (mean t = 4 min). The performance of radiologists was greatly enhanced when using CAD: sensitivity 91.26% and 94.17% for CAD+A and CAD+B, respectively (AUC=0.889 and 0.917). CAD was most advantageous in the detection of nodules <10 mm. Conclusion: At a 1.0-mm reconstruction thickness, CAD's ability to detect nodules < 10 mm is superior to that of radiologists and its relatively short evaluation time makes it a viable second reader.