AUTOMATIC DETECTION OF PULMONARY NODULES IN MDCT: PERFORMANCE EVALUATION WITH INDEPENDENT DATASETS

The purpose of this study was to evaluate the performance of a computer-aided diagnosis (CAD) system on the detection of pulmonary nodules in multidetector row computed tomography (MDCT) images by using two independent datasets. We collected CT cases of 63 patients with 132 nodules ranging 4–30 mm in diameter from a hospital in Spain (20 patients) and a hospital in France (43 patients). CT examinations were acquired by using a SOMATOM Emotion CT scanner in Spain, and a dual-source SOMATOM Definition CT scanner in France (Siemens Medical System, Forchheim, Germany), with the following parameters: 6 × 1.0 mm collimation, 130 kVp, 70 mA (Emotion 6); or 64 × 0.6 mm collimation, 100–120 kVp, and 100–110 mAs (Definition 64). Nodules were detected independently by three experienced chest radiologists, and their detection results were used as the reference standard. The CAD scheme was developed with an advanced 3D iris filter for improving nodule detection. The performance of the CAD scheme was tested with an independent evaluation method based on the two databases. Free-response receiver operating characteristic curves, sensitivity and number of false-positive per scan, were employed to evaluate the performance of the CAD scheme. The study was approved by the Institutional Review Board. At an average false positive (FP) rate of 5 per scan, our CAD scheme achieved sensitivities of 79.5% for all nodules, 80.3% for solid, 60.0% for non-solid, 58.1% for spiculated, and 86.1% for non-spiculated nodules. In conclusion, our CAD scheme could be utilized to help radiologists in the detection of lung nodules in CT. However, in this study we confirmed that significant differences could be found in the performance of the system depending on the testing database.

keywords: CT, Pulmonary nodule, Computer-aided diagnosis, Independent datasets