Computer-aided detection of acute pulmonary embolism with 64-slice multi-detector row computed tomography: impact of the scanning conditions and overall image quality in the detection of peripheral clots

Abstract

Purpose: To evaluate the performance of a computer-aided detection (CAD) system for diagnosing peripheral acute pulmonary embolism (PE) with a 64-slice multi-detector row computed tomography (CT).

Materials and methods: Two radiologists investigated the accuracy of a software aimed at detecting peripheral clots (PECAD prototype, version 7; Siemens Medical Systems, Forchheim, Germany) by applying this tool for the analysis of the pulmonary arterial bed of 74 CT angiograms obtained with 64-slice dual-source CT (Definition; Siemens Medical Systems). These cases were retrospectively selected from a database of CT studies performed on the same CT unit, with a similar collimation (64 x 0.6 mm) and similar injection protocols. Patient selection was based on a variety of (1) scanning conditions, namely, nongated (n = 30), electrocardiography-gated (n = 30), and dual-energy CT angiograms (n = 14), and (2) image quality (IQ), namely, scans of excellent IQ (n = 53) and lower IQ due to lower levels of arterial enhancement and/or presence of noise (n = 21). The standard of truth was based on the 2 radiologists' consensus reading and the results of CAD.

Results: The software detected 80 of 93 peripheral clots present in the 21 patients (42 segmental and 38 subsegmental clots). The overall sensitivity (95% confidence interval) of the CAD tool was 86% (77%-92%) for detecting peripheral clots, 78% (64.5%-88%) at the segmental level and 97% (85.5%-99.9%) at the subsegmental level. Assuming normal vascular anatomy with 20 segmental and 40 subsegmental arteries, overall specificity and positive and negative predictive values (95% confidence interval) of the software were 91.8% (91%-92.6%), 18.4% (15%-22.4%), and 99.7% (99.5%-99.8%), respectively. A mean of 5.4 false positives was found per patient (total, 354 false positives), mainly linked to the presence of perivascular connective tissue (n = 119; 34%) and perivascular airspace consolidation (n = 97; 27%). The sensitivities (95% confidence interval) for the CAD tool were 91% (69.8%-99.3%) for dual-energy, 87% (59.3%-93.2%) for electrocardiography-gated, and 87% (73.5%-95.3%) for nongated scans (P > 0.05). No significant difference was found in the sensitivity of the CAD software when comparing the scans according to the scanning conditions and image quality.

Conclusions: The evaluated CAD software has a good sensitivity in detecting peripheral PE, which is not influenced by the scanning conditions or the overall image quality.