Lung cancer screening with low-dose CT demonstrated a prevalence of asymptomatic cancers in 1.3% of a smoking population, including a high proportion of early tumor stages and a 20% (three of 15) rate of invasive procedures for benign lesions.

The aim of this study was to assess the in vivo measurement precision of a software tool for volumetric analysis of pulmonary nodules from two consecutive low-dose multi-row detector CT scans. A total of 151 pulmonary nodules (diameter 2.2-20.5 mm, mean diameter 7.4+/-4.5 mm) in ten subjects with pulmonary metastases were examined with low-dose four-detector-row CT (120 kVp, 20 mAs (effective), collimation 4x1 mm, normalized pitch 1.75, slice thickness 1.25 mm, reconstruction increment 0.8 mm; Somatom VolumeZoom, Siemens). Two consecutive low-dose scans covering the whole lung were performed within 10 min. Nodule volume was determined for all pulmonary nodules visually detected in both scans using the volumetry tool included in the Siemens LungCare software. The 95% limits of agreement between nodule volume measurements on different scans were calculated using the Bland and Altman method for assessing measurement agreement. Intra- and interobserver agreement of volume measurement were determined using repetitive measurements of 50 randomly selected nodules at the same scan by the same and different observers. Taking into account all 151 nodules, 95% limits of agreement were -20.4 to 21.9% (standard error 1.5%); they were -19.3 to 20.4% (standard error 1.7%) for 105 nodules <10 mm. Limits of agreement were -3.9 to 5.7% for intraobserver and -5.5 to 6.6% for interobserver agreement. Precision of in vivo volumetric analysis of nodules with an automatic volumetry software tool was sufficiently high to allow for detection of clinically relevant growth in small pulmonary nodules.

The aim of this study was to evaluate a computer-aided diagnosis (CAD) workstation with automatic detection of pulmonary nodules at low-dose spiral CT in a clinical setting for early detection of lung cancer. Eighty-eight consecutive spiral-CT examinations were reported by two radiologists in consensus. All examinations were reviewed using a CAD workstation with a self-developed algorithm for automatic detection of pulmonary nodules. The algorithm is designed to detect nodules with diameters of at least 5 mm. A total of 153 nodules were detected with at least one modality (radiologists in consensus, CAD, 85 nodules with diameter < 5 mm, 68 with diameter > or = 5 mm). The results of automatic nodule detection were compared to nodules detected with any modality as gold standard. Computer-aided diagnosis correctly identified 26 of 59 (38%) nodules with diameters > or = 5 mm detected by visual assessment by the radiologists; of these, CAD detected 44% (24 of 54) nodules without pleural contact. In addition, 12 nodules > or = 5 mm were detected which were not mentioned in the radiologist's report but represented real nodules. Sensitivity for detection of nodules > or = 5 mm was 85% (58 of 68) for radiologists and 38% (26 of 68) for CAD. There were 5.8+/-3.6 false-positive results of CAD per CT study. Computer-aided diagnosis improves detection of pulmonary nodules at spiral CT and is a valuable second opinion in a clinical setting for lung cancer screening despite of its still limited sensitivity.

The purpose of this study was to compare the accuracy of an automated volumetry software for phantom pulmonary nodules across various 16-slice multislice spiral CT (MSCT) scanners from different vendors. A lung phantom containing five different nodule categories (intraparenchymal, around a vessel, vessel attached, pleural, and attached to the pleura), with each category comprised of 7-9 nodules (total, n = 40) of varying sizes (diameter 3-10 mm; volume 6.62 mm(3)-525 mm(3)), was scanned with four different 16-slice MSCT scanners (Siemens, GE, Philips, Toshiba). Routine and low-dose chest protocols with thin and thick collimations were applied. The data from all scanners were used for further analysis using a dedicated prototype volumetry software. Absolute percentage volume errors (APE) were calculated and compared. The mean APE for all nodules was 8.4% (+/-7.7%) for data acquired with the 16-slice Siemens scanner, 14.3% (+/-11.1%) for the GE scanner, 9.7% (+/-9.6%) for the Philips scanner and 7.5% (+/-7.2%) for the Toshiba scanner, respectively. The lowest APEs were found within the diameter size range of 5-10 mm and volumes >66 mm(3). Nodule volumetry is accurate with a reasonable volume error in data from different scanner vendors. This may have an important impact for intraindividual follow-up studies.

Background -There are few published

Pulmonary nodules were detected reliably at CT with 50 mA and pitch of 2 or with 25 mA and a pitch of 1. However, further reduction of the dose to that used at chest radiography was associated with a significant decrease in the number of nodules 5 mm or smaller that were detected, possibly due to artifacts.

The purpose of this study was to assess the effectiveness of double reading to increase the sensitivity of lung nodule detection at standard-dose (SDCT) and low-dose multirow-detector CT (LDCT). SDCT (100 mAs effective tube current) and LDCT (20 mAs) of nine patients with pulmonary metastases were obtained within 5 min using four-row detector CT. Softcopy images reconstructed with 5-mm slice thickness were read by three radiologists independently. Images with 1.25-mm slice thickness served as the gold standard. Sensitivity was assessed for single readers and combinations. The effectiveness of double reading was expressed as the increase of sensitivity. Average sensitivity for detection of 390 nodules (size 3.9+/-3.2 mm) for single readers was 0.63 (SDCT) and 0.64 (LDCT). Double reading significantly increased sensitivity to 0.74 and 0.79, respectively. No significant difference between sensitivity at SDCT and LDCT was observed. The percentage of nodules detected by all three readers concordantly was 52% for SDCT and 47% for LDCT. Although double reading increased the detection rate of pulmonary nodules from 63% to 74-79%, a considerable proportion of nodules remained undetected. No difference between sensitivities at LDCT and SDCT for detection of small nodules was observed.

The aim was to reach consensus in imaging for staging and follow-up as well as for therapy response assessment in patients with gastrointestinal stromal tumours (GIST). The German GIST Imaging Working Group was formed by 9 radiologists engaged in assessing patients with GIST treated with targeted therapy. The following topics were discussed: indication and optimal acquisition techniques of computed tomography (CT), magnetic resonance imaging (MRI) and positron emission tomography (PET)/CT; tumour response assessment considering response criteria and measurement techniques on CT, MRI and PET/CT; result interpretation; staging interval and pitfalls. Contrast-enhanced CT is the standard method for GIST imaging. MRI is the method of choice in case of liver-specific questions or contraindications to CT. PET/CT should be used for early response assessment or inconclusive results on morphologic imaging. All imaging techniques should be standardized allowing a reliable response assessment. Response has to be assessed with respect to lesion size, lesion density and appearance of new lesions. A critical issue is pseudoprogression due to myxoid degeneration or intratumoural haemorrhage. The management of patients with GIST receiving a targeted therapy requires a standardized algorithm for imaging and an appropriate response assessment with respect to changes in lesion size and density.