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.

Volumetric growth assessment of pulmonary lesions is crucial to both lung cancer screening and oncological therapy monitoring. While several methods for small pulmonary nodules have previously been presented, the segmentation of larger tumors that appear frequently in oncological patients and are more likely to be complexly interconnected with lung morphology has not yet received much attention. We present a fast, automated segmentation method that is based on morphological processing and is suitable for both small and large lesions. In addition, the proposed approach addresses clinical challenges to volume assessment such as variations in imaging protocol or inspiration state by introducing a method of segmentation-based partial volume analysis (SPVA) that follows on the segmentation procedure. Accuracy and reproducibility studies were performed to evaluate the new algorithms. In vivo interobserver and interscan studies on low-dose data from eight clinical metastasis patients revealed that clinically significant volume change can be detected reliably and with negligible computation time by the presented methods. In addition, phantom studies were conducted. Based on the segmentation performed with the proposed method, the performance of the SPVA volumetry method was compared with the conventional technique on a phantom that was scanned with different dosages and reconstructed with varying parameters. Both systematic and absolute errors were shown to be reduced substantially by the SPVA method. The method was especially successful in accounting for slice thickness and reconstruction kernel variations, where the median error was more than halved in comparison to the conventional approach.

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 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.

This information provided by the Thoracic Imaging Section of the German Radiological Society is intended to give physicians recommendations on the use of thoracic imaging procedures in the context of the current COVID-19 pandemic. It represents the consensus of the authors based on the previous scientific knowledge and is intended to provide guidance for unified, structured CT reporting if COVID-19 pneumonia is suspected. The recommendations presented correspond to state of knowledge at the time of print and will be updated according to the results of ongoing and future scientific studies. Key Points: Citation Format

Small pulmonary nodules with diameters

The aim of this study was to compare numbers of pulmonary nodules detected with maximum intensity projections using a slab thickness of 15 mm (MIP 15) and 30 mm (MIP 30) with single image (SI) presentation of chest CT scans. Two readers reviewed MIP 15, MIP 30, and SI presentations of 10-mm (n = 8) and 5-mm collimation (n = 10) helical CT scans and recorded size, location, and diagnostic confidence (definite, probable) of pulmonary nodules. Readers 1 and 2 recorded more nodules with MIP 15 than with SI: 10-mm collimation, 77/64 and 60/56; 5-mm collimation, 64/60 and 40/36; and more "definite" nodules (10-mm collimation: 68/57 and 51/42; 5-mm collimation: 43/36 and 34/30). MIP 15 also detected more nodules than MIP 30 at 10-mm collimation: 77/72 and 60/50; with no major differences at 5-mm collimation: 64/66 and 40/38; and more "definite" nodules (10-mm collimation: 68/58 and 51/36; 5-mm collimation: 43/39 and 34/29). There were only minor differences between SI and MIP 30. Reading time and image number per study were reduced with MIP presentations by a factor of 1.4-5.3. There were no significant differences in the number of nodules detected with SI, MIP 15, and MIP 30, but MIP presentation reduced reporting time and filming cost when compared with SI reporting. For detection of nodules MIP 15 was slightly superior to MIP 30.