Accuracy of automated volumetry of pulmonary nodules across different multislice CT scanners

Department of Diagnostic Radiology, RWTH Aachen University, Pauwelsstrasse 30, 52072 Aachen, Germany.
European Radiology (Impact Factor: 4.01). 09/2007; 17(8):1979-84. DOI: 10.1007/s00330-006-0562-1
Source: PubMed


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.

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Available from: Hans-Ulrich Kauczor
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    • "It has been well-known that nodule attachments make it difficult to accurately define boundaries in the case of juxtavascular and juxtapleural solid nodules (13), with segmentation failure rates ranging from 20% to 28%, according to a study by Kostis et al. (14). In addition, Das et al. (15) showed that the overall absolute percentage error of volume measurement was highest for juxtapleural nodules. Our result partly coincides with these previous studies (13-15) and it is noteworthy that vascular attachment would be critical in the volumetric analysis of GGNs, not only for solid nodules. "
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    ABSTRACT: To compare the segmentation capability of the 2 currently available commercial volumetry software programs with specific segmentation algorithms for pulmonary ground-glass nodules (GGNs) and to assess their measurement accuracy. In this study, 55 patients with 66 GGNs underwent unenhanced low-dose CT. GGN segmentation was performed by using 2 volumetry software programs (LungCARE, Siemens Healthcare; LungVCAR, GE Healthcare). Successful nodule segmentation was assessed visually and morphologic features of GGNs were evaluated to determine factors affecting segmentation by both types of software. In addition, the measurement accuracy of the software programs was investigated by using an anthropomorphic chest phantom containing simulated GGNs. The successful nodule segmentation rate was significantly higher in LungCARE (90.9%) than in LungVCAR (72.7%) (p = 0.012). Vascular attachment was a negatively influencing morphologic feature of nodule segmentation for both software programs. As for measurement accuracy, mean relative volume measurement errors in nodules ≥ 10 mm were 14.89% with LungCARE and 19.96% with LungVCAR. The mean relative attenuation measurement errors in nodules ≥ 10 mm were 3.03% with LungCARE and 5.12% with LungVCAR. LungCARE shows significantly higher segmentation success rates than LungVCAR. Measurement accuracy of volume and attenuation of GGNs is acceptable in GGNs ≥ 10 mm by both software programs.
    Full-text · Article · Jul 2013 · Korean journal of radiology: official journal of the Korean Radiological Society
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    • "This setting is somewhat different than the analysis of lung nodules in diagnostic settings, where thin slice CT has been used to compute volume doubling time [32]. The observed high average accuracy of 97% and low standard deviation of 4.8% are better than previously reported for small nodules [33] and consistent with the error expected solely due to acquisition [34]. "
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    ABSTRACT: . This study presents a semiautomated approach for volumetric analysis of lung tumors and evaluates the feasibility of using volumes as an alternative to line lengths as a basis for response evaluation criteria in solid tumors (RECIST). The overall goal for the implementation was to accurately, precisely, and efficiently enable the analyses of lesions in the lung under the guidance of an operator. Methods . An anthropomorphic phantom with embedded model masses and 71 time points in 10 clinical cases with advanced lung cancer was analyzed using a semi-automated workflow. The implementation was done using the Cognition Network Technology. Results . Analysis of the phantom showed an average accuracy of 97%. The analyses of the clinical cases showed both intra- and interreader variabilities of approximately 5% on average with an upper 95% confidence interval of 14% and 19%, respectively. Compared to line lengths, the use of volumes clearly shows enhanced sensitivity with respect to determining response to therapy. Conclusions . It is feasible to perform volumetric analysis efficiently with high accuracy and low variability, even in patients with late-stage cancer who have complex lesions.
    Full-text · Article · May 2011 · International Journal of Biomedical Imaging
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    • "With regard to radiation exposure, two studies that compared the measurement error between low-dose and standard-dose techniques showed different results (32, 39). While the bias error was significantly smaller for a 120 mAs scan than that for a 20 mAs scan in a study by Ko et al. (39), a study by Das et al. (32) showed no significant effect on the volumetric measurement error between the low-dose (20 mAs) and standard-dose (100 mAs) protocols. "
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    ABSTRACT: As the detection and characterization of lung nodules are of paramount importance in thoracic radiology, various tools for making a computer-aided diagnosis (CAD) have been developed to improve the diagnostic performance of radiologists in clinical practice. Numerous studies over the years have shown that the CAD system can effectively help readers identify more nodules. Moreover, nodule malignancy and the response of malignant lung tumors to treatment can also be assessed using nodule volumetry. CAD also has the potential to objectively analyze the morphology of nodules and enhance the workflow during the assessment of follow-up studies. Therefore, understanding the current status and limitations of CAD for evaluating lung nodules is essential to effectively apply CAD in clinical practice.
    Full-text · Article · Mar 2011 · Korean journal of radiology: official journal of the Korean Radiological Society
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