Solid Pulmonary Nodules Research Articles

Evaluation of Individuals With Pulmonary Nodules: When Is It Lung Cancer?: Diagnosis and Management of Lung Cancer, 3rd ed: American College of Chest Physicians Evidence-Based Clinical Practice Guidelines

The objective of this article is to update previous evidence-based recommendations for evaluation and management of individuals with solid pulmonary nodules and to generate new recommendations for those with nonsolid nodules. We updated prior literature reviews, synthesized evidence, and formulated recommendations by using the methods described in the "Methodology for Development of Guidelines for Lung Cancer" in the American College of Chest Physicians Lung Cancer Guidelines, 3rd ed. We formulated recommendations for evaluating solid pulmonary nodules that measure > 8 mm in diameter, solid nodules that measure ≤ 8 mm in diameter, and subsolid nodules. The recommendations stress the value of assessing the probability of malignancy, the utility of imaging tests, the need to weigh the benefits and harms of different management strategies (nonsurgical biopsy, surgical resection, and surveillance with chest CT imaging), and the importance of eliciting patient preferences. Individuals with pulmonary nodules should be evaluated and managed by estimating the probability of malignancy, performing imaging tests to better characterize the lesions, evaluating the risks associated with various management alternatives, and eliciting their preferences for management.

Magnetic Resonance Imaging for Lung Cancer

Since the publication of the Radiologic Diagnostic Oncology Group Report in 1991, the clinical application of pulmonary magnetic resonance imaging (MRI) in patients with lung cancer has been limited. In contrast, MRI for lung cancer has undergone continuous development, and several promising techniques have been introduced to overcome the previously suggested limitations. In addition, comparative studies involving multidetector-row computed tomography and positron emission tomography or positron emission tomography/computed tomography with 2-deoxy-2-[F]fluoro-D-glucose have shown useful new clinical applications for MRI in lung cancer. Moreover, MRI can provide not only morphologic information based on various parameters such as T1 and T2 relaxation times, tissue diffusion, perfusion, etc. but also functional information; it also has a significant role in nuclear medicine studies. In this review article, we describe recent advances made in MRI with respect to lung cancer, focusing on (1) detection of solid pulmonary nodules; (2) characterization of solid pulmonary nodules; (3) TNM staging assessment using chest and whole-body MRI examinations; (4) prediction of postsurgical lung function; and (5) prediction of tumor treatment response. We believe that further basic studies, as well as studies on clinical applications of new MRI techniques, are important for improving the management of lung cancer patients.

The Effects of Computed Tomography with Iterative Reconstruction on Solid Pulmonary Nodule Volume Quantification

BackgroundThe objectives of this study were to evaluate the influence of iterative reconstruction (IR) on pulmonary nodule volumetry with chest computed tomography (CT).MethodsTwenty patients (12 women and 8 men, mean age 61.9, range 32–87) underwent evaluation of pulmonary nodules with a 64-slice CT-scanner. Data were reconstructed using filtered back projection (FBP) and IR (Philips Healthcare, iDose4-levels 2, 4 and 6) at similar radiation dose. Volumetric nodule measurements were performed with semi-automatic software on thin slice reconstructions. Only solid pulmonary nodules were measured, no additional selection criteria were used for the nature of nodules. For intra-observer and inter-observer variability, measurements were performed once by one observer and twice by another observer. Algorithms were compared using the concordance correlation-coefficient (pc) and Friedman-test, and post-hoc analysis with the Wilcoxon-signed ranks-test with Bonferroni-correction (significance-level p<0.017).ResultsSeventy-eight nodules were present including 56 small nodules (volume<200 mm3, diameter<8 mm) and 22 large nodules (volume≥200 mm3, diameter≥8 mm). No significant differences in measured pulmonary nodule volumes between FBP, iDose4-levels 2, 4 and 6 were found in both small nodules and large nodules. FBP and iDose4-levels 2, 4 and 6 were correlated with pc-values of 0.98 or higher for both small and large nodules. Pc-values of intra-observer and inter-observer variability were 0.98 or higher.ConclusionsMeasurements of solid pulmonary nodule volume measured with standard-FBP were comparable with IR, regardless of the IR-level and no significant differences between measured volumes of both small and large solid nodules were found.

Definition of a Positive Test Result in Computed Tomography Screening for Lung Cancer

Low-dose computed tomography screening for lung cancer can reduce mortality among high-risk persons, but "false-positive" findings may result in unnecessary evaluations with attendant risks. The effect of alternative thresholds for defining a positive result on the rates of positive results and cancer diagnoses is unknown. To assess the frequency of positive results and potential delays in diagnosis in the baseline round of screening by using more restrictive thresholds. Prospective cohort study. Multi-institutional International Early Lung Cancer Action Program. 21 136 participants with baseline computed tomography performed between 2006 and 2010. The frequency of solid and part-solid pulmonary nodules and the rate of lung cancer diagnosis by using current (5 mm) and more restrictive thresholds of nodule diameter. The frequency of positive results in the baseline round by using the current definition of positive result (any parenchymal, solid or part-solid, noncalcified nodule ≥5.0 mm) was 16% (3396/21 136). When alternative threshold values of 6.0, 7.0, 8.0 and 9.0 mm were used, the frequencies of positive results were 10.2% (95% CI, 9.8% to 10.6%), 7.1% (CI, 6.7% to 7.4%), 5.1% (CI, 4.8% to 5.4%), and 4.0% (CI, 3.7% to 4.2%), respectively. Use of these alternative definitions would have reduced the work-up by 36%, 56%, 68%, and 75%, respectively. Concomitantly, lung cancer diagnostics would have been delayed by at most 9 months for 0%, 5.0% (CI, 1.1% to 9.0%), 5.9% (CI, 1.7 to 10.1%), and 6.7% (CI, 2.2% to 11.2%) of the cases of cancer, respectively. This was a retrospective analysis and thus whether delays in diagnosis would have altered outcomes cannot be determined. These findings suggest that using a threshold of 7 or 8 mm to define positive results in the baseline round of computed tomography screening for lung cancer should be prospectively evaluated to determine whether the benefits of decreasing further work-up outweigh the consequent delay in diagnosis in some patients.

Measurement Methods and Algorithms for the Management of Solid Nodules

The increased detection of incidental small pulmonary nodules on multidetector computed tomography has driven attempts to refine the characterization and management of such nodules. A variety of methods have been developed to measure the size and biological activity of nodules to help define their nature, but these have limitations. Several clinical trials have assessed the efficacy of low-dose computed tomography screening for lung cancer and offer some insights into these limitations; however, they also provide evidence that refines existing nodule management strategies. This article reviews the size-based and functional measurement methods that can be used to predict the likelihood of malignancy in noncalcified solid pulmonary nodules and discusses their incorporation into existing algorithms for nodule management. The issue of multiple nodules and the optimum frequency and duration of follow-up are explored.

Improved Efficiency of CT Interpretation Using an Automated Lung Nodule Matching Program

The purpose of this study was to assess the impact of an automated program on improvement in lung nodule matching efficiency. Four thoracic radiologists independently reviewed two serial chest CT examinations from each of 57 patients. Each radiologist performed timed manual lung nodule matching. After 6 weeks, all radiologists independently repeated the timed matching portion using an automated nodule matching program. The time required for manual and automated matching was compared. The impact of nodule size and number on matching efficiency was determined. An average of 325 (range, 244-413) noncalcified solid pulmonary nodules was identified. Nodule matching was significantly faster with the automated program irrespective of the interpreting radiologist (p < 0.0001 for each). The maximal time saved with automated matching was 11.4 minutes (mean, 2.3 ± 2.0 minutes). Matching was faster in 56 of 57 cases (98.2%) for three readers and in 46 of 57 cases (80.7%) for one reader. There were no differences among readers with respect to the mean time saved per matched nodule (p > 0.5). The automated program achieved 90%, 90%, 79%, and 92% accuracy for the four readers. The improvement in efficiency for a given patient using the automated technique was proportional to the number of matched nodules (p < 0.0001) and inversely proportional to nodule size (p < 0.05). Use of the automated lung nodule matching program significantly improves diagnostic efficiency. The time saved is proportionate to the number of nodules identified and inversely proportional to nodule size. Adoption of such a program should expedite CT examination interpretation and improve report turnaround time.

18 F-fluoro-deoxy-glucose focal uptake in very small pulmonary nodules: fact or artifact? Case reports

Background18F-fluoro-deoxy-glucose (18 F-FDG) positron emission tomography integrated/combined with computed tomography (PET-CT) provides the best diagnostic results in the metabolic characterization of undetermined solid pulmonary nodules. The diagnostic performance of 18 F-FDG is similar for nodules measuring at least 1 cm and for larger masses, but few data exist for nodules smaller than 1 cm.Case presentationWe report five cases of oncologic patients showing focal lung 18 F-FDG uptake on PET-CT in nodules smaller than 1 cm. We also discuss the most common causes of 18 F-FDG false-positive and false-negative results in the pulmonary parenchyma.In patient 1, contrast-enhanced CT performed 10 days before PET-CT did not show any abnormality in the site of uptake; in patient 2, high-resolution CT performed 1 month after PET showed a bronchiole filled with dense material interpreted as a mucoid impaction; in patient 3, contrast-enhanced CT performed 15 days before PET-CT did not identify any nodules; in patients 4 and 5, contrast-enhanced CT revealed a nodule smaller than 1 cm which could not be characterized. The 18 F-FDG uptake at follow-up confirmed the malignant nature of pulmonary nodules smaller than 1 cm which were undetectable, misinterpreted, not recognized or undetermined at contrast-enhanced CT.ConclusionIn all five oncologic patients, 18 F-FDG was able to metabolically characterize as malignant those nodules smaller than 1 cm, underlining that: 18 F-FDG uptake is not only a function of tumor size but it is strongly related to the tumor biology; functional alterations may precede morphologic abnormalities. In the oncologic population, especially in higher-risk patients, PET can be performed even when the nodules are smaller than 1 cm, because it might give an earlier characterization and, sometimes, could guide in the identification of alterations missed on CT.

Gray-Scale Reversal for the Detection of Pulmonary Nodules on a PACS Workstation

The purpose of this article is to evaluate the impact of gray-scale reversal on the detection of small pulmonary nodules in two-view chest radiography. One hundred twenty-eight patients (mean age, 62 years) who underwent CT and chest radiography within 6 weeks were retrospectively selected for this study. Seventy-three percent of patients showed variable degrees of radiographic findings of a "dirty lung." A total of 129 solid pulmonary nodules were present in 74 patients (nodule diameter range, 5-30 mm; mean diameter, 13 mm). The remaining 54 patients served as negative control subjects. Six readers with varying experience levels evaluated the images without and with the availability of gray-scale reversal in two separate reading sessions. Figure of merit (FOM), sensitivity per lesion, mean number of false-positive marks per image, and accuracy were calculated. Five of the six readers showed a slight increase in sensitivity with the use of gray-scale reversal, but on average, the difference was not significant (48% vs 50%; p > 0.05). The mean number of false-positive marks per image also nonsignificantly increased from 0.20 to 0.23. The increases in both sensitivity and the mean number of false-positive marks per image translated into nonsignificant decreases in average FOM (0.79 vs 0.77) and accuracy (72% vs 71%). Data analysis of subgroups of nodules or different reader groups, depending on level of experience, did not reveal significant differences. Using PACS display of digital chest radiographs, gray-scale reversal does not help the radiologists in detecting pulmonary nodules.

Segmentation of Juxtapleural Pulmonary Nodules Using a Robust Surface Estimate

An algorithm was developed to segment solid pulmonary nodules attached to the chest wall in computed tomography scans. The pleural surface was estimated and used to segment the nodule from the chest wall. To estimate the surface, a robust approach was used to identify points that lie on the pleural surface but not on the nodule. A 3D surface was estimated from the identified surface points. The segmentation performance of the algorithm was evaluated on a database of 150 solid juxtapleural pulmonary nodules. Segmented images were rated on a scale of 1 to 4 based on visual inspection, with 3 and 4 considered acceptable. This algorithm offers a large improvement in the success rate of juxtapleural nodule segmentation, successfully segmenting 98.0% of nodules compared to 81.3% for a previously published plane-fitting algorithm, which will provide for the development of more robust automated nodule measurement methods.

Three-dimensional analysis of pulmonary nodules: variability of semiautomated volume measurements between different versions of the same software

This study was done to evaluate the variability of semiautomated volume measurements of solid pulmonary nodules between two different versions of the same volumetric software. The volumes of 100 solid intraparenchymal nodules (mean volume 88.10 mm(3); range 7.36-595.25 mm(3)) studied with the same multidetector computed tomography (MDCT) protocol were determined using two different versions of the same volumetric software (LungCARE 2006G and LungCARE 2007S). The 2006G version is based on a single-segmentation algorithm, whereas the newer version features two algorithms: SmallSizeNodule and AllSizeNodule. The results obtained with the 2006G version were compared with those of the 2007S version with the SmallSizeNodule algorithm, as recommended by the user manual. In addition, we compared the volumetric measurements obtained by the two different algorithms of the 2007S version. The 2006G version and the 2007S version with the SmallSizeNodule algorithm agreed in only two of 100 cases and showed a mean variability of 1.66% (range 0%-8.78%). A more significant volumetric discrepancy was observed between the two different algorithms of the 2007S version, with the AllSizeNodule algorithm providing on average larger volumes (mean variability 71.08%; range 6.02%-218.80%) than SmallSizeNodule. Volume discrepancies were more pronounced in the subgroups of smaller nodules in all comparisons. There is variability also in the results provided by different versions of the same volumetric software, and this may affect the calculation of the nodule-doubling time. Computer-aided assessment of the growth of lung nodules should always be performed using the same version of volumetric software and the same segmentation algorithm.

Influence of software volumetric evaluation on management of solid pulmonary nodules in Nelson study

Influence of software volumetric evaluation on management of solid pulmonary nodules in Nelson study

Volumetric measurement of pulmonary nodules at low-dose chest CT: effect of reconstruction setting on measurement variability

ObjectiveTo assess volumetric measurement variability in pulmonary nodules detected at low-dose chest CT with three reconstruction settings.MethodsThe volume of 200 solid pulmonary nodules was measured three times using commercially available semi-automated software of low-dose chest CT data-sets reconstructed with 1 mm section thickness and a soft kernel (A), 2 mm and a soft kernel (B), and 2 mm and a sharp kernel (C), respectively. Repeatability coefficients of the three measurements within each setting were calculated by the Bland and Altman method. A three-level model was applied to test the impact of reconstruction setting on the measured volume.ResultsThe repeatability coefficients were 8.9, 22.5 and 37.5% for settings A, B and C. Three-level analysis showed that settings A and C yielded a 1.29 times higher estimate of nodule volume compared with setting B (P = 0.03). The significant interaction among setting, nodule location and morphology demonstrated that the effect of the reconstruction setting was different for different types of nodules. Low-dose CT reconstructed with 1 mm section thickness and a soft kernel provided the most repeatable volume measurement.ConclusionA wide, nodule-type-dependent range of agreement between volume measurements with different reconstruction settings suggests strict consistency is required for serial CT studies.

Role of baseline nodule density and changes in density and nodule features in the discrimination between benign and malignant solid indeterminate pulmonary nodules

Role of baseline nodule density and changes in density and nodule features in the discrimination between benign and malignant solid indeterminate pulmonary nodules

Imprecision in Automated Volume Measurements of Pulmonary Nodules and Its Effect on the Level of Uncertainty in Volume Doubling Time Estimation

Detection of small indeterminate pulmonary nodules (4 to 10 mm in diameter) in clinical practice is increasing, largely because of increased utilization and improved imaging technology. Although there currently exists software for CT scan machines that automate nodule volume estimation, the imprecision associated with volume estimates is particularly poor for nodules < or = 6 mm in diameter, with greater imprecision associated with increasing CT scan slice thickness. This study examined the effects of the volume estimation error associated with four CT scan slice thicknesses (0.625, 1.25, 2.50, and 5.00 mm) on estimates of volume doubling time (VDT) for solid nodules of various sizes. Data reflecting the accuracy of 1,624 automated volume estimations were obtained from experiments incorporating volume estimation software, performed on a commercially available lung phantom. These data informed mathematical simulations that were used to estimate imprecision around VDT estimates for hypothetical pairs of volume estimates for a given solid pulmonary nodule observed at different time points. The confidence intervals around the VDT estimates were extremely wide for 2.50- and 5.00-mm slice thicknesses, often encompassing values traditionally associated with both benignity and malignity for simulated 1- and 2-mm growths in diameter. Because of the inaccuracy in automated volume estimation, the confidence a clinician should have in estimating VDT should be highly dependent on the degree of observed growth and on the CT scan slice thickness. The performance of CT scanners with slice thicknesses of > or = 2.5 mm for assessing growth in pulmonary nodules is essentially inadequate for 1-mm changes in nodule diameter.

In Vivo Repeatability of Automated Volume Calculations of Small Pulmonary Nodules With CT

The objectives of our study were to evaluate the in vivo reproducibility of automated volume calculations of small lung nodules with both low-dose and standard-dose CT and to assess whether repeatability within each technique varies according to the diameter, site, or morphology of the nodule or to percentage of emphysema. Sixty-six subjects with 83 solid pulmonary nodules between 5 and 10 mm in diameter were enrolled in this prospective study. Four consecutive MDCT data sets, two low dose and two standard dose, were obtained for each nodule on separate breath-holds during the same session. The volume of each nodule was calculated by automated software. Repeatability was evaluated by Bland-Altman's approach and the coefficient of repeatability. Associations of the percentage of volume variation between two measurements with nodule diameter, emphysema percentage, nodule site, and nodule morphology were assessed by Spearman's correlation coefficient and the Kruskal-Wallis test. A p value of < 0.05 was considered statistically significant. The range of variation of the volumes of pulmonary nodules between two subsequent measurements was -38% +/- 60% for low-dose CT and -27% +/- 40% for standard-dose CT. No significant statistical association was found between variation in volume measurements and nodule site, nodule diameter, nodule morphology, or emphysema percentage by semiautomated calculation of lung density. Automated volume calculations of small pulmonary nodules can significantly differ between two subsequent breath-holds with both low-dose and standard-dose CT techniques; in clinical practice we recommend that a volume variation of greater than 30% for nodules between 5 and 10 mm should be confirmed by follow-up CT to be sure that a nodule is actually growing.

Diagnostic value of computer-aided diagnosis with three-dimensional volumetric measurement in solid pulmonary nodules on follow-up CT

Objective To evaluate the diagnostic value of computer-aided diagnosis with three-dimensional volumetric measurement in solid pulmonary nodules on follow-up CT. Methods Forty-six patients with 58 solid nodules were evaluated.Each patient underwent chest CT at least twice.The volume, doubling times(DTs) and volumetric growth rates(VGRs) of nodules were calculated with three-dimensional volumetric measurement software. If there were significant statistical differences in DTs between malignant and benign nodules, Nonparametric test was used to make the analysis.If there were significant statistical differences in upper DTs'values between malignant and benign nodules, X2 test was used to make the analysis.Final diagnosis of all nodule were confirmed by pathology or by response to diagnostic therapy or by clinical follow-up.Results The DTs of 13 malignant nodules were 400 days in 22 nodules, with a range from 302 days to 55 158 days (median, 2 704 days); the VGRs ranged from 0 to 24% (median, 6.5%. Twenty-one nodules that decreased in size had the VGRs ranging from -38% to -4% (median, - 16.0%), and the DTs of these nodules could not be calculated by software.There were significant statistical differences in DTs between malignant and benign nodules(P <0.05).Taking 500 days, 400 days and 300 days as the upper value for malignancies there were statistically significant differences in these values between malignant and benign nodules(P < 0.01).Their sensitivities were 100.0%, 92.3%, 76.9% respectively and specificities were 87.5%, 91.7%, 100.0% respectively.Accuracies were 91.9%, 94.6% ,91.9% respectively and positive predictive values were 8,11,0.Conclusions Taking 400 days as the upper DTs'value for malignancies has important diagnostic value. Computer-aided diagnosis with three-dimensional volumetric measurement is sensitive to the volume changes of solid pulmonary nodules on follow-up CT, and is useful in the differential diagnosis of pulmonary nodules. Key words: Coin lesion,pulmonary; Lung neoplasm; Tomography, X-ray computed; Imageprocessing, computer-assisted

Linear and Volume Measurements of Pulmonary Nodules at Different CT Dose Levels – Intrascan and Interscan Analysis

To compare the interobserver variability of the unidimensional diameter and volume measurements of pulmonary nodules in an intrascan and interscan analysis using semi-automated segmentation software on ultra-low-dose computed tomography (ULD-CT) and standard dose CT (SD-CT) data. In 33 patients with pulmonary nodules, two chest multi-slice CT (MSCT) datasets (1 mm slice thickness; 20 % reconstruction overlap) had been consecutively acquired with an ultra-low dose (120 kV, 5 mAs) and standard dose technique (120 kV, 75 mAs). MSCT data was retrospectively analyzed using the segmentation software OncoTREAT (MeVis, Bremen, Germany, version 1.3). The volume of 229 solid pulmonary nodules included in the analysis as well as the largest diameter according to RECIST (Response Evaluation Criteria for Solid Tumors) were measured by two radiologists. Interobserver variability was calculated and SD-CT and ULD-CT data compared in an intrascan and interscan analysis. The median nodule diameter (n = 229 nodules) was registered with 8.2 mm (range: 2.8 to 43.6 mm, mean: 10.8 mm). The nodule volume ranged between 0.01 and 49.1 ml (median 0.1 ml, mean 1.5 ml). With respect to interobserver variability, the intrascan analysis did not reveal statistically significant differences (p > 0.05) between ULD-CT and SD-CT with broader limits of agreement for relative differences of RECIST measurements (-31.0 % + 27.0 % mean -2.0 % for SD-CT; -27.0 % + 38.6 %, mean 5.8 % for ULD-CT) than for volume measurements (-9.4 %, 8.0 %, mean 0.7 % for SD-CT; -13 %, 13 %, mean 0.0 % for ULD-CT). The interscan analysis showed broadened 95 % confidence intervals for volume measurements (-26.5 % 29.1 % mean 1.3 %, and -25.2 %, 29.6 %, mean 2.2 %) but yielded comparable limits of agreement for RECIST measurements. The variability of nodule volumetry assessed by semi-automated segmentation software as well as nodule size determination by RECIST appears to be independent of the acquisition dose in the CT source dataset. This is particularly important regarding size determination of pulmonary nodules in screening trials using low-dose CT data for follow-up imaging.

Performance evaluation of a computer-aided detection algorithm for solid pulmonary nodules in low-dose and standard-dose MDCT chest examinations and its influence on radiologists

The aim of the study was to evaluate the performance of a computer-aided detection (CAD) algorithm in low-dose and full-dose multidetector-row CT (MDCT) of the thorax and its impact on radiologists' performance. Chest CT examinations of 77 patients were evaluated retrospectively for pulmonary nodules. All patients had undergone a 16-slice MDCT chest examination with a standard acquisition protocol. Artificial image noise was added to the raw data to simulate image acquisition at 10 mAs(eff.) The data were transferred to dedicated lung analysis software (LungCare) with a prototype CAD algorithm (LungCAD). CAD was applied to both dose settings. Images were read by a radiologist and a first-year resident with and without the software at both dose settings. All images were reviewed in consensus by the two radiologists to set the reference standard. Sensitivity results with respect to the reference standard were compared. No statistically significant differences in the detection rate for all pulmonary nodules could be found between low-dose and full-dose settings for the CAD software alone (p = 0.0065). Both radiologists displayed a statistically significant increase in sensitivity with the use of CAD (p<0.0001). In conclusion, CAD is beneficial in both low-dose and standard-dose settings. This may be beneficial in reducing false-negative diagnosis in lung cancer screening, standard chest examinations and the search for metastases.

Variability of Semiautomated Lung Nodule Volumetry on Ultralow-Dose CT: Comparison with Nodule Volumetry on Standard-Dose CT

The study investigates the effect of a substantial dose reduction on the variability of lung nodule volume measurements by assessing and comparing nodule volumes using a dedicated semiautomated segmentation software on ultralow-dose computed tomography (ULD-CT) and standard-dose computed tomography (SD-CT) data. In 20 patients, thin-slice chest CT datasets (1 mm slice thickness; 20% reconstruction overlap) were acquired at ultralow-dose (120 kV, 5 mAs) and at standard-dose (120 kV, 75 mAs), respectively, and analyzed using the segmentation software OncoTREAT (MeVis, Bremen, Germany; version 1.3). Interobserver variability of volume measurements of 202 solid pulmonary nodules (mean diameter 11 mm, range 3.2-44.5 mm) was calculated for SD-CT and ULD-CT. With respect to interobserver variability, the 95% confidence interval for the relative differences in nodule volume in the intrascan analysis was measured with -9.7% to 8.3% (mean difference -0.7%) for SD-CT and with -12.6% to 12.4% (mean difference -0.2%) for ULD-CT. In the interscan analysis, the 95% confidence intervals for the differences in nodule volume ranged with -25.1% to -23.4% and 26.2% to 28.9% (mean difference 1.4% to 2.1%) dependent on the combination of readers and scans. Intrascan interobserver variability of volume measurements was comparable for ULD-CT and SD-CT data. The calculated variability of volume measurements in the interscan analysis was similar to the data reported in the literature for CT data acquired with equal radiation dose. Thus, the evaluated segmentation software provides nodule volumetry that appears to be independent of the dose level with which the CT source dataset is acquired.

Effect of Nodule Characteristics on Variability of Semiautomated Volume Measurements in Pulmonary Nodules Detected in a Lung Cancer Screening Program

To retrospectively assess volume measurement variability in solid pulmonary nodules (volume, 15-500 mm(3)) detected at lung cancer screening and to quantify the independent effects of nodule morphology, size, and location. This retrospective study was a substudy of the screening program that was approved by the Dutch Ministry of Health, and all participants provided written informed consent. Two independent readers used semiautomated software to measure the volume of pulmonary nodules detected in 6774 participants aged 50-75 years (5917 men). Nodules were classified according to their location (purely intraparenchymal, pleural based, juxtavascular, or fissure attached), morphology (smooth, polylobulated, spiculated, or irregular), and size (<or=50 mm(3) or >50 mm(3)). The level of agreement was expressed by using the absolute values of the relative volume differences (RVDs). Multivariate logistic regression analysis was performed, and odds ratios (ORs) were computed to quantify the independent effects of morphology, location, and size on RVD categories. Altogether, 4225 nodules in 2239 participants were included. Complete agreement in volume was obtained for 3646 (86%) of the nodules. Disagreement was small (absolute value of RVD < 5%) for 173 (4%) nodules, moderate (absolute value of RVD >or= 5% but < 15%) for 232 (6%), and large (absolute value of RVD >or= 15%) for 174 (4%). Multivariate analysis showed that the ORs of volume disagreement were 15.7, 3.1, and 1.9 for irregular, spiculated, and polylobulated nodules, respectively; 3.5, 2.6, and 2.1 for juxtavascular, pleural-based, and fissure-attached nodules, respectively; and 1.3 for large nodules compared with smooth, purely intraparenchymal, and small reference nodules. Nodule morphology, location, and size influence volume measurement variability, particularly for juxtavascular and irregular nodules.