Computer-assisted lung nodule volumetry from multi-detector row CT: Influence of image reconstruction parameters

Factors influencing the outcome of volumetry tools for pulmonary nodule analysis: a systematic review and attempted meta-analysis

Health systems worldwide are implementing lung cancer screening programmes to identify early-stage lung cancer and maximise patient survival. Volumetry is recommended for follow-up of pulmonary nodules and outperforms other measurement methods. However, volumetry is known to be influenced by multiple factors. The objectives of this systematic review (PROSPERO CRD42022370233) are to summarise the current knowledge regarding factors that influence volumetry tools used in the analysis of pulmonary nodules, assess for significant clinical impact, identify gaps in current knowledge and suggest future research. Five databases (Medline, Scopus, Journals@Ovid, Embase and Emcare) were searched on the 21st of September, 2022, and 137 original research studies were included, explicitly testing the potential impact of influencing factors on the outcome of volumetry tools. The summary of these studies is tabulated, and a narrative review is provided. A subset of studies (n = 16) reporting clinical significance were selected, and their results were combined, if appropriate, using meta-analysis. Factors with clinical significance include the segmentation algorithm, quality of the segmentation, slice thickness, the level of inspiration for solid nodules, and the reconstruction algorithm and kernel in subsolid nodules. Although there is a large body of evidence in this field, it is unclear how to apply the results from these studies in clinical practice as most studies do not test for clinical relevance. The meta-analysis did not improve our understanding due to the small number and heterogeneity of studies testing for clinical significance. CRITICAL RELEVANCE STATEMENT: Many studies have investigated the influencing factors of pulmonary nodule volumetry, but only 11% of these questioned their clinical relevance in their management. The heterogeneity among these studies presents a challenge in consolidating results and clinical application of the evidence. KEY POINTS: • Factors influencing the volumetry of pulmonary nodules have been extensively investigated. • Just 11% of studies test clinical significance (wrongly diagnosing growth). • Nodule size interacts with most other influencing factors (especially for smaller nodules). • Heterogeneity among studies makes comparison and consolidation of results challenging. • Future research should focus on clinical applicability, screening, and updated technology.

Lung nodules: size still matters

The incidence of indeterminate pulmonary nodules has risen constantly over the past few years. Determination of lung nodule malignancy is pivotal, because the early diagnosis of lung cancer could lead to a definitive intervention. According to the current international guidelines, size and growth rate represent the main indicators to determine the nature of a pulmonary nodule. However, there are some limitations in evaluating and characterising nodules when only their dimensions are taken into account. There is no single method for measuring nodules, and intrinsic errors, which can determine variations in nodule measurement and in growth assessment, do exist when performing measurements either manually or with automated or semi-automated methods. When considering subsolid nodules the presence and size of a solid component is the major determinant of malignancy and nodule management, as reported in the latest guidelines. Nevertheless, other nodule morphological characteristics have been associated with an increased risk of malignancy. In addition, the clinical context should not be overlooked in determining the probability of malignancy. Predictive models have been proposed as a potential means to overcome the limitations of a sized-based assessment of the malignancy risk for indeterminate pulmonary nodules.

Volumetric analysis of lung nodules in computed tomography (CT): comparison of two different segmentation algorithm softwares and two different reconstruction filters on automated volume calculation

BACKGROUND

A precise detection of volume change allows for better estimating the biological behavior of the lung nodules. Postprocessing tools with automated detection, segmentation, and volumetric analysis of lung nodules may expedite radiological processes and give additional confidence to the radiologists.

PURPOSE

To compare two different postprocessing software algorithms (LMS Lung, Median Technologies; LungCARE®, Siemens) in CT volumetric measurement and to analyze the effect of soft (B30) and hard reconstruction filter (B70) on automated volume measurement.

MATERIAL AND METHODS

Between January 2010 and April 2010, 45 patients with a total of 113 pulmonary nodules were included. The CT exam was performed on a 64-row multidetector CT scanner (Somatom Sensation, Siemens, Erlangen, Germany) with the following parameters: collimation, 24x1.2 mm; pitch, 1.15; voltage, 120 kVp; reference tube current-time, 100 mAs. Automated volumetric measurement of each lung nodule was performed with the two different postprocessing algorithms based on two reconstruction filters (B30 and B70). The average relative volume measurement difference (VME%) and the limits of agreement between two methods were used for comparison.

RESULTS

At soft reconstruction filters the LMS system produced mean nodule volumes that were 34.1% (P < 0.0001) larger than those by LungCARE® system. The VME% was 42.2% with a limit of agreement between -53.9% and 138.4%.The volume measurement with soft filters (B30) was significantly larger than with hard filters (B70); 11.2% for LMS and 1.6% for LungCARE®, respectively (both with P < 0.05). LMS measured greater volumes with both filters, 13.6% for soft and 3.8% for hard filters, respectively (P < 0.01 and P > 0.05).

CONCLUSION

There is a substantial inter-software (LMS/LungCARE®) as well as intra-software variability (B30/B70) in lung nodule volume measurement; therefore, it is mandatory to use the same equipment with the same reconstruction filter for the follow-up of lung nodule volume.

Benefit of overlapping reconstruction for improving the quantitative assessment of CT lung nodule volume

RATIONALE AND OBJECTIVES

The aim of this study was to quantify the effect of overlapping reconstruction on the precision and accuracy of lung nodule volume estimates in a phantom computed tomographic (CT) study.

MATERIALS AND METHODS

An anthropomorphic phantom was used with a vasculature insert on which synthetic lung nodules were attached. Repeated scans of the phantom were acquired using a 64-slice CT scanner. Overlapping and contiguous reconstructions were performed for a range of CT imaging parameters (exposure, slice thickness, pitch, reconstruction kernel) and a range of nodule characteristics (size, density). Nodule volume was estimated with a previously developed matched-filter algorithm.

RESULTS

Absolute percentage bias across all nodule sizes (n = 2880) was significantly lower when overlapping reconstruction was used, with an absolute percentage bias of 6.6% (95% confidence interval [CI], 6.4-6.9), compared to 13.2% (95% CI, 12.7-13.8) for contiguous reconstruction. Overlapping reconstruction also showed a precision benefit, with a lower standard percentage error of 7.1% (95% CI, 6.9-7.2) compared with 15.3% (95% CI, 14.9-15.7) for contiguous reconstructions across all nodules. Both effects were more pronounced for the smaller, subcentimeter nodules.

CONCLUSIONS

These results support the use of overlapping reconstruction to improve the quantitative assessment of nodule size with CT imaging.

Computer-aided diagnosis systems for lung cancer: challenges and methodologies

This paper overviews one of the most important, interesting, and challenging problems in oncology, the problem of lung cancer diagnosis. Developing an effective computer-aided diagnosis (CAD) system for lung cancer is of great clinical importance and can increase the patient's chance of survival. For this reason, CAD systems for lung cancer have been investigated in a huge number of research studies. A typical CAD system for lung cancer diagnosis is composed of four main processing steps: segmentation of the lung fields, detection of nodules inside the lung fields, segmentation of the detected nodules, and diagnosis of the nodules as benign or malignant. This paper overviews the current state-of-the-art techniques that have been developed to implement each of these CAD processing steps. For each technique, various aspects of technical issues, implemented methodologies, training and testing databases, and validation methods, as well as achieved performances, are described. In addition, the paper addresses several challenges that researchers face in each implementation step and outlines the strengths and drawbacks of the existing approaches for lung cancer CAD systems.

Technical parameters and interpretive issues in screening computed tomography scans for lung cancer

Lung cancer screening computed tomographies (CTs) differ from traditional chest CT scans in that they are performed at very low radiation doses, which allow the detection of small nodules but which have a much higher noise content than would be acceptable in a diagnostic chest CT. The technical parameters require a great deal of attention on the part of the user, because inappropriate settings could result in either excess radiation dose to the large population of screened individuals or in low-quality images with impaired nodule detectability. Both situations undermine the main goal of the screening program, which is to detect lung nodules using as low a radiation dose as can reasonably be achieved. Once an image has been obtained, there are unique interpretive issues that must be addressed mainly because of the very high noise content of the images and the high prevalence of incidental findings in the chest unrelated to the sought-after pulmonary nodules.

Pulmonary Nodules: growth rate assessment in patients by using serial CT and three-dimensional volumetry

PURPOSE

To determine the precision of a three-dimensional (3D) method for measuring the growth rate of solid and subsolid nodules and its ability to detect abnormal growth rates.

MATERIALS AND METHODS

This study was approved by the Institutional Research Board and was HIPAA compliant. Informed consent was waived. The growth rates of 123 lung nodules in 59 patients who had undergone lung cancer screening computed tomography (CT) were measured by using a 3D semiautomated computer-assisted volume method. Clinical stability was established with long-term CT follow-up (mean, 6.4 years±1.9 [standard deviation]; range, 2.0-8.5 years). A mean of 4.1 CT examinations per patient±1.2 (range, two to seven CT examinations per patient) was analyzed during 2.4 years±0.5 after baseline CT. Nodule morphology, attenuation, and location were characterized. The analysis of standard deviation of growth rate in relation to time between scans yielded a normative model for detecting abnormal growth.

RESULTS

Growth rate precision increased with greater time between scans. Overall estimate for standard deviation of growth rate, on the basis of 939 growth rate determinations in clinically stable nodules, was 36.5% per year. Peripheral location (P=.01; 37.1% per year vs 25.6% per year) and adjacency to pleural surface (P=.05; 38.9% per year vs 34.0% per year) significantly increased standard deviation of growth rate. All eight malignant nodules had an abnormally high growth rate detected. By using 3D volumetry, growth rate-based diagnosis of malignancy was made at a mean of 183 days±158, compared with radiologic or clinical diagnosis at 344 days±284.

CONCLUSION

A normative model derived from the variability of growth rates of nodules that were stable for an average of 6.4 years may enable identification of lung cancer.

European and North American lung cancer screening experience and implications for pulmonary nodule management

The potential for low dose computed tomography (LDCT) to act as an effective tool in screening for lung cancer is currently the subject of several randomised control trials. It has recently been given prominence by interim results released by the North American National Lung Screening Trial (NLST). Several other trials assessing LDCT as a screening tool are currently underway in Europe, and are due to report their final results in the next few years. These include the NELSON, DLSCT, DANTE, ITALUNG, MILD and LUSI trials. Although slow to instigate a trial of its own, the UK Lung Screen (UKLS) trial will shortly commence. The knowledge gained from the newer trials has mostly reinforced and refined previous concepts that have formed the basis of existing nodule management guidelines. This article takes the opportunity to summarise the main aspects and initial results of the trials presently underway, assess the status of current collaborative efforts and the scope for future collaboration, and analyse observations from these studies that may usefully inform the management of the indeterminate pulmonary nodule. Key Points • Low dose CT screening for lung cancer is promising. • The effect of LDCT screening on mortality is still uncertain. • Several European randomised controlled trials for LDCT are underway. • The trials vary in methodology but most compare LDCT to no screening. • Preliminary results have reinforced existing nodule management concepts.

Comparison of semiautomated and manual measurements for simulated hypo- and hyper-attenuating hepatic tumors on MDCT: effect of slice thickness and reconstruction increment on their accuracy

RATIONALE AND OBJECTIVES

The aims of this study were to compare accuracy between semiautomated and manual measurements of the longest diameter and volume of simulated hepatic tumors in phantoms and to evaluate the effects of slice thickness (ST) and reconstruction increment (RI) on accuracy.

MATERIALS AND METHODS

Liver phantoms with 45 hypoattenuating and 45 hyperattenuating lesions of different sizes (diameter, 13.3-50.7 mm; volume, 0.4-54.0 mm(3)) and shapes (spherical or elliptical) were scanned using a 64-row multidetector computed tomographic scanner. Images were reconstructed with ST and RI settings of 0.75 and 0.7 mm, 1.0 and 0.7 mm, 1.5 and 1.0 mm, 3.0 and 2.0 mm, 3.0 and 3.0 mm, and 5.0 and 5.0 mm. The longest diameter and volume of each lesion were measured both manually and semiautomatically. To assess accuracy, measurements were compared to reference values by calculating absolute percentage error. Comparisons of absolute percentage error between methods and between ST and RI settings were performed using paired t tests. The degree of correlation between each measurement and a reference value was also assessed.

RESULTS

The semiautomated method showed significantly higher accuracy than the manual method in volume for most ST and RI settings (0.75 and 0.7 mm, 1.0 and 0.7 mm, and 1.5 and 1.0 mm in hypoattenuating lesions and all settings in hyperattenuating lesions; P < .05) and showed similar accuracy in diameter for all ST and RI settings regardless of lesion attenuation (P > .05). Semiautomated measurements also demonstrated higher correlation with reference values than the manual method for both diameter and volume. The absolute percentage error tended to be increased as ST and RI increased for both methods, and acceptable maximum ST and RI in semiautomated method were 1.5 and 1.0 mm.

CONCLUSIONS

Semiautomated computed tomographic measurement showed higher accuracy and correlation than the manual method in measuring the diameter and volume of hepatic lesions. The accuracy of both methods was highly dependent on z-axis resolution.

A computer-aided diagnosis for evaluating lung nodules on chest CT: the current status and perspective

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.

Interobserver and intraobserver variability in the response evaluation of cancer therapy according to RECIST and WHO-criteria

BACKGROUND

Response Evaluation Criteria In Solid Tumors (RECIST) and WHO-criteria are used to evaluate treatment effects in clinical trials. The purpose of this study was to examine interobserver and intraobserver variations in radiological response assessment using these criteria.

MATERIAL AND METHODS

Thirty-nine patients were eligible. Each patient's series of CT images were reviewed. Each patient was classified into one of four categories according RECIST and WHO-criteria. To examine interobserver variation, response classifications were independently obtained by two radiologists. One radiologist repeated the procedure on two additional different occasions to examine intraobserver variation. Kappa statistics was applied to examine agreement.

RESULTS

Interobserver variation using RECIST and WHO-criteria were 0.53 (95% CI 0.33-0.72) and 0.60 (0.39-0.80), respectively. Response rates (RR) according to RECIST obtained by reader A and reader B were 33% and 21%, respectively. RR according to WHO-criteria obtained by reader A and reader B were 33% and 23% respectively. Intraobserver variation using RECIST and WHO-criteria ranged between 0.76-0.96 and 0.86-0.91, respectively.

CONCLUSION

Radiological tumor response evaluation according to RECIST and WHO-criteria are subject to considerable inter- and intraobserver variability. Efforts are necessary to reduce inconsistencies from current response evaluation criteria.

Morphological analysis of pancreatic cystic masses

RATIONALE AND OBJECTIVES

The aim of this study was to analyze the morphology of pancreatic cystic masses detected on multi-detector row computed tomography (MDCT) to determine whether single-dimension measurements of these masses are accurate reflections of their volumes.

MATERIALS AND METHODS

Twenty-five pancreatic cystic masses detected on MDCT in 25 patients were evaluated. Pancreatic cysts were segmented on MDCT using commercially available software. All measurements were obtained twice by two independent investigators, and the means of values for segmented cyst volume (Vs) (milliliters), maximum transaxial diameter (millimeters), and elongation value (defined as 1 - [width/length], where 1 = ellipsoid and 0 = spherical) were reported for each cystic lesion. The volume of each cyst was also calculated (Vc) using the maximum transaxial diameter, with the hypothesis that the cyst was spherical. Student's t test was used to analyze the differences between values of Vs and Vc. Bland-Altman plots and Lin's concordance correlation were used to assess agreement between different measurement techniques. A P value < .05 denoted statistical significance. Interobserver variability was also determined using the Bland-Altman method.

RESULTS

There was a significant difference between Vs and Vc (P < .0001). The elongation values ranged from 0.28 to 0.83 (mean, 0.57 +/- 0.12; median, 0.56). Mean interobserver variability was 1.7% (95% confidence interval, -4.89% to 8.30%).

CONCLUSIONS

The results suggest that pancreatic cystic masses are not spherical. Therefore, a cyst's single largest transaxial dimension is not an accurate surrogate of its actual volume.

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

PURPOSE

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.

MATERIALS AND METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

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

OBJECTIVE

To assess volumetric measurement variability in pulmonary nodules detected at low-dose chest CT with three reconstruction settings.

METHODS

The 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.

RESULTS

The 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.

CONCLUSION

A wide, nodule-type-dependent range of agreement between volume measurements with different reconstruction settings suggests strict consistency is required for serial CT studies.

Influence of pixel size on quantification of airway wall thickness in computed tomography

OBJECTIVES

The purpose of this study was to determine the point where a further decrease in voxel size does not result in better automatic quantification of the bronchial wall thickness by using 2 different assessment techniques.

MATERIALS AND METHODS

The results from the commonly used full-width-at-half-maximum (FWHM) principle and a new technique (integral-based method [IBM]) were compared for thin-section multidetector computed tomography (MDCT) data sets from an airway phantom containing 10 different tubular airway phantoms and in a human subsegmental bronchus in vivo. Correlation with the actual wall thickness and comparison of the wall thicknesses assessed for different voxel sizes were performed, and the image resolutions were also compared subjectively.

RESULTS

The relative error ranged from 0% (biggest phantom) to 330% (smallest phantom, biggest field of view, smaller matrix, and FWHM). Using IBM, the maximum relative error was 10% in the same setting. For FWHM, the improvement was marginal for most settings with a pixel spacing less than 0.195 x 0.195 x 0.8 mm; however, it still decreases the relative error from 290% to 273.6% for a wall thickness of 0.3 mm and a pixel spacing of 0.076 x 0.076 x 0.8 mm.

CONCLUSIONS

(1) Using a special technique such as IBM to account for computed tomography's blurring effect in assessing airway wall thickness had the greatest impact on correct quantification. (2) The visual impression and the automatic quantification using the FWHM technique improved marginally by decreasing the voxel size to less than 0.195 x 0.195 x 0.8 mm. (3) The FWHM technique as a model for visual quantification is not reliable for airway wall thicknesses less than 1.5 mm.

Incidental lung nodules on CT examinations of the abdomen: prevalence and reporting rates in the PACS era

OBJECTIVES

To retrospectively evaluate prevalence, reporting rates and clinical implications of incidental pulmonary nodules detected in multidetector computed tomography (MDCT) abdominal studies.

MATERIALS AND METHODS

Abdominal MDCT studies of 243 consecutive patients, 94 of whom had a history of cancer, were evaluated. Lung bases included in the scan were reviewed on a PACS workstation with different window settings and post-processing techniques. Nodules were classified according to their density (calcified, solid noncalcified, non-solid, part-solid) and size (<4mm; 4-6mm; 6-8mm; >8mm). The study findings were compared with the corresponding radiologic reports. Previous of following CT studies, when available from the PACS, were also reviewed to evaluate changes in number and size of the detected nodules.

RESULTS

An average of 8.2 cm of lung parenchyma was imaged in each patient. 213 noncalcified nodules (NCNs) were identified in 95 patients (39.1%) but only 8 patients (8.4%) had it mentioned in the final report. Comparison CT studies were available for 44 out of the 95 positive patients showing disappearance of the nodules in 2 cases, no interval change in 26 and progression in size and/or number in 16 patients, in whom a final diagnosis of metastasis or primary lung cancers was achieved.

CONCLUSION

Radiologists tend to overlook lung portions on abdominal CT studies. Underreporting may affect patient care and have medico-legal implications since images are permanently stored in digital format on PACS and CD-ROMs. Management of the discovered nodules should be tailored to the clinical situation of the patient, and particular care should be reserved to patients with oncologic history.

Reproducibility of temporal volume change in CT of lung cancer: comparison of computer software and manual assessment

The purpose of this study was to investigate the reproducibility of volumetric software evaluation and manual evaluation of tumour growth. Three observers manually evaluated whether tumour volume was increasing, if it was unchanged, or if it had decreased in size in 2 serial CT examinations of 45 solid lung cancers. The tumour volumes were calculated 3 times using volumetric software and were evaluated using the same classifications as for manual evaluation. Both data sets were divided into three groups: growth or reduction with consistency among all three evaluations (group A), growth or reduction with consistency between only two evaluations (group B), and others (group C). The volume variation and relative volume variation were calculated from the median volumes measured by volumetric software. Although all 45 tumours were categorised in group A by volumetric software, only 21 tumours were categorised in group A by manual assessment. The relative volume variation of the manual assessment was 88.5 +/- 76.5%, 20.8 +/- 28.3% and 12.9 +/- 12.8% in group A, B and C, respectively. Significant differences were found between groups A and B (p<0.01) and between groups A and C (p<0.001). Inconsistency is often seen in manual assessment; in contrast, evaluation using volumetric software has good reproducibility, even when the relative change in tumour volume is small.

Pulmonary nodule volume: effects of reconstruction parameters on automated measurements--a phantom study

PURPOSE

To prospectively evaluate in a phantom the effects of reconstruction kernel, field of view (FOV), and section thickness on automated measurements of pulmonary nodule volume.

MATERIALS AND METHODS

Spherical and lobulated pulmonary nodules 3-15 mm in diameter were placed in a commercially available lung phantom and scanned by using a 16-section computed tomographic (CT) scanner. Nodule volume (V) was determined by using the diameters of 27 spherical nodules and the mass and density values of 29 lobulated nodules measured by using the formulas V = (4/3)pi r(3) (spherical nodules) and V = 1000 x (M/D) (lobulated nodules) as reference standards, where r is nodule radius; M, nodule mass; and D, wax density. Experiments were performed to evaluate seven reconstruction kernels and the independent effects of FOV and section thickness. Automated nodule volume measurements were performed by using computer-assisted volume measurement software. General linear regression models were used to examine the independent effects of each parameter, with percentage overestimation of volume as the dependent variable of interest.

RESULTS

There was no substantial difference in the accuracy of volume estimations across the seven reconstruction kernels. The bone reconstruction kernel was deemed optimal on the basis of the results of a series of statistical analyses and other qualitative findings. Overall, volume accuracy was significantly associated (P < .0001) with larger reference standard-measured nodule diameter. There was substantial overestimation of the volumes of the 3-5-mm nodules measured by using the volume measurement software. Decreasing the FOV facilitated no significant improvement in the precision of lobulated nodule volume measurements. The accuracy of volume estimations--particularly those for small nodules--was significantly (P < .0001) affected by section thickness.

CONCLUSION

Substantial, highly variable overestimation of volume occurs with decreasing nodule diameter. A section thickness that enables the acquisition of at least three measurements along the z-axis should be used to measure the volumes of larger pulmonary nodules.