Original Research
Cardiopulmonary Imaging
September 2011

CT Screening and Follow-Up of Lung Nodules: Effects of Tube Current–Time Setting and Nodule Size and Density on Detectability and of Tube Current–Time Setting on Apparent Size


OBJECTIVE. The purpose of the study was to quantify and compare the effect of CT dose and of size and density of nodules on the detectability of lung nodules and to quantify the influence of CT dose on the size of the nodules.
MATERIALS AND METHODS. From 50 patients a total of 125 cuboidal regions of interest (3 × 3 × 1.5 cm volumes) showing a single nodule (≤ 8 mm) and 27 normal cuboids were selected. Image sets were reconstructed with the software from raw data simulating different dose levels: 300 (original dose), 220, 180, 140, 100, 80, 60, 50, 40, 30, 20, 10, and 5 reference mAs. A logistic regression model was used to analyze detectability for three blinded readers. Odds ratios were calculated for nodule size smaller than 3 mm versus 3 mm and larger and for nodule attenuation of –300 HU and greater versus less than –300 HU.
RESULTS. Tube current–time settings of 10 mAs and greater were not associated with a significant difference in individual reader sensitivity compared with the standard setting of 300 mAs. At 5 mAs only one reader had a significant decrease in sensitivity, from 82% to 77% (p = 0.0035). According to the odds ratios and logistic regression results, the strongest negative effect on sensitivity can be assumed for low nodule density followed by small nodule size and dose level. The mean nodule volume measurement error between 5 and 300 mAs was 2.2% ± 18% (SD) and much lower than the interobserver volume measurement error rate of 38% ± 45%.
CONCLUSION. The results show the feasibility of a low-dose CT protocol at 10 mAs for follow-up of lung nodules. Computer-aided volume measurement in follow-up of lung nodules decreases interobserver variability.
CT screening has increased the rate of detection of small nodules, including those of early peripheral lung cancer [13]. Despite the increasingly higher spatial and contrast resolution of CT, nodular lesions are missed at chest CT. The sensitivity of chest CT for lung nodules depends on nodule characteristics and image acquisition parameters. In a study by Ko et al. [4], a small nodule size of 5 mm or less in diameter (detection sensitivity, 74%; > 5 mm, 82%), ground-glass opacity nodules (sensitivity, 65%; solid nodules, 83%), and lesion location (sensitivity for central nodules, 61%; peripheral nodules, 80%) were found to be major factors contributing to the difficulty of detecting nodules. In several studies, investigators have defined the lowest acceptable tube current–time settings without significant loss of sensitivity compared with standard-dose CT (100–250 mAs). Hetmaniak et al. [5] found 30 mAs; Weng et al. [6], 43 mAs; Gergely et al. [7], 5 mAs; and Das et al. [8], 10 mAs. Nodule detection can be improved by advances in computer-aided detection systems that are being developed and evaluated to provide a second perspective on nodule detection [9, 10]. However, Lee et al. [11] found that the sensitivity of a computer-aided detection system (81%) did not differ significantly from that of radiologists (85%). Radiologists were more sensitive at detecting nodules attached to other structures, whereas computer-aided detection was better for finding isolated nodules and those 5 mm in diameter or smaller.
The size of the nodule is important because volume-doubling time is a predictor of malignancy [12, 13]. There is considerable interreader and intrareader variability in assessing the size of pulmonary nodules [1416]. The measurement is affected by measuring method (determination of longest axis, segmentation of computer-aided measurement), FOV, window width and level settings [17], and changes in lung volume during breathing [18]. Even though computerized methods seem to be immune to some factors that affect radiologists, challenges persist owing to image acquisition parameters and nodule characteristics. Tube current–time setting, slice thickness, reconstruction algorithm, nodule attenuation, and nodule size influence volume error in studies of computer-aided detection [19, 20].
The purposes of this study were to quantify and compare the effect of CT tube current–time setting, nodule size, and nodule density on the detectability of lung nodules and to investigate the effect of CT tube current–time setting on apparent nodule size. The major differences between this investigation and previous studies include the larger study sample, greater continuous range of nodule attenuation and tube current–time settings, comparison between radiologist and computer methods, and investigation of lung nodules in real patients. We also examined the influence of both nodule characteristics and image acquisition parameters on sensitivity and the influence of tube current–time setting on volume measurement.

Materials and Methods


Raw data from thoracic CT scans of 50 consecutively registered patients referred for cancer staging, lung nodule screening, or follow-up were prospectively collected between January and March 2009. The images were read by a consensus panel of two thoracic radiologists with 9 and 10 years of experience. Lung nodules were found in 41 patients (22 men, 19 women; median age, 55.5 years; range, 35–83 years; median weight, 73 kg; range, 42–120 kg), and a total of 125 nodules were selected. The nine patients with normal (true-negative) thoracic CT findings served as a control group. The study protocol was approved by the local institutional review board, and informed consent was waived because of the retrospective data processing.

Image Acquisition

All image data were obtained with a 64-MDCT scanner (Somatom Sensation, Siemens Healthcare) with collimation of 64 × 0.75 mm, pitch of 2, 120 kVp, and reference tube current–time setting of 300 mAs. Automated tube current–time modulation (CARE Dose, Siemens Healthcare) was engaged to ensure a similar noise index for each patient. Acquisition parameters were identical to the parameters used in our standard clinical protocol. All patients were scanned in the supine position from the lung apex to the base of the chest at breath-hold full inspiration. No IV contrast material was administered.

Image Reconstruction

After reconstruction of the original DICOM images with an image matrix of 512 × 512 and a B45 kernel, 12 additional image sets were reconstructed from the same raw data simulating different dose levels. Computer-calculated noise was superimposed on the original raw dataset by use of the validated CT software (Somatom Noise+ V5.Obeta, 2006, Siemens Healthcare) [21], and reference tube current–time settings of 220, 180, 140, 100, 80, 60, 50, 40, 30, 20, 10, and 5 mAs were simulated. This process resulted in 13 datasets per patient available for image analysis. The rather low kernel was a compromise for improving the 3D reformation and the accuracy of semiautomated volume measurement.

Expert-Derived Standard of Reference

A consensus panel of two thoracic radiologists (9 and 10 years of experience) identified small nodules (≤ 8 mm), making sure that approximately one half of the selected nodules were solid (maximum attenuation, ≥ –300 HU) and one half ground-glass nodules (< –300 HU). Cuboidal regions of interest (3 × 3 × 1.5 cm volumes) containing only one nodule each were selected by transferring the center coordinates to the lung nodule evaluation platform (Internet readout workstation described later). To compensate for potential enhanced nodule detection for limited FOV and z-axis, only smaller (≤ 8 mm) nodules were selected. A total of 125 cuboids showing a single nodule were selected for subsequent image analysis. A total of 27 cuboids of the nine patients without disease were randomly collected and added for image analysis. The greatest axial dimension of each CT finding was measured with digital calipers, and its maximum attenuation was noted in Hounsfield units.

Image Analysis

Detectability of nodules—Interpretation was performed with a lung nodule evaluation platform that was not commercially available. The system featured a split screen for automated display of two orthogonal cross sections (axial and coronal planes combined for multiplanar reconstruction) in stacked cine mode of a cuboidal region of interest. For our study the screen was zoomed to the axial cross sections, and the coronal plane was hidden. Before appearing on the screen, the cuboids were randomly axially rotated to positions 0°, 90°, 180°, 270°. The cuboids then could be browsed slice by slice. In a separate integrated window, readers recorded their answers by mouse click. Data then were automatically transmitted online to a statistician. The lung nodule evaluation platform was installed on a PC workstation (Dell E520, 2.1 GHz, Intel Core 2 Duo, 4 GB RAM) with a 20-inch (51 cm) liquid crystal display (Dell Ultrasharp, 1600 × 1200 pixels). Ambient light conditions, which were similar to our clinical reading conditions, and the display window settings (window center, –600 HU; width, 1500 HU) were kept constant for all reading sessions and readers.
Size of nodules—Digitized measurements of nodule size were performed with a Mac Book Pro 15-inch (38 cm) screen (Apple Computer) running OsiriX Imaging Software (OsiriX). Center coordinates were used to find the nodules in the full CT datasets. Zoom of 2000% was used for all nodules. Computer-aided volume measurement (LMS Lung/Track, Median Technologies) was performed. This semiautomatic lesion measure ment solution seg ments pulmonary nodules identi fied by radiologists (radiologist chooses nodule by mouse click). The segmentation is the result of a region-growing algorithm followed by thresholding on attenuation (Hounsfield units) and concluded by mathematic morphologic operations (erosion and dilation) to separate the suspicious lesion from surrounding anatomic structures. The method published by Bland and Altman for assessing agree ment between two methods of clinical measure ment [22] was used to determine intraobserver and inter observer variability and agreement of volume measurements at different CT tube current–time levels. This method was used to calculate limits of agreement.


Detectability of nodules—Three readers (board-certified radiologists who had 5 years [reader 1], 6 years [reader 2], and 12 years [reader 3] of experience in interpretation of chest CT scans) were instructed to identify lung nodules. A pilot study with a different reader (9 years of chest CT experience) of 36 cuboids from 20 patients at five dose levels (300, 100, 50, 20, and 5 mAs) was performed to decrease the number of readout cases. Only at dose levels of 20 and 5 mAs were different answers given compared with 300 mAs. Therefore, we limited the evaluation to seven relevant tube current–time levels: 300, 100, 40, 30, 20, 10, and 5 mAs. The readers' answers were recorded by use of the lung nodule evaluation platform. Image interpretation took place in multiple reading sessions with blocks not exceeding 1 hour of successive reading to minimize reader fatigue. Presentation of the cases to the readers was randomized, and the cuboids were randomly rotated over 360° in the axial plane to minimize recognition bias.
Size of nodules—Comparison of measurements on two consecutive scans requires knowledge of observer agreement because all observed differences between measurements of the same nodule on different scans can be attributed to intraobserver variability or to interscan variability. Two readers (other than the aforementioned board-certified radiologists who had 3 years [reader 4] and 6 years [reader 5] of experience in interpretation of chest CT) measured the size and volume of the 59 solid nodules at all 13 tube current–time levels manually and with the computer-aided lesion measurement solution software. The readers measured volume by multiplying the longest axial axis (according to the Response Evaluation Criteria in Solid Tumors [RECIST]), the perpendicular short axial axis, the longest coronal z-axis, and π/6. Reader 4 performed the same evaluation 3 months later to determine intrareader agreement. The computer measurements were obtained with segmentation and threshold methods for automated volume and longest axial diameter measurement (RECIST). This semiautomatic process was performed by both readers. Reader 5 repeated the measurement with a delay of 1 day for intrareader agreement. This technique was chosen because it was our hypothesis that repeated measurement with different starting points might give different volumes.

Statistical Analysis

Detectability of nodules—Individual results from the three readers were compared with the standard of reference. Sensitivity and specificity for each reader in detection of pulmonary nodules were calculated at each dose level. Paired comparisons of the sensitivity of detection at each lower dose level against the corresponding detection at 300 mA were performed. The null hypothesis of equality of sensitivity for this dose pair was then tested by McNemar test for each reader [23].
For comparison of the effect of tube current–time level and nodule size and density on sensitivity for lung nodules the odds ratio for each variable and reader was calculated. For practical reasons, data on each variable were dichotomized: size 3 mm and larger versus less than 3 mm, attenuation –300 HU versus less than –300 HU (ground-glass opacity), and standard dose (300 mAs) versus low dose (5 mAs). Odds ratios and sensitivities were calculated, and p values for each variable were analyzed by chi-square test. If an absolute cell frequency was lower than 5, the Fisher exact probability test was applied. A value of p < 0.05 was considered to indicate a statistically significant difference. To rule out a bias of dichotomizing level setting and to obtain a more general statement, the following logistic regression model was fitted to diagnosis [24]:
where p is the probability of a reader's giving a positive diagnosis (sensitivity); c is the intercept; b0, b1, and b2 are regression coefficients (log odds ratios); LD is log(dose) in milliampere-seconds (tube current–time level); st indicates standardized, size is the maximum axial diameter of a nodule (average of readers 4 and 5 at standard tube current–time level); HU is attenuation; and error is the specification error. The variables were standardized, and the mean was made 0 with 1 SD. The advantage of using this calculation is that the regression coefficients b0, b1, and b2 become comparable on the same scale. Because a logarithmic relation between dose and sensitivity can be assumed, the logarithmic dose was used.
Size of nodules—The method described by Bland and Altman [22] for assessing agreement between two methods of clinical measurement was used to determine intraobserver and interobserver variability and agreement of volume measurements between the standard tube current–time setting of 300 mAs and all lower tube current–time levels (220–5 mAs). This method of calculation of limits of agreement results in the range of 95% of all volume measurement errors. Because the percentage of changes is more important than absolute volume values, the volume measurement error (VME) is given as a percentage of absolute volume measurement difference (V1 – V2) in relation to nodule volume, where V1 is the volume of the first reading. The true nodule volume is not known; therefore, the mean nodule volume of both measurements (V1 + V2) / 2 is the best estimate obtainable [22]:
In addition, the 95% CI of the mean volume measurement error of the upper and lower limits of agreement was calculated. The limit of agreement was calculated from the SD of the measurement differences (VME), and the 95% CI was derived from the standard error for normally distributed data, which can be assumed for measurement differences [22] (Fig. 1). To find significant differences in volume measurements, the 95% CI of the volume measurement error should not include 0 for significant systematic bias. The corrected SD of measurement differences was applied for repeated measurements [22].
Fig. 1 Chart shows Bland-Altman method. Limits of agreement (LA) are volume measurement error (VME) ± 2 SD, indicating limits, where 95% of errors lie. LA are only estimates of values that apply to whole population. Second sample would give different limits. Therefore, CI of VME and LA better describe real limits for whole population (corrected LA). CI can be calculated with standard error of VME, which follows approximately normal distribution.


Standard of Reference

Diameter and volume are indicated as the average of readers at a tube current–time level of 300 mAs. The range of largest axial diameters of the 125 nodules was 1.1–8.0 mm (mean, 3.11 ± 1.66 [SD] mm; median, 2.65 mm). The range of nodule volumes was 3.9–397.5 mm3 (mean, 58.1 ± 83.5 mm3; median, 24.9 mm3). Attenuation fluctuated from –800 to 212 HU (mean, –300.4 ± 317 HU; median, –352 HU). Forty-nine of the 125 nodules (39%) were small (< 3 mm), and 66 (53%) were ground-glass nodules (< –300 HU).

Dose-Dependent Sensitivity and Specificity

The tube current–times levels of 10, 20, 30, 40, and 100 mAs did not exhibit a significant difference in individual reader sensitivity compared with the standard 300 mAs (Table 1). At the lowest tube current level of 5 mAs, only reader 1 had a significant decrease in sensitivity from 82% to 77% (p = 0.0035). Nodule detection by readers 2 and 3 decreased from 98% and 94% at 300 mAs to 95% and 92% at 5 mAs, but the difference was not statistically significant (p = 0.39 and p = 0.45, McNemar test). Individual specificities were high and unaffected by tube current–time level. Specificity at the standard dose was 96%, 89%, and 92% for readers 1, 2, and 3 and did not decrease at lower dose levels (p > 0.31).
TABLE 1: Sensitivity for Lung Nodules (n = 125) Calculated With McNemar Test

Influence of Nodule Size, Density, and Tube Current–Time Setting on Sensitivity

Sensitivity as a function of nodule characteristic is shown in Table 2. There were large individual differences in ability to detect individual lung nodules. Reader 1 had significantly lower sensitivity for smaller and ground-glass nodules. The difference in sensitivity for ground-glass versus solid nodules even increased, from 22% at standard dose to 39% at the lowest dose level. The sensitivity of reader 1 for smaller nodules was 12–20% lower than that reader's sensitivity for larger nodules. Interestingly, the sensitivity of reader 2 was not statistically affected by nodule size, and nodule density influenced sensitivity only at dose levels lower than 30 mAs. Reader 3 was influenced by nodule size but not by nodule density. The sensitivity for smaller nodules was 8–14% lower than the sensitivity for larger nodules.
The readers had one thing in common: Sensitivity for all 125 nodules was not significantly influenced by dose level. The tube current–time products exhibited different individual sensitivities (Table 1), which did not decrease significantly from 300 to 5 mAs, according to results of the chi-square test (p > 0.25).
The probability of a true-positive finding if a condition is present (size ≥ 3 mm) compared with the probability of a true-positive finding if the condition is not present (size < 3 mm) resulted in individual reader odds ratios of 3.4–4.2. Only the odds ratio of reader 1 was significant (p = 0.0097). For variable density, the odds ratios were 2.8–9.9, only reader 1 having a statistically significant result (p = 0.00051). The odds ratios for variable tube current were a nonsignificant 1.4–2.1 (p > 0.25). Therefore, the strongest negative effect on sensitivity can be assumed for attenuation less than –300 HU, followed by size less than 3 mm. Examples of small ground-glass nodules at different dose levels are shown in Figure 2.
TABLE 2: Sensitivity of Nodule Detection in Relation to Nodule Size and Attenuation

Logistic Regression

The most significant predictor of sensitivity was nodule density (p < 0.0001 for all three readers). Nodule size significantly affected the sensitivity of reader 1 only (p = 0.02). Tube current–time level did significantly influence the sensitivity of reader 1 alone (p = 0.007), but for readers 2 and 3, it was not a significant predictor of sensitivity (p = 0.20, p = 0.19) in the logistic regression model.

Measurement of Nodule Size by Radiologists

Agreement of nodule size for lower tube current–time levels compared with reference standard—Agreement between the 5 and 300 mAs levels is presented; the relative volume differences are shown in Figure 3. The results for the other tube current–time levels are listed in Table 3. The mean volume measurement error of two readers combined was –2.2% with a 95% CI of –7.8% to 3.4%; hence a systematic bias between the measurements cannot be assumed (zero is inside the 95% CI). The negative sign means that volume 2 (5 mAs) was measured larger than volume 1 (300 mAs).
The lower limit of agreement was –39% (95% CI, –47% to 31%), and the upper limit was 35% (95% CI, 27–43%). An increase in nodule volume of more than 43% from the standard tube current–time level (300 mAs) to a hypothetical low-dose follow-up examination (5 mAs) is more likely to indicate real nodule growth than interscan variability. Often the nodules were distorted at lower tube current–time levels because of superimposed noise, and their diameter might have increased (Fig. 4) or decreased but would have had no statistical effect on the overall volume measurement. However, the longest axial diameter measurement exhibited significant differences for lower tube current–time levels. The diameters were measured longer for levels of 100 mAs and less (Fig. 4). The mean measurement error for 5 mAs was –5.3% (95% CI, –7.5% to –3%) with corrected limits of agreement of –28% and 17%.
Fig. 2 CT scans show ground-glass nodules at different tube current–time levels.
A, 54-year-old man with 2-mm ground-glass nodule (arrows) with attenuation of –584 HU detectable at all levels.
B, 48-year-old man with 1.5-mm ground-glass nodule with attenuation of –647 HU clearly visible at standard and higher tube current–time levels (white arrows) that disappears at lower levels (black arrows).
Fig. 3 Graphs show agreement of manual volume measurement of all nodules for standard and lowest tube current–time levels and for different readers (interreader agreement). Mean difference of two readers (bottom) is much higher than mean difference between two currents (top), and scattering of volume differences is much larger for different readers than for different currents. Therefore, upper limit of agreement is greater than 100% for two readers and less than 50% for different tube current–time settings. VME = volume measurement error.
Interreader and intrareader agreement of volume and diameter measurement—With application of the same method for the two readers (reader 1 volume – reader 2 volume / mean volume), the mean interobserver variability of volume measurements at the standard tube current–time level of 300 mAs was 38% (95% CI, 27–49%), meaning a significant difference between the two readers. The mean volume measurement of reader 1 was substantially larger. A lower limit of agreement of –51% (95% CI, –71% to –32%) and an upper limit of agreement of 127% (95% CI, 108–146%) were found. In absolute figures, these results meant a variability among the readers of 9.3 ± 20.7 mm3 with limits of –32.1 mm3 (95% CI, –41 to –23 mm3) to 51 mm3 (95% CI, 42–59.7 mm3) for an average absolute nodule volume of 58.1 mm3 (mean of readers 1 and 2). Interobserver variability did not change substantially for lower tube current–time levels (Table 3). Intraobserver agreement of volume measurement by reader 1 at the standard tube current–time level was 3.9% (95% CI, –2.4% to 10%) with a lower limit of agreement of –35% (95% CI, –43% to –27%) and an upper limit of agreement of 43% (95% CI, 35–51%). The intraobserver measurement error for the longest diameter was 2.8% (95% CI, –0.1% to 5.8% and therefore not significant) and had a smaller limit of agreement according to the single dimension (compared with the volume) of –20% to 26%. The measurement errors for neither volume nor diameter differed substantially for lower tube current–time levels (Table 3).

Computer-Aided Volume Measurement

Agreement on nodule size for lower tube current–time levels compared with standard level—Less agreement of volume and diameter measurement for the computer-aided lesion measurement solution resulted in higher variability and therefore wider limits of agreement. For example, at 5 mAs, the mean volume measurement error was 6% (95% CI, –1.8% to 13.8%) with a lower limit of agreement of –58% (95% CI, –71% to –44%) and an upper limit of agreement of 70% (95% CI, 56–83%).
TABLE 3: Manual Nodule Volume Measurement Errors and Corrected Limits of Agreement
Interreader and intrareader agreement on volume and diameter measurement—Much better interreader agreement was found for the computer-aided lesion measurement solution than for manual measurements (Fig. 5). The volume measurement error at standard dose was –4.1%, and the corrected limits of agreement were –69% and 60%. In contrast to manual measurement, the lesion measurement solution exhibited no significant difference between the two readers. Good intrareader agreement with a similarly low rate of volume measurement error of –5.9% was found with corrected limits of agreement of –68% and 56%. The reader measurement error for longest diameter also exhibited better interreader and intrareader agreement with the computer-aided technique than without it.
Fig. 4 55-year-old man with metastases from bronchogenic carcinoma. CT scans show measurements of longest axial diameter (green line) at different tube current–time levels in milliampere-seconds (top left number in each image). Top left image shows 3D reconstruction for computer-aided volume measurement (LMS Lung/Track, Median Technologies). Increases in manually measured size are due to distortion of nodule from increasing superimposed noise. Computer-aided volume measurements (yellow dots) are shown at 300 and 5 mAs. Although readers included larger parts of ground-glass transition zone in nodule diameter, computer-aided measurement had higher attenuation threshold level. Therefore, volumes measured with computer-aided technique were smaller than manually measured volumes.

Human Versus Machine

At each dose level, the readers measured a significantly larger volume than did the computer. At the standard dose level (300 mAs) the readers measured 25% more, with corrected limits of agreement of –86% and 136%. The longest diameter was also 15.8% longer measured manually.
Fig. 5 Graphs show computer-aided (left) and manual (right) volumetric results for each nodule for reader 1 versus reader 2. Dashed lines show correlation between readers for same nodules. Solid line indicates y = x, that is, 100% correlation. Agreement between readers is much better with computer-aided than with manual technique.


This study showed a significant difference between tube current–time settings of 300 and 5 mAs with respect to sensitivity of chest CT for lung nodules for one of the three readers and no significant change in sensitivity for doses of 100, 40, 30, 20, and 10 mAs compared with 300 mAs. These results suggest 10 mAs is the threshold for diagnostic image quality in this study setting and confirm the results of other low-dose CT studies [7, 8]. Reducing the tube current–time level from 300 to 10 mAs automatically reduces the radiation dose received to an average of 3.3% of the standard dose. The risk of radiation-induced cancer death for one standard chest CT examination is estimated at approximately 1:4000 [25] and may hypothetically be reduced to 1:120,000 if a linear relation between dose and risk of cancer death is assumed [25]. A low-dose protocol would be of special interest to patients with lung nodules because the cumulative radiation dose of repetitive follow-up CT examinations might be reduced considerably.
In our study, nodule characteristics had the most important negative effect on detectability of small pulmonary nodules, and the technical parameter tube current–time product had less influence on sensitivity. Ko et al. [4] reported a similar odds ratio of 3.7 for nodule size (> 5 mm vs ≤ 5 mm) and an odds ratio of 3.2 for ground-glass opacity. One reader in our study had a higher odds ratio of 9.9 for nodule density, probably because of the selection of smaller nodules. Small ground-glass nodules are much more difficult to identify, leading to a larger gap of sensitivities.
The odds ratios of the tube current–time levels exhibited little influence of dose on nodule sensitivity. In addition, in the reader-specific logistic regressions, the effect of tube current–time level was significant only for reader 1. The McNemar test, which is the better test for comparing paired data alone, showed significantly lower sensitivity only at 5 mAs for the same reader, indicating that the tube current–time level may influence the detectability of lung nodules, but not as much as the nodule characteristics.
The manual volume measurement in our study was much more influenced by inter-reader variability than by variability of tube current–time level or intraobserver variability. The tube current–time setting can be reduced from 300 to 5 mAs with remarkable reproducibility of volume measurement: a volume measurement error of –2.2% compared with more than 32% for different readers at any tube current–time level.
The problem with volume-doubling rate calculations is not a change in technical parameters between scans but the change of readers. Gietema et al. [26] found narrower limits of agreement (–21.2% to 23.8%) for interscan volume measurement; Wormanns et al. [27] and Hein et al. [28, 29] found narrower limits of agreement for automated interscan volume measurement (–20.4% to 21.9% and –26.5% to 29.6%) and even narrower interobserver and intraobserver agreement. The narrower limits of agreement in these studies is probably due to the smaller tube current–time gap (75 vs 5 mAs) and the selection of larger nodules (mean diameter, 11 mm) whereby measurement error automatically leads to a smaller relative difference.
We used corrected limits of agreement [22], which widened the range of agreement. The interobserver variability for manual measurement of 38% at the standard tube current–time setting appears large for a volume but would be much reduced in evaluation of diameter. For a hypothetical spherical nodule with a diameter of 1 mm, a 38% decrease in volume would mean a reduction of the diameter to 0.85 mm. Taking large interreader variability into account, a clinically significant volume increase would be detected when the volume difference exceeded the 95% limit of agreement of approximately 146%. With an increase in volume measurement of greater than 146%, the probability of real growth, rather than interreader inaccuracy, would be 95% for small lung nodules. Measurement variability at different tube current–time levels is much smaller. From a first examination at 300 mAs to a hypothetical low-dose follow-up CT examination at 5 mAs, a volume increase greater than 43% (95% limit of agreement) for the same reader would be 95% more likely to be real growth than variability between tube current–time levels.
The readers measured significantly larger volumes manually than with the computer-aided lesion measurement solution. The readers probably tended to include large parts of the small ground-glass transition around the nodule into the measurement of the diameter. The computer algorithm, however, measured from fixed higher-attenuation threshold levels with the potential of being inappropriately small owing to elimination of partial volume at the lung interface. Over all, the lesion measurement solution was associated with slightly greater volume measurement error on average at the different dose levels but would be definitely superior in tumor follow-up CT with different readers, especially because the volume difference between two readers was significant for the manual measurement but not for the computer-aided lesion measurement solution. Although studies have shown imprecision in automated volume measurements and volume-doubling time estimation of pulmonary nodules [30], our study showed that volume-doubling time estimation would be more accurate if an automated measurement tool were used for both measurements than if individual manual measurements were made by two readers.
A limitation of this study was that it did not capture depth of inspiration as a factor affecting nodule volume. In addition, the design whereby interscan variability is assessed by repeating scanning after very short time intervals and patients are required to leave the imaging table can strongly influence volume measurement, and this aspect was neglected in our analysis. In this study, low-dose images were simulated, and using a setting less than 50 mAs with artificial noise might not have been an accurate representation of an actual 50-mAs scan because other issues beyond quantum mottle can affect image quality. Furthermore, to compensate for potential enhanced nodule detection for limited FOV and z-axis, only smaller nodules (≤ 8 mm) were selected. Whether our study design represents circumstances in daily clinical routine has yet to be investigated. Results of ongoing low-dose studies without simulation should clarify the answer to this question.


We thank the Department of Radiology, Stanford University, for providing infrastructure and staffing.


Supported by the Swiss National Science Foundation (SSMBS, Patronage of Swiss Academy of Medical Sciences), the Swiss Gottfried und Julia Bangerter-Rhyner Foundation and the Swiss Huggenberg-Bischoff Foundation.


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Information & Authors


Published In

American Journal of Roentgenology
Pages: 623 - 630
PubMed: 21862804


Submitted: July 8, 2010
Accepted: January 17, 2011


  1. CT
  2. density
  3. dose
  4. lung nodules
  5. sensitivity
  6. volume measurement error



Andreas Christe
Department of Radiology, Stanford University School of Medicine, Stanford, CA.
Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, University Hospital, University of Bern, Freiburgstrasse 10, CH-3010 Bern, Switzerland.
Jaled Charimo Torrente
Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, University Hospital, University of Bern, Freiburgstrasse 10, CH-3010 Bern, Switzerland.
Margaret Lin
Department of Radiology, Stanford University School of Medicine, Stanford, CA.
Andrew Yen
Department of Radiology, Stanford University School of Medicine, Stanford, CA.
Rich Hallett
Department of Radiology, Stanford University School of Medicine, Stanford, CA.
Kingshuk Roychoudhury
Statistics Department, University College Cork, Cork, Ireland.
Florian Schmitzberger
Department of Radiology, Stanford University School of Medicine, Stanford, CA.
Peter Vock
Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, University Hospital, University of Bern, Freiburgstrasse 10, CH-3010 Bern, Switzerland.
Justus Roos
Department of Radiology, Stanford University School of Medicine, Stanford, CA.


Address correspondence to A. Christe ([email protected]).

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