Discrimination tasks in simulated low-dose CT noise

BACKGROUND

This study reports the results of a set of discrimination experiments using simulated images that represent the appearance of subtle lesions in low-dose computed tomography (CT) of the lungs. Noise in these images has a characteristic ramp-spectrum before apodization by noise control filters. We consider three specific diagnostic features that determine whether a lesion is considered malignant or benign, two system-resolution levels, and four apodization levels for a total of 24 experimental conditions.

PURPOSE

The goal of the investigation is to better understand how well human observers perform subtle discrimination tasks like these, and the mechanisms of that performance. We use a forced-choice psychophysical paradigm to estimate observer efficiency and classification images. These measures quantify how effectively subjects can read the images, and how they use images to perform discrimination tasks across the different imaging conditions.

MATERIALS AND METHODS

The simulated CT images used as stimuli in the psychophysical experiments are generated from high-resolution objects passed through a modulation transfer function (MTF) before down-sampling to the image-pixel grid. Acquisition noise is then added with a ramp noise-power spectrum (NPS), with subsequent smoothing through apodization filters. The features considered are lesion size, indistinct lesion boundary, and a nonuniform lesion interior. System resolution is implemented by an MTF with resolution (10% max.) of 0.47 or 0.58 cyc/mm. Apodization is implemented by a Shepp-Logan filter (Sinc profile) with various cutoffs. Six medically naïve subjects participated in the psychophysical studies, entailing training and testing components for each condition. Training consisted of staircase procedures to find the 80% correct threshold for each subject, and testing involved 2000 psychophysical trials at the threshold value for each subject. Human-observer performance is compared to the Ideal Observer to generate estimates of task efficiency. The significance of imaging factors is assessed using ANOVA. Classification images are used to estimate the linear template weights used by subjects to perform these tasks. Classification-image spectra are used to analyze subject weights in the spatial-frequency domain.

RESULTS

Overall, average observer efficiency is relatively low in these experiments (10%-40%) relative to detection and localization studies reported previously. We find significant effects for feature type and apodization level on observer efficiency. Somewhat surprisingly, system resolution is not a significant factor. Efficiency effects of the different features appear to be well explained by the profile of the linear templates in the classification images. Increasingly strong apodization is found to both increase the classification-image weights and to increase the mean-frequency of the classification-image spectra. A secondary analysis of "Unapodized" classification images shows that this is largely due to observers undoing (inverting) the effects of apodization filters.

CONCLUSIONS

These studies demonstrate that human observers can be relatively inefficient at feature-discrimination tasks in ramp-spectrum noise. Observers appear to be adapting to frequency suppression implemented in apodization filters, but there are residual effects that are not explained by spatial weighting patterns. The studies also suggest that the mechanisms for improving performance through the application of noise-control filters may require further investigation.

Semi-automated volumetry of pulmonary nodules: Intra-individual comparison of standard dose and chest X-ray equivalent ultralow dose chest CT scans

PURPOSE

To assess the performance of semi-automated volumetry of solid pulmonary nodules on single-energy tin-filtered ultralow dose (ULD) chest CT scans at a radiation dose equivalent to chest X-ray relative to standard dose (SD) chest CT scans and assess the impact of kernel and iterative reconstruction selection.

METHODS

Ninety-four consecutive patients from a prospective single-center study were included and underwent clinically indicated SD chest CT (1.9 ± 0.8 mSv) and additional ULD chest CT (0.13 ± 0.01 mSv) in the same session. All scans were reconstructed with a soft tissue (Br40) and lung (Bl64) kernel as well as with Filtered Back Projection (FBP) and Iterative Reconstruction (ADMIRE-3 and ADMIRE-5). One hundred and forty-eight solid pulmonary nodules were identified and analysed by semi-automated volumetry on all reconstructions. Nodule volumes were compared amongst all reconstructions thereby focusing on the agreement between SD and ULD scans.

RESULTS

Nodule volumes ranged from 58.5 (28.8-126) mm3 for ADMIRE-5 Br40 ULD reconstructions to 72.5 (39-134) mm3 for FBP Bl64 SD reconstructions with significant differences between reconstructions (p < 0.001). Interscan agreement of volumes between two given reconstructions ranged from ICC = 0.605 to ICC = 0.999. Between SD and ULD scans, agreement of nodule volumes was highest for FBP Br40 (ICC = 0.995), FBP Bl64 (ICC = 0.939) and ADMIRE-5 Bl64 (ICC = 0.994) reconstructions. ADMIRE-3 reconstructions exhibited reduced interscan agreement of nodule volumes (ICCs from 0.788 - 0.882).

CONCLUSIONS

The interscan agreement of node volumes between SD and ULD is high depending on the choice of kernel and reconstruction algorithm. However, caution should be exercised when comparing two image series that were not identically reconstructed.

Diagnostic validation of a deep learning nodule detection algorithm in low-dose chest CT: determination of optimized dose thresholds in a virtual screening scenario

OBJECTIVES

This study was conducted to evaluate the effect of dose reduction on the performance of a deep learning (DL)-based computer-aided diagnosis (CAD) system regarding pulmonary nodule detection in a virtual screening scenario.

METHODS

Sixty-eight anthropomorphic chest phantoms were equipped with 329 nodules (150 ground glass, 179 solid) with four sizes (5 mm, 8 mm, 10 mm, 12 mm) and scanned with nine tube voltage/current combinations. The examinations were analyzed by a commercially available DL-based CAD system. The results were compared by a comparison of proportions. Logistic regression was performed to evaluate the impact of tube voltage, tube current, nodule size, nodule density, and nodule location.

RESULTS

The combination with the lowest effective dose (E) and unimpaired detection rate was 80 kV/50 mAs (sensitivity: 97.9%, mean false-positive rate (FPR): 1.9, mean CTDIvol: 1.2 ± 0.4 mGy, mean E: 0.66 mSv). Logistic regression revealed that tube voltage and current had the greatest impact on the detection rate, while nodule size and density had no significant influence.

CONCLUSIONS

The optimal tube voltage/current combination proposed in this study (80 kV/50 mAs) is comparable to the proposed combinations in similar studies, which mostly dealt with conventional CAD software. Modification of tube voltage and tube current has a significant impact on the performance of DL-based CAD software in pulmonary nodule detection regardless of their size and composition.

KEY POINTS

• Modification of tube voltage and tube current has a significant impact on the performance of deep learning-based CAD software. • Nodule size and composition have no significant impact on the software's performance. • The optimal tube voltage/current combination for the examined software is 80 kV/50 mAs.

Lung Cancer Imaging: Screening Result and Nodule Management

Background: Lung cancer (LC) represents the main cause of cancer-related deaths worldwide, especially because the majority of patients present with an advanced stage of the disease at the time of diagnosis. This systematic review describes the evidence behind screening results and the current guidelines available to manage lung nodules. Methods: This review was guided by the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. The following electronic databases were searched: PubMed, EMBASE, and the Web of Science. Results: Five studies were included in the systematic review. The study cohort included 46,364 patients, and, in this case series, LC was detected in 9028 patients. Among the patients with detected LC, 1261 died of lung cancer, 3153 died of other types of cancers and 4614 died of other causes. Conclusions: This systematic review validates the use of CT in LC screening follow-ups, and bids for future integration and implementation of nodule management protocols to improve LC screening, avoid missed cancers and to reduce the number of unnecessary investigations.

Imaging protocols for CT chest: A recommendation

Computed Tomography (CT) is the mainstay of diagnostic imaging evaluation of thoracic disorders. However, there are a number of CT protocols ranging from a simple non-contrast CT at one end of the spectrum, and CT perfusion as a complex protocol available only on high-end scanners. With the growing diversity, there is a pressing need for radiologists, and clinicians to have a basic understanding of the recommended CT examinations for individual indications. This brief review aims to summarise the currently prevalent CT examination protocols, including their recommended indications, as well as technical specifications for performing them.

Management of pulmonary nodules

Pulmonary nodules are frequently detected during clinical practice and require a structured approach in their management in order to identify early lung cancers and avoid harm from over investigation. The article reviews the 2015 British Thoracic Society guidelines for the management of pulmonary nodules and the evidence behind them.

Diameter of the Solid Component in Subsolid Nodules on Low-Dose Unenhanced Chest Computed Tomography: Measurement Accuracy for the Prediction of Invasive Component in Lung Adenocarcinoma

Objective

To determine if measurement of the diameter of the solid component in subsolid nodules (SSNs) on low-dose unenhanced chest computed tomography (CT) is as accurate as on standard-dose enhanced CT in prediction of pathological size of invasive component of lung adenocarcinoma.

Materials and Methods

From February 2012 to October 2015, 114 SSNs were identified in 105 patients that underwent low-dose unenhanced and standard-dose enhanced CT pre-operatively. Three radiologists independently measured the largest diameter of the solid component. Intraclass correlation coefficients (ICCs) were used to assess inter-reader agreement. We estimated measurement differences between the size of solid component and that of invasive component. We measured diagnostic accuracy of the prediction of invasive adenocarcinoma using a size criterion of a solid component ≥ 6 mm, and compared them using a generalized linear mixed model.

Results

Inter-reader agreement was excellent (ICC, 0.84.0.89). The mean ± standard deviation of absolute measurement differences between the solid component and invasive component was 4 ± 4 mm in low-dose unenhanced CT and 5 ± 4 mm in standard-dose enhanced CT. Diagnostic accuracy was 81.3% (95% confidence interval, 76.7.85.3%) in low-dose unenhanced CT and 76.6% (71.8.81.0%) in standard-dose enhanced CT, with no statistically significant difference (p = 0.130).

Conclusion

Measurement of the diameter of the solid component of SSNs on low-dose unenhanced chest CT was as accurate as on standard-dose enhanced CT for predicting the invasive component. Thus, low-dose unenhanced CT may be used safely in the evaluation of patients with SSNs.

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.

Impact of radiation dose and iterative reconstruction on pulmonary nodule measurements at chest CT: a phantom study

PURPOSE

We aimed to identify the impact of radiation dose and iterative reconstruction (IR) on measurement of pulmonary nodules by chest computed tomography (CT).

METHODS

CT scans were performed on a chest phantom containing various nodules (diameters of 3, 5, 8, 10, and 12 mm; +100, -630 and -800 HU for each diameter) at 80, 100, 120 kVp and 10, 20, 50, 100 mAs (a total of 12 radiation dose settings). Each CT was reconstructed using filtered back projection, iDose4, and iterative model reconstruction (IMR). Thereafter, two radiologists measured the diameter and attenuation of the nodules. Noise, contrast-to-noise ratio and signal-to-noise ratio of CT images were also obtained. Influence of radiation dose and reconstruction algorithm on measurement error and objective image quality metrics was analyzed using generalized estimating equations.

RESULTS

The 80 kVp, 10 mAs CT scan was not feasible for the measurement of 3 mm sized simulated ground-glass nodule (GGN); otherwise, diameter measurement error was not significantly influenced by radiation dose (P > 0.05). IR did not have a significant impact on diameter measurement error for simulated solid nodules (P > 0.05). However, for simulated GGNs, IMR was associated with significantly decreased relative diameter measurement error (P < 0.001). Attenuation measurement error was not significantly influenced by either radiation dose or reconstruction algorithm (P > 0.05). Objective image quality was significantly better with IMR (P < 0.05).

CONCLUSION

Nodule measurements were not affected by radiation dose except for 3 mm simulated GGN on 80 kVp, 10 mAs dose setting. However, for GGNs, IMR may help reduce diameter measurement error while improving image quality.

Computer-aided detection (CAD) of solid pulmonary nodules in chest x-ray equivalent ultralow dose chest CT - first in-vivo results at dose levels of 0.13mSv

OBJECTIVES

To determine the value of computer-aided detection (CAD) for solid pulmonary nodules in ultralow radiation dose single-energy computed tomography (CT) of the chest using third-generation dual-source CT at 100kV and fixed tube current at 70 mAs with tin filtration.

METHODS

202 consecutive patients undergoing clinically indicated standard dose chest CT (1.8±0.7 mSv) were prospectively included and scanned with an additional ultralow dose CT (0.13±0.01 mSv) in the same session. Standard of reference (SOR) was established by consensus reading of standard dose CT by two radiologists. CAD was performed in standard dose and ultralow dose CT with two different reconstruction kernels. CAD detection rate of nodules was evaluated including subgroups of different nodule sizes (<5, 5-7, >7mm). Sensitivity was further analysed in multivariable mixed effects logistic regression.

RESULTS

The SOR included 279 solid nodules (mean diameter 4.3±3.4mm, range 1-24mm). There was no significant difference in per-nodule sensitivity of CAD in standard dose with 70% compared to 68% in ultralow dose CT both overall and in different size subgroups (all p>0.05). CAD led to a significant increase of sensitivity for both radiologists reading the ultralow dose CT scans (all p<0.001). In multivariable analysis, the use of CAD (p<0.001), and nodule size (p<0.0001) were independent predictors for nodule detection, but not BMI (p=0.933) and the use of contrast agents (p=0.176).

CONCLUSIONS

Computer-aided detection of solid pulmonary nodules using ultralow dose CT with chest X-ray equivalent radiation dose has similar sensitivities to those from standard dose CT. Adding CAD in ultralow dose CT significantly improves the sensitivity of radiologists.

Submillisievert CT using model-based iterative reconstruction with lung-specific setting: An initial phantom study

OBJECTIVE

To assess image quality of filtered back-projection (FBP) and model-based iterative reconstruction (MBIR) with a conventional setting and a new lung-specific setting on submillisievert CT.

METHODS

A lung phantom with artificial nodules was scanned with 10 mA at 120 kVp and 80 kVp (0.14 mSv and 0.05 mSv, respectively); images were reconstructed using FBP and MBIR with conventional setting (MBIR_Stnd) and lung-specific settings (MBIR_RP20/Tx and MBIR_RP20). Three observers subjectively scored overall image quality and image findings on a 5-point scale (1 = worst, 5 = best) compared with reference standard images (50 mA-FBP at 120, 100, 80 kVp). Image noise was measured objectively.

RESULTS

MBIR_RP20/Tx performed significantly better than MBIR_Stnd for overall image quality in 80-kVp images (p < 0.01), blurring of the border between lung and chest wall in 120p-kVp images (p < 0.05) and the ventral area of 80-kVp images (p < 0.001), and clarity of small vessels in the ventral area of 80-kVp images (p = 0.037). At 120 kVp, 10 mA-MBIR_RP20 and 10 mA-MBIR_RP20/Tx showed similar performance to 50 mA-FBP. MBIR_Stnd was better for noise reduction. Except for blurring in 120 kVp-MBIR_Stnd, MBIRs performed better than FBP.

CONCLUSION

Although a conventional setting was advantageous in noise reduction, a lung-specific setting can provide more appropriate image quality, even on submillisievert CT.

KEY POINTS

• Lung-specific submillisievert 10 mA-MBIR CT setting has similar performance to 50 mA-FBP • The new lung-specific settings improve vessel clarity and blurring of borders • The new settings may provide more appropriate images than conventional settings.

Effect of the high-pitch mode in dual-source computed tomography on the accuracy of three-dimensional volumetry of solid pulmonary nodules: a phantom study

Objective

To evaluate the influence of high-pitch mode (HPM) in dual-source computed tomography (DSCT) on the accuracy of three-dimensional (3D) volumetry for solid pulmonary nodules.

Materials and Methods

A lung phantom implanted with 45 solid pulmonary nodules (n = 15 for each of 4-mm, 6-mm, and 8-mm in diameter) was scanned twice, first in conventional pitch mode (CPM) and then in HPM using DSCT. The relative percentage volume errors (RPEs) of 3D volumetry were compared between the HPM and CPM. In addition, the intermode volume variability (IVV) of 3D volumetry was calculated.

Results

In the measurement of the 6-mm and 8-mm nodules, there was no significant difference in RPE (p > 0.05, respectively) between the CPM and HPM (IVVs of 1.2 ± 0.9%, and 1.7 ± 1.5%, respectively). In the measurement of the 4-mm nodules, the mean RPE in the HPM (35.1 ± 7.4%) was significantly greater (p < 0.01) than that in the CPM (18.4 ± 5.3%), with an IVV of 13.1 ± 6.6%. However, the IVVs were in an acceptable range (< 25%), regardless of nodule size.

Conclusion

The accuracy of 3D volumetry with HPM for solid pulmonary nodule is comparable to that with CPM. However, the use of HPM may adversely affect the accuracy of 3D volumetry for smaller (< 5 mm in diameter) nodule.

Small irregular pulmonary nodules in low-dose CT: observer detection sensitivity and volumetry accuracy

OBJECTIVE

The purpose of this study is to evaluate observer detection and volume measurement of small irregular solid artificial pulmonary nodules on 64-MDCT in an anthropomorphic thoracic phantom.

MATERIALS AND METHODS

Forty in-house-made solid pulmonary nodules (lobulated and spiculated; actual volume, 5.1-88.4 mm3; actual CT densities, -51 to 157 HU) were randomly placed inside an anthropomorphic thoracic phantom with pulmonary vasculature. The phantom was examined on two 64-MDCT scanners, using a scan protocol as applied in lung cancer screening. Two independent blinded observers screened for pulmonary nodules. Nodule volume was evaluated semiautomatically using dedicated software and was compared with the actual volume using an independent-samples t test. The interscanner and interobserver agreement of volumetry was assessed using Bland-Altman analysis.

RESULTS

Observer detection sensitivity increased along with increasing size of irregular nodules. Sensitivity was 100% when the actual volume was at least 69 mm3, regardless of specific observer, scanner, nodule shape, and density. Overall, nodule volume was underestimated by (mean±SD) 18.9±11.8 mm3 (39%±21%; p<0.001). The relative interscanner difference of volumetry was 3.3% (95% CI, -33.9% to 40.4%). The relative interobserver difference was 0.6% (-33.3% to 34.5%).

CONCLUSION

Small irregular solid pulmonary nodules with an actual volume of at least 69 mm3 are reliably detected on 64-MDCT. However, CT-derived volume of those small nodules is largely underestimated, with considerable variation.

Optimal dose levels in screening chest CT for unimpaired detection and volumetry of lung nodules, with and without computer assisted detection at minimal patient radiation

OBJECTIVES

The aim of this phantom study was to minimize the radiation dose by finding the best combination of low tube current and low voltage that would result in accurate volume measurements when compared to standard CT imaging without significantly decreasing the sensitivity of detecting lung nodules both with and without the assistance of CAD.

METHODS

An anthropomorphic chest phantom containing artificial solid and ground glass nodules (GGNs, 5-12 mm) was examined with a 64-row multi-detector CT scanner with three tube currents of 100, 50 and 25 mAs in combination with three tube voltages of 120, 100 and 80 kVp. This resulted in eight different protocols that were then compared to standard CT sensitivity (100 mAs/120 kVp). For each protocol, at least 127 different nodules were scanned in 21-25 phantoms. The nodules were analyzed in two separate sessions by three independent, blinded radiologists and computer-aided detection (CAD) software.

RESULTS

The mean sensitivity of the radiologists for identifying solid lung nodules on a standard CT was 89.7% ± 4.9%. The sensitivity was not significantly impaired when the tube and current voltage were lowered at the same time, except at the lowest exposure level of 25 mAs/80 kVp [80.6% ± 4.3% (p = 0.031)]. Compared to the standard CT, the sensitivity for detecting GGNs was significantly lower at all dose levels when the voltage was 80 kVp; this result was independent of the tube current. The CAD significantly increased the radiologists' sensitivity for detecting solid nodules at all dose levels (5-11%). No significant volume measurement errors (VMEs) were documented for the radiologists or the CAD software at any dose level.

CONCLUSIONS

Our results suggest a CT protocol with 25 mAs and 100 kVp is optimal for detecting solid and ground glass nodules in lung cancer screening. The use of CAD software is highly recommended at all dose levels.

A questionnaire survey reviewing radiologists' and clinical specialist radiographers' knowledge of CT exposure parameters

Objective

To review knowledge of computed tomography (CT) parameters and their influence on patient dose and image quality amongst a cohort of clinical specialist radiographers (CSRs) and examining radiologists.

Methods

A questionnaire survey was devised and distributed to a cohort of 65 examining radiologists attending the American Board of Radiology exam in Kentucky in November 2011. The questionnaire was later distributed by post to a matching cohort of Irish CT CSRs. Each questionnaire contained 40 questions concerning CT parameters and their influence on both patient dose and image quality.

Results

A response rate of 22 % (radiologists) and 32 % (CSRs) was achieved. No difference in mean scores was detected between either group (27.8 ± 4 vs 28.1 ± 4, P = 0.87) although large ranges were noted (18–36). Considerable variations in understanding of CT parameters was identified, especially regarding operation of automatic exposure control and the influence of kilovoltage and tube current on patient dose and image quality. Radiologists were unaware of recommended diagnostic reference levels. Both cohorts were concerned regarding CT doses in their departments.

Conclusions

CT parameters were well understood by both groups. However, a number of deficiencies were noted which may have a considerable impact on patient doses and limit the potential for optimisation in clinical practice.

Key points

• CT users must adapt parameters to optimise patient dose and image quality.

• The influence of some parameters is not well understood.

• A need for ongoing education in dose optimisation is identified.

Impact of image quality, radiologists, lung segments, and Gunnar eyewear on detectability of lung nodules in chest CT

BACKGROUND

Despite the increasingly higher spatial and contrast resolution of CT, nodular lesions are prone to be missed on chest CT. Tinted lenses increase visual acuity and contrast sensitivity by filtering short wavelength light of solar and artificial origin.

PURPOSE

To test the impact of Gunnar eyewear, image quality (standard versus low dose CT) and nodule location on detectability of lung nodules in CT and to compare their individual influence.

MATERIAL AND METHODS

A pre-existing database of CT images of patients with lung nodules >5 mm, scanned with standard does image quality (150 ref mAs/120 kVp) and lower dose/quality (40 ref mAs/120 kVp), was used. Five radiologists read 60 chest CTs twice: once with Gunnar glasses and once without glasses with a 1 month break between. At both read-outs the cases were shown at lower dose or standard dose level to quantify the influence of both variables (eyewear vs. image quality) on nodule sensitivity.

RESULTS

The sensitivity of CT for lung nodules increased significantly using Gunnar eyewear for two readers and insignificantly for two other readers. Over all, the mean sensitivity of all radiologist raised significantly from 50% to 53%, using the glasses (P value = 0.034). In contrast, sensitivity for lung nodules was not significantly affected by lowering the image quality from 150 to 40 ref mAs. The average sensitivity was 52% at low dose level, that was even 0.7% higher than at standard dose level (P value = 0.40). The strongest impact on sensitivity had the factors readers and nodule location (lung segments).

CONCLUSION

Sensitivity for lung nodules was significantly enhanced by Gunnar eyewear (+3%), while lower image quality (40 ref mAs) had no impact on nodule sensitivity. Not using the glasses had a bigger impact on sensitivity than lowering the image quality.

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.

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.