Inter- and intrascanner variability of pulmonary nodule volumetry on low-dose 64-row CT: an anthropomorphic phantom study

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.

Reduced Lung-Cancer Mortality with Volume CT Screening in a Randomized Trial

BACKGROUND

There are limited data from randomized trials regarding whether volume-based, low-dose computed tomographic (CT) screening can reduce lung-cancer mortality among male former and current smokers.

METHODS

A total of 13,195 men (primary analysis) and 2594 women (subgroup analyses) between the ages of 50 and 74 were randomly assigned to undergo CT screening at T0 (baseline), year 1, year 3, and year 5.5 or no screening. We obtained data on cancer diagnosis and the date and cause of death through linkages with national registries in the Netherlands and Belgium, and a review committee confirmed lung cancer as the cause of death when possible. A minimum follow-up of 10 years until December 31, 2015, was completed for all participants.

RESULTS

Among men, the average adherence to CT screening was 90.0%. On average, 9.2% of the screened participants underwent at least one additional CT scan (initially indeterminate). The overall referral rate for suspicious nodules was 2.1%. At 10 years of follow-up, the incidence of lung cancer was 5.58 cases per 1000 person-years in the screening group and 4.91 cases per 1000 person-years in the control group; lung-cancer mortality was 2.50 deaths per 1000 person-years and 3.30 deaths per 1000 person-years, respectively. The cumulative rate ratio for death from lung cancer at 10 years was 0.76 (95% confidence interval [CI], 0.61 to 0.94; P = 0.01) in the screening group as compared with the control group, similar to the values at years 8 and 9. Among women, the rate ratio was 0.67 (95% CI, 0.38 to 1.14) at 10 years of follow-up, with values of 0.41 to 0.52 in years 7 through 9.

CONCLUSIONS

In this trial involving high-risk persons, lung-cancer mortality was significantly lower among those who underwent volume CT screening than among those who underwent no screening. There were low rates of follow-up procedures for results suggestive of lung cancer. (Funded by the Netherlands Organization of Health Research and Development and others; NELSON Netherlands Trial Register number, NL580.).

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.

Variability in Heart-to-Mediastinum Ratio from Planar 123I-MIBG Images of a Thorax Phantom for 6 Common γ-Camera Models

A heart-to-mediastinum (H/M) ratio of 1.6 or greater on planar ¹²³I-iobenguane (¹²³I-MIBG) images identifies heart failure patients at low risk of experiencing an adverse cardiac event. This phase-4 study used standardized phantoms to assess the intercamera, intracamera, and interhead variability in H/M ratio determinations from planar cardiac ¹²³I-MIBG imaging using commercially available, dual-head γ-cameras. Methods: A fillable thorax phantom was developed to simulate the typical uptake of ¹²³I-MIBG. The phantom had a nominal H/M ratio of 1.6 on the reference camera. Commercial cameras used in the study were dual-head and capable of 90° configuration for cardiac imaging. The target sample size was 8 units (examples) per camera model. Two imaging technologists independently analyzed planar images of simulated ¹²³I-MIBG uptake from the thorax phantom. H/M was the ratio of the average counts per pixel of the heart and mediastinum regions of interest. The primary endpoint, intercamera variability in H/M ratio from head 1, was determined for each camera model via comparison with the H/M ratio on the reference camera. Only cameras with at least 8 units tested (n ≥ 8) were included in the primary analysis. Intracamera and interhead variability in the H/M ratio were also evaluated. Results: Nine camera models were studied. The mean H/M ratio ranged from 1.342 to 1.677. The primary analysis (6 camera models) using a mixed-model, repeated-measures analysis showed no significant difference in H/M ratio between any camera model and the reference camera. Intracamera variability (head 1) in the H/M ratio among camera models with 8 units or more was high, with SDs ranging from 0.0455 to 0.1193. Interhead variability was low (SDs of the interhead difference, 0.017-0.074). Conclusion: Commonly used γ-cameras produced H/M ratios from simulated ¹²³I-MIBG phantom images that were not significantly different from those on the reference camera. This finding indicates that the results of previous clinical trials of ¹²³I-MIBG, involving many different clinical sites and camera models, are valid. The assessment of the performance of a given camera unit using an ¹²³I planar phantom before H/M results from ¹²³I-MIBG imaging are used for classifying risk in heart failure patients is encouraged.

Techniques for virtual lung nodule insertion: volumetric and morphometric comparison of projection-based and image-based methods for quantitative CT

Virtual nodule insertion paves the way towards the development of standardized databases of hybrid CT images with known lesions. The purpose of this study was to assess three methods (an established and two newly developed techniques) for inserting virtual lung nodules into CT images. Assessment was done by comparing virtual nodule volume and shape to the CT-derived volume and shape of synthetic nodules. 24 synthetic nodules (three sizes, four morphologies, two repeats) were physically inserted into the lung cavity of an anthropomorphic chest phantom (KYOTO KAGAKU). The phantom was imaged with and without nodules on a commercial CT scanner (SOMATOM Definition Flash, Siemens) using a standard thoracic CT protocol at two dose levels (1.4 and 22 mGy CTDI_vol). Raw projection data were saved and reconstructed with filtered back-projection and sinogram affirmed iterative reconstruction (SAFIRE, strength 5) at 0.6 mm slice thickness. Corresponding 3D idealized, virtual nodule models were co-registered with the CT images to determine each nodule's location and orientation. Virtual nodules were voxelized, partial volume corrected, and inserted into nodule-free CT data (accounting for system imaging physics) using two methods: projection-based Technique A, and image-based Technique B. Also a third Technique C based on cropping a region of interest from the acquired image of the real nodule and blending it into the nodule-free image was tested. Nodule volumes were measured using a commercial segmentation tool (iNtuition, TeraRecon, Inc.) and deformation was assessed using the Hausdorff distance. Nodule volumes and deformations were compared between the idealized, CT-derived and virtual nodules using a linear mixed effects regression model which utilized the mean, standard deviation, and coefficient of variation ([Formula: see text], [Formula: see text] and [Formula: see text] of the regional Hausdorff distance. Overall, there was a close concordance between the volumes of the CT-derived and virtual nodules. Percent differences between them were less than 3% for all insertion techniques and were not statistically significant in most cases. Correlation coefficient values were greater than 0.97. The deformation according to the Hausdorff distance was also similar between the CT-derived and virtual nodules with minimal statistical significance in the ([Formula: see text]) for Techniques A, B, and C. This study shows that both projection-based and image-based nodule insertion techniques yield realistic nodule renderings with statistical similarity to the synthetic nodules with respect to nodule volume and deformation. These techniques could be used to create a database of hybrid CT images containing nodules of known size, location and morphology.

Advanced imaging tools in pulmonary nodule detection and surveillance

Lung cancer is a leading cause of death worldwide. The National Lung Screening Trial has demonstrated that lung cancer screening can reduce lung cancer specific and all cause mortality. With approval of national coverage for lung cancer screening, it is expected that an increase in exams related to pulmonary nodule detection and surveillance will ensue. Advanced imaging technologies for nodule detection and surveillance will be more important than ever. While computed tomography (CT) remains the modality of choice, other emerging modalities such as magnetic resonance imaging provides viable alternatives to CT.

Inter-Method Performance Study of Tumor Volumetry Assessment on Computed Tomography Test-Retest Data

RATIONALE AND OBJECTIVES

Tumor volume change has potential as a biomarker for diagnosis, therapy planning, and treatment response. Precision was evaluated and compared among semiautomated lung tumor volume measurement algorithms from clinical thoracic computed tomography data sets. The results inform approaches and testing requirements for establishing conformance with the Quantitative Imaging Biomarker Alliance (QIBA) Computed Tomography Volumetry Profile.

MATERIALS AND METHODS

Industry and academic groups participated in a challenge study. Intra-algorithm repeatability and inter-algorithm reproducibility were estimated. Relative magnitudes of various sources of variability were estimated using a linear mixed effects model. Segmentation boundaries were compared to provide a basis on which to optimize algorithm performance for developers.

RESULTS

Intra-algorithm repeatability ranged from 13% (best performing) to 100% (least performing), with most algorithms demonstrating improved repeatability as the tumor size increased. Inter-algorithm reproducibility was determined in three partitions and was found to be 58% for the four best performing groups, 70% for the set of groups meeting repeatability requirements, and 84% when all groups but the least performer were included. The best performing partition performed markedly better on tumors with equivalent diameters greater than 40 mm. Larger tumors benefitted by human editing but smaller tumors did not. One-fifth to one-half of the total variability came from sources independent of the algorithms. Segmentation boundaries differed substantially, not ony in overall volume but also in detail.

CONCLUSIONS

Nine of the 12 participating algorithms pass precision requirements similar to what is indicated in the QIBA Profile, with the caveat that the present study was not designed to explicitly evaluate algorithm profile conformance. Change in tumor volume can be measured with confidence to within ±14% using any of these nine algorithms on tumor sizes greater than 10 mm. No partition of the algorithms was able to meet the QIBA requirements for interchangeability down to 10 mm, although the partition comprising best performing algorithms did meet this requirement for a tumor size of greater than approximately 40 mm.

Reproducibility of Volumetric Computed Tomography of Stable Small Pulmonary Nodules with Implications on Estimated Growth Rate and Optimal Scan Interval

Purpose

To use clinically measured reproducibility of volumetric CT (vCT) of lung nodules to estimate error in nodule growth rate in order to determine optimal scan interval for patient follow-up.

Methods

We performed quantitative vCT on 89 stable non-calcified nodules and 49 calcified nodules measuring 3–13 mm diameter in 71 patients who underwent 3–9 repeat vCT studies for clinical evaluation of pulmonary nodules. Calculated volume standard deviation as a function of mean nodule volume was used to compute error in estimated growth rate. This error was then used to determine the optimal patient follow-up scan interval while fixing the false positive rate at 5%.

Results

Linear regression of nodule volume standard deviation versus the mean nodule volume for stable non-calcified nodules yielded a slope of 0.057±0.002 (r² = 0.79, p<0.001). For calcified stable nodules, the regression slope was 0.052±0.005 (r² = 0.65, p = 0.03). Using this with the error propagation formula, the optimal patient follow-up scan interval was calculated to be 81 days, independent of initial nodule volume.

Conclusions

Reproducibility of vCT is excellent, and the standard error is proportional to the mean calculated nodule volume for the range of nodules examined. This relationship constrains statistical certainty of vCT calculated doubling times and results in an optimal scan interval that is independent of the initial nodule volume.

Lung cancer imaging

Lung cancer remains the leading cause of cancer-related deaths in the US. Imaging plays an important role in the diagnosis, staging, and follow-up evaluation of patients with lung cancer. With recent advances in technology, it is important to update and standardize the radiological practices in lung cancer evaluation. In this article, the authors review the main clinical applications of different imaging modalities and the most common radiological presentations of lung cancer.