Pulmonary Nodules Detected at Lung Cancer Screening: Interobserver Variability of Semiautomated Volume Measurements

Volumetric Analysis: Effect on Diagnosis and Management of Indeterminate Solid Pulmonary Nodules in Routine Clinical Practice

OBJECTIVE

To evaluate the effect of volumetric analysis on the diagnosis and management of indeterminate solid pulmonary nodules in routine clinical practice.

METHODS

This was a retrospective study with 107 computed tomography (CT) cases of solid pulmonary nodules (range, 6-15 mm), 57 pathology-proven malignancies (lung cancer, n = 34; metastasis, n = 23), and 50 benign nodules. Nodules were evaluated on a total of 309 CT scans (average number of CTs/nodule, 2.9 [range, 2-7]). CT scans were from multiple institutions with variable technique. Nine radiologists (attendings, n = 3; fellows, n = 3; residents, n = 3) were asked their level of suspicion for malignancy (low/moderate or high) and management recommendation (no follow-up, CT follow-up, or care escalation) for baseline and follow-up studies first without and then with volumetric analysis data. Effect of volumetry on diagnosis and management was assessed by generalized linear and logistic regression models.

RESULTS

Volumetric analysis improved sensitivity (P = 0.009) and allowed earlier recognition (P < 0.05) of malignant nodules. Attending radiologists showed higher sensitivity in recognition of malignant nodules (P = 0.03) and recommendation of care escalation (P < 0.001) compared with trainees. Volumetric analysis altered management of high suspicion nodules only in the fellow group (P = 0.008). κ Statistics for suspicion for malignancy and recommended management were fair to substantial (0.38-0.66) and fair to moderate (0.33-0.50). Volumetric analysis improved interobserver variability for identification of nodule malignancy from 0.52 to 0.66 (P = 0.004) only on the second follow-up study.

CONCLUSIONS

Volumetric analysis of indeterminate solid pulmonary nodules in routine clinical practice can result in improved sensitivity and earlier identification of malignant nodules. The effect of volumetric analysis on management recommendations is variable and influenced by reader experience.

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.

The Role of 3D Printing in Treatment Planning of Spine and Sacral Tumors

Three-dimensional (3D) printing technology has proven to have many advantages in spine and sacrum surgery. 3D printing allows the manufacturing of life-size patient-specific anatomic and pathologic models to improve preoperative understanding of patient anatomy and pathology. Additionally, virtual surgical planning using medical computer-aided design software has enabled surgeons to create patient-specific surgical plans and simulate procedures in a virtual environment. This has resulted in reduced operative times, decreased complications, and improved patient outcomes. Combined with new surgical techniques, 3D-printed custom medical devices and instruments using titanium and biocompatible resins and polyamides have allowed innovative reconstructions.

Comparability of Pulmonary Nodule Size Measurements among Different Scanners and Protocols: Should Diameter Be Favorized over Volume?

BACKGROUND

To assess the impact of the lung cancer screening protocol recommended by the European Society of Thoracic Imaging (ESTI) on nodule diameter, volume, and density throughout different computed tomography (CT) scanners.

METHODS

An anthropomorphic chest phantom containing fourteen different-sized (range 3-12 mm) and CT-attenuated (100 HU, -630 HU and -800 HU, termed as solid, GG1 and GG2) pulmonary nodules was imaged on five CT scanners with institute-specific standard protocols (P_S) and the lung cancer screening protocol recommended by ESTI (ESTI protocol, P_E). Images were reconstructed with filtered back projection (FBP) and iterative reconstruction (REC). Image noise, nodule density and size (diameter/volume) were measured. Absolute percentage errors (APEs) of measurements were calculated.

RESULTS

Using P_E, dosage variance between different scanners tended to decrease compared to P_S, and the mean differences were statistically insignificant (p = 0.48). P_S and P_E(REC) showed significantly less image noise than P_E(FBP) (p < 0.001). The smallest size measurement errors were noted with volumetric measurements in P_E(REC) and highest with diametric measurements in P_E(FBP). Volume performed better than diameter measurements in solid and GG1 nodules (p < 0.001). However, in GG2 nodules, this could not be observed (p = 0.20). Regarding nodule density, REC values were more consistent throughout different scanners and protocols.

CONCLUSION

Considering radiation dose, image noise, nodule size, and density measurements, we fully endorse the ESTI screening protocol including the use of REC. For size measurements, volume should be preferred over diameter.

Establishing a Point-of-Care Virtual Planning and 3D Printing Program

Virtual surgical planning (VSP) and three-dimensional (3D) printing have become a standard of care at our institution, transforming the surgical care of complex patients. Patient-specific, anatomic models and surgical guides are clinically used to improve multidisciplinary communication, presurgical planning, intraoperative guidance, and the patient informed consent. Recent innovations have allowed both VSP and 3D printing to become more accessible to various sized hospital systems. Insourcing such work has several advantages including quicker turnaround times and increased innovation through collaborative multidisciplinary teams. Centralizing 3D printing programs at the point-of-care provides a greater cost-efficient investment for institutions. The following article will detail capital equipment needs, institutional structure, operational personnel, and other considerations necessary in the establishment of a POC manufacturing program.

A segmentation tool for pulmonary nodules in lung cancer screening: Testing and clinical usage

PURPOSE

With the future goal of defining a large dataset based on low-dose CT with labelled pulmonary lesions for lung cancer screening (LCS) research, the aim of this work is to propose and evaluate into a clinical context a tool for semi-automatic segmentation able to facilitate the process of labels collection from a LCS study (COSMOS, Continuous Observation of SMOking Subjects).

METHODS

Considering a preliminary set of manual annotations, a segmentation model based on a 2D-Unet was trained from scratch. Contour quality of the final 2D-Unet was assessed on an internal test set of manual annotations and on a subset of the public available LIDC dataset used as external test set. The tool for semi-automatic segmentation was then designed integrating the tested model into a Graphical User Interface. According to the opinion of two clinical users, the percentage of lesions properly contoured through the tool was quantified (Acceptance Rate, AR). The variability between segmentations derived by the two readers was estimated as mean percentage of difference (MPD) between the two sets of volumes and comparing the likelihood of malignancy derived from Volume Doubling Time (VDT).

RESULTS

Performance in test sets were found similar (DICE ~ 0.75(0.15)). Accordingly, a good mean AR (80.1%) resulted from the two readers. Variability in terms of MPD was equal to 23.6% while 2.7% was the VDTs percentage of disagreement.

CONCLUSIONS

A semi-automatic segmentation tool was developed and its applicability evaluated into a clinical context demonstrating the efficacy of the tool in facilitating the collection of labelled data.

Lung-RADS Version 1.1: Challenges and a Look Ahead, From the AJR Special Series on Radiology Reporting and Data Systems

In 2014, the American College of Radiology (ACR) created Lung-RADS 1.0. The system was updated to Lung-RADS 1.1 in 2019, and further updates are anticipated as additional data become available. Lung-RADS provides a common lexicon and standardized nodule follow-up management paradigm for use when reporting lung cancer screening (LCS) low-dose CT (LDCT) chest examinations and serves as a quality assurance and outcome monitoring tool. The use of Lung-RADS is intended to improve LCS performance and lead to better patient outcomes. To date, the ACR's Lung Cancer Screening Registry is the only LCS registry approved by the Centers for Medicare & Medicaid Services and requires the use of Lung-RADS categories for reimbursement. Numerous challenges have emerged regarding the use of Lung-RADS in clinical practice, including the timing of return to LCS after planned follow-up diagnostic evaluation; potential substitution of interval diagnostic CT for future LDCT; role of volumetric analysis in assessing nodule size; assessment of nodule growth; assessment of cavitary, subpleural, and category 4X nodules; and variability in reporting of the S modifier. This article highlights the major updates between versions 1.0 and 1.1 of Lung-RADS, describes the system's ongoing challenges, and summarizes current evidence and recommendations.

Radiologists and Clinical Trials: Part 1 The Truth About Reader Disagreements

The debate over human visual perception and how medical images should be interpreted have persisted since X-rays were the only imaging technique available. Concerns over rates of disagreement between expert image readers are associated with much of the clinical research and at times driven by the belief that any image endpoint variability is problematic. The deeper understanding of the reasons, value, and risk of disagreement are somewhat siloed, leading, at times, to costly and risky approaches, especially in clinical trials. Although artificial intelligence promises some relief from mistakes, its routine application for assessing tumors within cancer trials is still an aspiration. Our consortium of international experts in medical imaging for drug development research, the Pharma Imaging Network for Therapeutics and Diagnostics (PINTAD), tapped the collective knowledge of its members to ground expectations, summarize common reasons for reader discordance, identify what factors can be controlled and which actions are likely to be effective in reducing discordance. Reinforced by an exhaustive literature review, our work defines the forces that shape reader variability. This review article aims to produce a singular authoritative resource outlining reader performance's practical realities within cancer trials, whether they occur within a clinical or an independent central review.

The Study of Tumor Volume as a Prognostic Factor in T Staging System for Non-Small Cell Lung Cancer: An Exploratory Study

Background:

This study aimed to evaluate T staging system for non-small cell lung cancer (NSCLC) using tumor volume (TV) and other prognostic factors.

Methods:

This study included 1309 cases. The TV and greatest tumor diameter (GTD) were semi-automatically measured. The receiver operating characteristic (ROC) curves of TV and GTD were used to predict survival. The regression analysis was used to describe the correlation between GTD and TV. Overall survival (OS) was analyzed using the Kaplan-Meier method. Cox’s proportional hazards regression model was applied for multivariate analysis.

Results:

Using the OS in pN0M0 patients (997 cases), we obtained 4 optimal cutoff values and divided all cases into 5 TV groups (V1: TV ≤ 2.80 cm³; V2: TV > 2.80–6.40 cm³; V3: TV > 6.40–12.9 cm³; V4: TV > 12.9–55.01 cm³; V5: TV > 55.01 cm³) with significant OS (P < 0.001). Multivariate analysis showed that age, visceral pleural invasion (VPI), and all TV cutoff points were independent factors of OS (P < 0.05). For V3 and V4 groups, the OS in patients without VPI was better than that in patients with VPI. Using the values of TV, VPI, and N stages, we classified all cases into 5 stages from I to V depending on the OS. The OS in I, II, III, IV, and V stages were 71.3%, 65.5%, 59.8%, 47.7%, and 35.1% respectively (P < 0.001).

Conclusions:

We proposed a new T staging system using TV as the main prognostic descriptor in NSCLC patients, which may provide a better comprehensive clinical value than GTD in clinical applications.

Improved Interobserver Agreement on Lung-RADS Classification of Solid Nodules Using Semiautomated CT Volumetry

Background Classification of lung cancer screening CT scans depends on measurement of lung nodule size. Information about interobserver agreement is limited. Purpose To assess interobserver agreement in the measurements and American College of Radiology Lung CT Screening Reporting and Data System (Lung-RADS) classifications of solid lung nodules detected at lung cancer screening using manual measurements of average diameter and computer-aided semiautomated measurements of average diameter and volume (CT volumetry). Materials and Methods Two radiologists and one radiology resident retrospectively measured lung nodules from screening CT scans obtained between September 2016 and June 2018 with a Lung-RADS (version 1.0) classification of 2, 3, 4A, or 4B in the clinical setting. Average manual diameter and semiautomated computer-aided diameter and volume measurements were converted to the corresponding Lung-RADS categories. Interobserver agreement in raw measurements was assessed using intraclass correlation and Bland-Altman indexes, and interobserver agreement in Lung-RADS classification was assessed using bi-rater κ. Results One hundred twenty patients (mean age, 63 years ± 6 [standard deviation]; 67 women) were evaluated. All manual, semiautomated diameter, and semiautomated volume measurements were obtained by all three readers in 120 of 147 nodules (82%). Intraclass correlation coefficients were greater than or equal to 0.95 for all reader pairs using all measurement methods and were highest using volumetry. Bias and 95% limits of agreement for average diameter were smaller with semiautomated measurements than with manual measurements. κ values across all Lung-RADS classifications were greater than or equal to 0.81, with the lowest being for manual measurements and the highest being for volumetric measurements. Forty-three of 120 (36%) of the nodules were classified into a lower Lung-RADS category on the basis of volumetry compared with using manual diameter measurements by at least one reader, whereas the reverse occurred for four of 120 (3%) of the nodules. Conclusion Interobserver agreement was high with manual diameter measurements and increased with semiautomated CT volumetric measurements. Semiautomated CT volumetry enabled classification of more nodules into lower Lung CT Screening Reporting and Data System categories than manual or semiautomated diameter measurements. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Nishino in this issue.

Tumor volume is more reliable to predict nodal metastasis in non-small cell lung cancer of 3.0 cm or less in the greatest tumor diameter

Background

In this study, we sought to evaluate the correlation between TV, GTD, and lymph node metastases in NSCLC patients with tumors of GTD ≤ 3.0 cm.

Methods

We retrospectively analyzed the characteristics of clinicopathologic variables for lymph node involvement in 285 NSCLC patients with tumors of GTD ≤ 3.0 cm who accepted curative surgical resection. The TVs were semi-automatically measured by a software, and optimal cutoff points were obtained using the X-tile software. The relationship between GTD and TV were described using non-linear regression. The correlation between GTD, TV, and N stages was analyzed using the Pearson correlation coefficient. The one-way ANOVA was used to compare the GTD and TV of different lymph node stage groups.

Results

The relationship between GTD and TV accorded with the exponential growth model: y = 0.113e^1.455x (y = TV, x = GTD). TV for patients with node metastases (4.78 cm³) was significantly greater than those without metastases (3.57 cm³) (P < 0.001). However, there were no obvious GTD differences in cases with or without lymph node metastases (P = 0.054). We divided all cases into three TV groups using the two cutoff values (0.9 cm³ and 3.9 cm³), and there was an obvious difference in the lymphatic involvement rate between the groups (P < 0.001). The tendency to metastasize was greater with higher TV especially when the TV was > 0.9–14.2 cm³ (P = 0.010).

Conclusions

For NSCLC tumors with GTD ≤ 3.0 cm, TV is a more sensitive marker than GTD in predicting the positive lymph node metastases. The likelihood for metastasis increases with an increasing TV especially when GTD is > 2.0–3.0 cm.

Low-Dose CT Screening for Lung Cancer: Evidence from 2 Decades of Study

Lung cancer remains the overwhelmingly greatest cause of cancer death in the United States, accounting for more annual deaths than breast, prostate, and colon cancer combined. Accumulated evidence since the mid to late 1990s, however, indicates that low-dose CT screening of high-risk patients enables detection of lung cancer at an early stage and can reduce the risk of dying from lung cancer. CT screening is now a recommended clinical service in the United States, subject to guidelines and reimbursement requirements intended to standardize practice and optimize the balance of benefits and risks. In this review, the evidence on the effectiveness of CT screening will be summarized and the current guidelines and standards will be described in the context of knowledge gained from lung cancer screening studies. In addition, an overview of the potential advances that may improve CT screening will be presented, and the need to better understand the performance in clinical practice outside of the research trial setting will be discussed. © RSNA, 2020.

Intervention to Reduce Interobserver Variability in Computed Tomographic Measurement of Cancer Lesions Among Experienced Radiologists

While a growing number of research studies have reported the inter-observer variability in computed tomographic (CT) measurements, there are very few interventional studies performed. We aimed to assess whether a peer benchmarking intervention tool may have an influence on reducing interobserver variability in CT measurements and identify possible barriers to the intervention. In this retrospective study, 13 board-certified radiologists repeatedly reviewed 10 CT image sets of lung lesions and hepatic metastases during 3 noncontiguous time periods (T1, T2, T3). Each preselected case contained normal anatomy cephalad and caudal to the lesion of interest. Lesion size measurement under RECISTS 1.1 guidelines, choice of CT slice, and time spent on measurement were captured. Prior to their final measurements, the participants were exposed to the intervention designed to reduce the number of measurements deviating from the median. Chi-square test was performed to identify radiologist-dependent factors associated with the variability. The percent of deviating measurements during T1 and T2 were 20.0% and 23.1%, respectively. There was no statistically significant change in the number of deviating measurements upon the presentation of the intervention despite the decrease in percent from 23.1% to 17.7%. The identified barriers to the intervention include clinical disagreements among radiologists. Specifically, the inter-observer variability was associated with the controversy over the choice of CT image slice (P = 0.045) and selection of start-point, axis, and end-point (P = 0.011). Clinical disagreements rather than random errors were barriers to reducing interobserver variability in CT measurement among experienced radiologists. Future interventions could aim to resolve the disagreement in an interactive approach.

Tumor Volume Is Better Than Diameter for Predicting the Prognosis of Patients with Early-Stage Non-small Cell Lung Cancer

BACKGROUND

This study aimed to investigate whether tumor volume (TV) is better than diameter for predicting the prognosis of patients with early-stage non-small cell lung cancer (NSCLC) after complete resection.

METHODS

This study retrospectively reviewed the clinicopathologic characteristics of 274 patients with early-stage NSCLC who had received pretreatment computed tomography (CT) scans and complete resection. TV was semi-automatically measured from CT scans using an imaging software program. The optimal cutoff of TV was determined by X-tile software. Disease-free survival (DFS) and overall survival (OS) were assessed by the Kaplan-Meier method. The prognostic significance of TV and other variables was assessed by Cox proportional hazards regression analysis.

RESULTS

Using 3.046 cm³ and 8.078 cm³ as optimal cutoff values of TV, the patients were separated into three groups. A larger TV was significantly associated with poor DFS and OS in the multivariable analysis. Kaplan-Meier curves of DFS and OS showed significant differences on the basis of TV among patients with stage 1a disease, greatest tumor diameter (GTD) of 2 cm or smaller, and GTD of 2-3 cm, respectively. Using two TV cutoff points, three categories of TV were created. In 54 cases (19.7%), patients migrated from the GTD categories of 2 cm or smaller, 2-3 cm, and larger than 3 cm into the TV categories of 3.046 cm³ or smaller, 3.046-8.078 cm³, and larger than 8.078 cm³.

CONCLUSION

TV is an independent prognostic factor of DFS and OS for early-stage NSCLC. The findings show that TV is better than GTD for predicting the prognosis of patients with early-stage NSCLC.

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.

Automatic Lung Segmentation With Juxta-Pleural Nodule Identification Using Active Contour Model and Bayesian Approach

OBJECTIVE

chest computed tomography (CT) images and their quantitative analyses have become increasingly important for a variety of purposes, including lung parenchyma density analysis, airway analysis, diaphragm mechanics analysis, and nodule detection for cancer screening. Lung segmentation is an important prerequisite step for automatic image analysis. We propose a novel lung segmentation method to minimize the juxta-pleural nodule issue, a notorious challenge in the applications.

METHOD

we initially used the Chan-Vese (CV) model for active lung contours and adopted a Bayesian approach based on the CV model results, which predicts the lung image based on the segmented lung contour in the previous frame image or neighboring upper frame image. Among the resultant juxta-pleural nodule candidates, false positives were eliminated through concave points detection and circle/ellipse Hough transform. Finally, the lung contour was modified by adding the final nodule candidates to the area of the CV model results.

RESULTS

to evaluate the proposed method, we collected chest CT digital imaging and communications in medicine images of 84 anonymous subjects, including 42 subjects with juxta-pleural nodules. There were 16 873 images in total. Among the images, 314 included juxta-pleural nodules. Our method exhibited a disc similarity coefficient of 0.9809, modified hausdorff distance of 0.4806, sensitivity of 0.9785, specificity of 0.9981, accuracy of 0.9964, and juxta-pleural nodule detection rate of 96%. It outperformed existing methods, such as the CV model used alone, the normalized CV model, and the snake algorithm. Clinical impact: the high accuracy with the juxta-pleural nodule detection in the lung segmentation can be beneficial for any computer aided diagnosis system that uses lung segmentation as an initial step.

Evaluation of T categories for pure ground-glass nodules with semi-automatic volumetry: is mass a better predictor of invasive part size than other volumetric parameters?

OBJECTIVES

This study aimed to investigate the diagnostic advantage of nodule mass in differentiating invasive pulmonary adenocarcinomas (IPAs) among pure ground-glass nodules (pGGNs) over other volumetric measurements. Another aim of this study was to analyse the correlation between volumetric measurements on computed tomography (CT) scans and the pathological invasive component size.

METHODS

This Institutional Review Board-approved retrospective study included 117 patients (men:women = 53:64; mean age, 57.3 years) with 117 pGGNs. Semi-automatic segmentation was performed for all nodules, and volumetric measurements, such as nodule volume, attenuation, mass, two-dimensional (2D) average diameter and three-dimensional (3D) longest diameter, were obtained. Receiver operating characteristic (ROC) curve analyses were performed to evaluate the diagnostic performances of the volumetric parameters in discriminating IPAs. Spearman correlation coefficients were calculated between the volumetric measurements and the invasive component size.

RESULTS

Area under the ROC curve for mass was 0.792 (95% CI, 0.691-0.872) in non-enhanced CT and 0.730 (95% CI, 0.607-0.832) in contrast-enhanced CT. Nodule mass was not superior to 2D average diameter for the differentiation of IPAs in both non-enhanced (0.792 vs 0.780; p = 0.501) CT and contrast-enhanced CT scans (0.730 vs 0.700; p = 0.319). The correlation between the volumetric measurements (mass, 3D longest diameter and 2D average diameter) and the invasive component size was moderate (Spearman's rho, 0.401-0.422) in non-enhanced CT and weak (Spearman's rho, 0.276-0.310) in contrast-enhanced CT.

CONCLUSIONS

Nodule mass measurement had no strength over other volumetric parameters for the prediction of pathological invasiveness in the diagnosis of pGGNs.

KEY POINTS

• Mass is not superior to other volumetric measurements for the diagnosis of pure ground-glass nodules. • Mass and two-dimensional average diameter exhibited comparable performance for the discrimination of invasive adenocarcinomas among pure ground-glass nodules. • The diagnostic performance of volumetric measurements was lower on contrast-enhanced CT scans. • The correlation between the volumetric measurements and the invasive component size was moderate on non-enhanced CT scans and weak on contrast-enhanced CT scans.

Pulmonary Nodule Management in Lung Cancer Screening: A Pictorial Review of Lung-RADS Version 1.0

The number of screening-detected lung nodules is expected to increase as low-dose computed tomography screening is implemented nationally. Standardized guidelines for image acquisition, interpretation, and screen-detected nodule workup are essential to ensure a high standard of medical care and that lung cancer screening is implemented safely and cost effectively. In this article, we review the current guidelines for pulmonary nodule management in the lung cancer screening setting.

Vessel suppressed chest Computed Tomography for semi-automated volumetric measurements of solid pulmonary nodules

OBJECTIVE

To evaluate whether vessel-suppressed computed tomography (VSCT) can be reliably used for semi-automated volumetric measurements of solid pulmonary nodules, as compared to standard CT (SCT) MATERIAL AND METHODS: Ninety-three SCT were elaborated by dedicated software (ClearRead CT, Riverain Technologies, Miamisburg, OH, USA), that allows subtracting vessels from lung parenchyma. Semi-automated volumetric measurements of 65 solid nodules were compared between SCT and VSCT. The measurements were repeated by two readers. For each solid nodule, volume measured on SCT by Reader 1 and Reader 2 was averaged and the average volume between readers acted as standard of reference value. Concordance between measurements was assessed using Lin's Concordance Correlation Coefficient (CCC). Limits of agreement (LoA) between readers and CT datasets were evaluated.

RESULTS

Standard of reference nodule volume ranged from 13 to 366 mm³. The mean overestimation between readers was 3 mm³ and 2.9 mm³ on SCT and VSCT, respectively. Semi-automated volumetric measurements on VSCT showed substantial agreement with the standard of reference (Lin's CCC = 0.990 for Reader 1; 0.985 for Reader 2). The upper and lower LoA between readers' measurements were (16.3, -22.4 mm³) and (15.5, -21.4 mm³) for SCT and VSCT, respectively.

CONCLUSIONS

VSCT datasets are feasible for the measurements of solid nodules, showing an almost perfect concordance between readers and with measurements on SCT.

Evaluation of the 95% limits of agreement of the volumes of 5-year clinically stable solid nodules for the development of a follow-up system for indeterminate solid nodules in CT lung cancer screening

Background

This study sought to evaluate the 95% limits of agreement of the volumes of 5-year clinically stable solid nodules for the development of a follow-up system for indeterminate solid nodules.

Methods

The volumes of 226 solid nodules that had been clinically stable for 5 years were measured in 186 patients (53 female never-smokers, 36 male never-smokers, 51 males with <30 pack-years, and 46 males with ≥30 pack-years) using a three-dimensional semiautomated method. Volume changes were evaluated using three methods: percent change, proportional change and growth rate. The 95% limits of agreement were evaluated using the Bland-Altman method.

Results

The 95% limits of agreement were as follows: range of percent change, from ±34.5% to ±37.8%; range of proportional change, from ±34.1% to ±36.8%; and range of growth rate, from ±39.2% to ±47.4%. Percent change-based, proportional change-based, and growth rate-based diagnoses of an increase or decrease in ten solid nodules were made at a mean of 302±402, 367±455, and 329±496 days, respectively, compared with a clinical diagnosis made at 809±616 days (P<0.05).

Conclusions

The 95% limits of agreement for volume change in 5-year stable solid nodules may enable the detection of an increase or decrease in the solid nodule at an earlier stage than that enabled by a clinical diagnosis, possibly contributing to the development of a follow-up system for reducing the number of additional Computed tomography (CT) scans performed during the follow-up period.

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.

Influence of lung nodule margin on volume- and diameter-based reader variability in CT lung cancer screening

OBJECTIVE:

To evaluate the influence of nodule margin on inter- and intrareader variability in manual diameter measurements and semi-automatic volume measurements of solid nodules detected in low-dose CT lung cancer screening.

METHODS:

25 nodules of each morphological category (smooth, lobulated, spiculated and irregular) were randomly selected from 93 participants of the Dutch-Belgian Randomized Lung Cancer Screening Trial (NELSON). Semi-automatic volume measurements were performed using Syngo LungCARE® software (Version Somaris/5 VB10A-W, Siemens, Forchheim, Germany). Three radiologists independently measured mean diameters manually. Impact of nodule margin on interreader variability was evaluated based on systematic error and 95% limits of agreement. Interreader variability was compared with the nodule growth cut-off as used in Lung CT Screening Reporting and Data System (LungRADS; +1.5-mm diameter) and the Dutch-Belgian Randomized Lung Cancer Screening Trial(acronym: NELSON) /British Thoracic Society (+25% volume).

RESULTS:

For manual diameter measurements, a significant systematic error (up to 1.2 mm) between readers was found in all morphological categories. For semi-automatic volume measurements, no statistically significant systematic error was found. The interreader variability in mean diameter measurements exceeded the 1.5-mm cut-off for nodule growth for all morphological categories [smooth: ±1.9 mm (+27%), lobulated: ±2.0 mm (+33%), spiculated: ±3.5 mm (+133%), irregular: ±4.5 mm (+200%)]. The 25% vol growth cut-off was exceeded slightly for spiculated [28% (+12%)] and irregular [27% (+8%)] nodules.

CONCLUSION:

Lung nodule sizing based on manual diameter measurement is affected by nodule margin. Interreader variability increases especially for nodules with spiculated and irregular margins, and causes substantial misclassification of nodule growth. This effect is almost neglectable for semi-automated volume measurements. Semi-automatic volume measurements are superior for both size and growth determination of pulmonary nodules.

ADVANCES IN KNOWLEDGE:

Nodule assessment based on manual diameter measurements is susceptible to nodule margin. This effect is almost neglectable for semi-automated volume measurements. The larger interreader variability for manual diameter measurement results in inaccurate lung nodule growth detection and size classification.

[Growth Evaluation of Pulmonary Nodules on Chest CT]

对肺结节行计算机断层扫描（computed tomography, CT）随访并确定结节生长特性是临床针对不定性肺结节常采用的策略。依据肿瘤生长指数模型，常采用体积或质量倍增时间量化结节的生长速率。本文拟对肺癌的指数生长模型、肺结节生长量化评价的方法学、不同类型肺结节的生长特性进行综述。

Variation in Screening CT-Detected Nodule Volumetry as a Function of Size

OBJECTIVE

The objective of this study is to evaluate measurement variability in volumetric assessment of pulmonary nodules on low-dose CT images with a view toward determining how this variability is influenced by nodule size.

MATERIALS AND METHODS

A large CT screening database was reviewed to identify solid pulmonary nodules that had remained stable in size on the basis of findings from at least three scans obtained over a 2-year period. Two software packages (Lung VCAR and syngo.via) were used to assess the nodule volume on the two most recent CT scans, which were obtained at a slice thickness of 0.625 mm. The percentage of volume change was calculated for each nodule. The SD of the percentage of volume change was determined for nodules in each of the following nodule diameter size categories: less than 4 mm, 4-5 mm, 6-9 mm, and 10 mm or larger. The diameter was the mean of the length and width in the CT image that represented the largest cross-sectional area of the nodule.

RESULTS

The 171 stable nodules that were identified in 117 CT screening participants (median age, 61 years) ranged in size from 2.2 to 18.7 mm. The time between acquisition of the first and last CT images ranged from 3.7 to 17.8 years (median, 11.5 years). For each of the four categories of diameter size (< 4, 4-5, 6-9, and ≥ 10 mm), the SD of the percentage of volume change was 20.4%, 17.7%, 14.6%, and 3.7%, with the use of Lung VCAR, and 59.5%, 24.3%, 9.1%, and 6.2%, with the use of syngo.via, respectively. The SD decreased with increasing nodule diameter, with the use of both software packages.

CONCLUSION

Measurement variability decreased with increasing nodule diameter for both software packages and was different between the two software packages.

Volume versus diameter assessment of small pulmonary nodules in CT lung cancer screening

Currently, lung cancer screening by low-dose chest CT is implemented in the United States for high-risk persons. A disadvantage of lung cancer screening is the large number of small-to-intermediate sized lung nodules, detected in around 50% of all participants, the large majority being benign. Accurate estimation of nodule size and growth is essential in the classification of lung nodules. Currently, manual diameter measurements are the standard for lung cancer screening programs and routine clinical care. However, European screening studies using semi-automated volume measurements have shown higher accuracy and reproducibility compared to diameter measurements. In addition to this, with the optimization of CT scan techniques and reconstruction parameters, as well as advances in segmentation software, the accuracy of nodule volume measurement can be improved even further. The positive results of previous studies on volume and diameter measurements of lung nodules suggest that manual measurements of nodule diameter may be replaced by semi-automated volume measurements in the (near) future.

Systematic analysis of measurement variability in lung cancer with multidetector computed tomography

OBJECTIVE:

To systematically analyze the nature of measurement variability in lung cancer with multidetector computed tomography (CT) scans.

METHODS:

Multidetector CT scans of 67 lung cancer patients were analyzed. Unidimensional (Response Evaluation Criteria in Solid Tumor criteria), bidimensional (World Health Organization criteria), and volumetric measurements were performed independently by ten radiologists and were repeated after at least 5 months. Repeatability and reproducibility measurement variations were estimated by analyzing reliability, agreement, variation coefficient, and misclassification statistically. The relationship of measurement variability with various sources was also analyzed.

RESULTS:

Analyses of 69 lung tumors with an average size of 1.1–12.1 cm (mean 4.3 cm) indicated that volumetric technique had the minimum measurement variability compared to the unidimensional or bidimensional technique. Tumor characteristics (object effect) could be the primary factor to influence measurement variability while the effect of raters (subjective effect) was faint. Segmentation and size in tumor characteristics were associated with measurement variability, and some mathematical function was established between the volumetric variability and tumor size.

CONCLUSION:

Volumetric technique has the minimum variability in measuring lung cancer, and measurement variability is associated with tumor size by nonlinear mathematical function.

Spectrum of early lung cancer presentation in low-dose screening CT: a pictorial review

The typical presentation of early stage lung cancers on low-dose CT screening are non-calcified pulmonary nodules. However, there is a wide spectrum of unusual focal abnormalities that can be early presentations of lung cancer. These abnormalities include, for example, cancers associated with 'cystic airspaces' or scar-like cancers. The detection of lung cancer with low-dose CT can be affected by the absence of intravenous contrast medium. As a consequence, endobronchial and central lesions can be difficult to recognize, raising the potential for missed cancers. Focal lesions arising within pre-existing lung disease, such as lung fibrosis or apical scars, can also be early lung cancer manifestations and deserve particular consideration as recognition of these lesions may be hindered by the underlying disease. Furthermore, the unpredictable growth rate of lung cancer, which ranges from indolent to aggressive cancers, necessitates attention to the wide spectrum of progression in lung cancer appearance on serial low-dose CT scans. In this pictorial review we discuss the spectrum of early lung cancer presentation in low-dose CT screening, highlighting typical as well as unusual radiological features and the varied growth rates of early lung cancer. Teaching Points • There is a wide spectrum of early presentations of lung cancer on LDCT. • Low radiation dose and the absence of contrast medium injection can affect lung cancer detection. • Lung cancer growth shows various behaviours, ranging from indolent to aggressive cancers. • Familiarity with LDCT technique can improve CT screening effectiveness and avoid missed diagnosis.

The IASLC Lung Cancer Staging Project: Proposals for Coding T Categories for Subsolid Nodules and Assessment of Tumor Size in Part-Solid Tumors in the Forthcoming Eighth Edition of the TNM Classification of Lung Cancer

This article proposes codes for the primary tumor categories of adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) and a uniform way to measure tumor size in part-solid tumors for the eighth edition of the tumor, node, and metastasis classification of lung cancer. In 2011, new entities of AIS, MIA, and lepidic predominant adenocarcinoma were defined, and they were later incorporated into the 2015 World Health Organization classification of lung cancer. To fit these entities into the T component of the staging system, the Tis category is proposed for AIS, with Tis (AIS) specified if it is to be distinguished from squamous cell carcinoma in situ (SCIS), which is to be designated Tis (SCIS). We also propose that MIA be classified as T1mi. Furthermore, the use of the invasive size for T descriptor size follows a recommendation made in three editions of the Union for International Cancer Control tumor, node, and metastasis supplement since 2003. For tumor size, the greatest dimension should be reported both clinically and pathologically. In nonmucinous lung adenocarcinomas, the computed tomography (CT) findings of ground glass versus solid opacities tend to correspond respectively to lepidic versus invasive patterns seen pathologically. However, this correlation is not absolute; so when CT features suggest nonmucinous AIS, MIA, and lepidic predominant adenocarcinoma, the suspected diagnosis and clinical staging should be regarded as a preliminary assessment that is subject to revision after pathologic evaluation of resected specimens. The ability to predict invasive versus noninvasive size on the basis of solid versus ground glass components is not applicable to mucinous AIS, MIA, or invasive mucinous adenocarcinomas because they generally show solid nodules or consolidation on CT.

Changes of lung tumour volume on CT - prediction of the reliability of assessments

Background

For oncological evaluations, quantitative radiology gives clinicians significant insight into patients’ response to therapy. In regard to the Response Evaluation Criteria in Solid Tumours (RECIST), the classification of disease evolution partly consists in applying thresholds to the measurement of the relative change of tumour. In the case of tumour volumetry, response thresholds have not yet been established. This study proposes and validates a model for calculating thresholds for the detection of minimal tumour change when using the volume of pulmonary lesions on CT as imaging biomarker.

Methods

Our work is based on the reliability analysis of tumour volume measurements documented by the Quantitative Imaging Biomarker Alliance. Statistics of measurements were entered into a multi-parametric mathematical model of the relative changes derived from the Geary-Hinkley transformation. The consistency of the model was tested by comparing modelled thresholds against Monte Carlo simulations of tumour volume measurements with additive random error. The model has been validated by repeating measurements on real patient follow ups.

Results

For unchanged tumour volume, relying on a normal distribution of error, the agreement between model and simulations featured a type I error of 5.25 %. Thus, we established that a threshold of 35 % of volume reduction corresponds to a partial response (PR) and a 55 % volume increase corresponds to progressive disease (PD). Changes between −35 and +55 % are categorized as stable disease (SD). Tested on real clinical data, 97.1 % [95.7; 98.0] of assessments fall into the range of variability predicted by our model of confidence interval.

Conclusions

Our study indicates that the Geary Hinkley model, using published statistics, is appropriate to predict response thresholds for the volume of pulmonary lesions on CT.

The 10 Pillars of Lung Cancer Screening: Rationale and Logistics of a Lung Cancer Screening Program

On the basis of the National Lung Screening Trial data released in 2011, the U.S. Preventive Services Task Force made lung cancer screening (LCS) with low-dose computed tomography (CT) a public health recommendation in 2013. The Centers for Medicare and Medicaid Services (CMS) currently reimburse LCS for asymptomatic individuals aged 55-77 years who have a tobacco smoking history of at least 30 pack-years and who are either currently smoking or had quit less than 15 years earlier. Commercial insurers reimburse the cost of LCS for individuals aged 55-80 years with the same smoking history. Effective care for the millions of Americans who qualify for LCS requires an organized step-wise approach. The 10-pillar model reflects the elements required to support a successful LCS program: eligibility, education, examination ordering, image acquisition, image review, communication, referral network, quality improvement, reimbursement, and research frontiers. Examination ordering can be coupled with decision support to ensure that only eligible individuals undergo LCS. Communication of results revolves around the Lung Imaging Reporting and Data System (Lung-RADS) from the American College of Radiology. Lung-RADS is a structured decision-oriented reporting system designed to minimize the rate of false-positive screening examination results. With nodule size and morphology as discriminators, Lung-RADS links nodule management pathways to the variety of nodules present on LCS CT studies. Tracking of patient outcomes is facilitated by a CMS-approved national registry maintained by the American College of Radiology. Online supplemental material is available for this article.

The normal mode analysis shape detection method for automated shape determination of lung nodules

Surface morphology and shape in general are important predictors for the behavior of solid-type lung nodules detected on CT. More broadly, shape analysis is useful in many areas of computer-aided diagnosis and essentially all scientific and engineering disciplines. Automated methods for shape detection have all previously, to the author's knowledge, relied on some sort of geometric measure. I introduce Normal Mode Analysis Shape Detection (NMA-SD), an approach that measures shape indirectly via the motion it would undergo if one imagined the shape to be a pseudomolecule. NMA-SD allows users to visualize internal movements in the imaging object and thereby develop an intuition for which motions are important, and which geometric features give rise to them. This can guide the identification of appropriate classification features to distinguish among classes of interest. I employ normal mode analysis (NMA) to animate pseudomolecules representing simulated lung nodules. Doing so, I am able to assign a testing set of nodules into the classes circular, elliptical, and irregular with roughly 97 % accuracy. This represents a proof-of-principle that one can obtain shape information by treating voxels as pseudoatoms in a pseudomolecule, and analyzing the pseudomolecule's predicted motion.

Statistical issues in the comparison of quantitative imaging biomarker algorithms using pulmonary nodule volume as an example

Quantitative imaging biomarkers are being used increasingly in medicine to diagnose and monitor patients' disease. The computer algorithms that measure quantitative imaging biomarkers have different technical performance characteristics. In this paper we illustrate the appropriate statistical methods for assessing and comparing the bias, precision, and agreement of computer algorithms. We use data from three studies of pulmonary nodules. The first study is a small phantom study used to illustrate metrics for assessing repeatability. The second study is a large phantom study allowing assessment of four algorithms' bias and reproducibility for measuring tumor volume and the change in tumor volume. The third study is a small clinical study of patients whose tumors were measured on two occasions. This study allows a direct assessment of six algorithms' performance for measuring tumor change. With these three examples we compare and contrast study designs and performance metrics, and we illustrate the advantages and limitations of various common statistical methods for quantitative imaging biomarker studies.

The Lung Reporting and Data System (LU-RADS): A Proposal for Computed Tomography Screening

Despite the positive outcome of the recent randomized trial of computed tomography (CT) screening for lung cancer, substantial implementation challenges remain, including the clear reporting of relative risk and suggested workup of screen-detected nodules. Based on current literature, we propose a 6-level Lung-Reporting and Data System (LU-RADS) that classifies screening CTs by the nodule with the highest malignancy risk. As the LU-RADS level increases, the risk of malignancy increases. The LU-RADS level is linked directly to suggested follow-up pathways. Compared with current narrative reporting, this structure should improve communication with patients and clinicians, and provide a data collection framework to facilitate screening program evaluation and radiologist training. In overview, category 1 includes CTs with no nodules and returns the subject to routine screening. Category 2 scans harbor minimal risk, including <5 mm, perifissural, or long-term stable nodules that require no further workup before the next routine screening CT. Category 3 scans contain indeterminate nodules and require CT follow up with the interval dependent on nodule size (small [5-9 mm] or large [≥10 mm] and possibly transient). Category 4 scans are suspicious and are subdivided into 4A, low risk of malignancy; 4B, likely low-grade adenocarcinoma; and 4C, likely malignant. The 4B and 4C nodules have a high likelihood of neoplasm simply based on screening CT features, even if positron emission tomography, needle biopsy, and/or bronchoscopy are negative. Category 5 nodules demonstrate frankly malignant behavior on screening CT, and category 6 scans contain tissue-proven malignancies.

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.

Computed tomography quantitative analysis of components: a new method monitoring the growth of pulmonary nodule

BACKGROUND

The estimation of the growth of solitary pulmonary nodules by using non-invasive methods is increasingly gaining clinical importance for performing the timely adequate treatment of these nodules.

PURPOSE

To evaluate the application value of computed tomography (CT) quantitative analysis of components for dynamic assessment of the growth of solitary pulmonary nodules, and compare this approach with three-dimensional (3D) volumetric measurement of pulmonary nodules.

MATERIAL AND METHODS

The imaging data of 21 patients who had undergone multiple follow-up CT scans for solitary pulmonary nodules were retrospectively analyzed, and the total volume of pulmonary nodules and the percentage change in the total volume of pulmonary nodules after multiple follow-up CT scans were measured using 3D volume measurement software. The volume of solid components in pulmonary nodules was measured using CT quantitative analysis; the percentage change in the volume of solid components across examinations was calculated; and the percentage change in the total volume of pulmonary nodules was compared and contrasted with the percentage change in the volume of solid components in the pulmonary nodules.

RESULTS

All 21 cases were malignant adenocarcinomas. In the 21 cases of malignant nodules, the 3D volumes of the nodules and solid components were both increased, with the percentage change in the volume of the solid components (115.78-418.91%, 130.45 ± 119.48) significantly different from the percentage change in the total volume of the nodules (78.56-105.73% , 42.34 ± 32.17) (P = 0.02).

CONCLUSION

By measuring volume changes in solid components in the nodules, CT quantitative analysis offers more sensitive and earlier evaluation of the dynamic growth of the nodules than measurement of volume changes in the nodules alone.

National lung screening trial: variability in nodule detection rates in chest CT studies

PURPOSE

To characterize the variability in radiologists' interpretations of computed tomography (CT) studies in the National Lung Screening Trial (NLST) (including assessment of false-positive rates [FPRs] and sensitivity), to examine factors that contribute to variability, and to evaluate trade-offs between FPRs and sensitivity among different groups of radiologists.

MATERIALS AND METHODS

The HIPAA-compliant NLST was approved by the institutional review board at each screening center; all participants provided informed consent. NLST radiologists reported overall screening results, nodule-specific findings, and recommendations for diagnostic follow-up. A noncalcified nodule of 4 mm or larger constituted a positive screening result. The FPR was defined as the rate of positive screening examinations in participants without a cancer diagnosis within 1 year. Descriptive analyses and mixed-effects models were utilized. The average odds ratio (OR) for a false-positive result across all pairs of radiologists was used as a measure of variability.

RESULTS

One hundred twelve radiologists at 32 screening centers each interpreted 100 or more NLST CT studies, interpreting 72 160 of 75 126 total NLST CT studies in aggregate. The mean FPR for radiologists was 28.7% ± 13.7 (standard deviation), with a range of 3.8%-69.0%. The model yielded an average OR of 2.49 across all pairs of radiologists and an OR of 1.83 for pairs within the same screening center. Mean FPRs were similar for academic versus nonacademic centers (27.9% and 26.7%, respectively) and for centers inside (25.0%) versus outside (28.7%) the U.S. "histoplasmosis belt." Aggregate sensitivity was 96.5% for radiologists with FPRs higher than the median (27.1%), compared with 91.9% for those with FPRs lower than the median (P = .02).

CONCLUSION

There was substantial variability in radiologists' FPRs. Higher FPRs were associated with modestly higher sensitivity.

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

OBJECTIVE

To assess inter- and intrascanner variability in volumetry of solid pulmonary nodules in an anthropomorphic thoracic phantom using low-dose CT.

METHODS

Five spherical solid artificial nodules [diameters 3, 5, 8, 10 and 12 mm; CT density +100 Hounsfield units (HU)] were randomly placed inside an anthropomorphic thoracic phantom in different combinations. The phantom was examined on two 64-row multidetector CT (64-MDCT) systems (CT-A and CT-B) from different vendors with a low-dose protocol. Each CT examination was performed three times. The CT examinations were evaluated twice by independent blinded observers. Nodule volume was semi-automatically measured by dedicated software. Interscanner variability was evaluated by Bland-Altman analysis and expressed as 95% confidence interval (CI) of relative differences. Intrascanner variability was expressed as 95% CI of relative variation from the mean.

RESULTS

No significant difference in CT-derived volume was found between CT-A and CT-B, except for the 3-mm nodules (p<0.05). The 95% CI of interscanner variability was within ±41.6%, ±18.2% and ±4.9% for 3, 5 and ≥8 mm nodules, respectively. The 95% CI of intrascanner variability was within ±28.6%, ±13.4% and ±2.6% for 3, 5 and ≥8 mm nodules, respectively.

CONCLUSION

Different 64-MDCT scanners in low-dose settings yield good agreement in volumetry of artificial pulmonary nodules between 5 mm and 12 mm in diameter. Inter- and intrascanner variability decreases at a larger nodule size to a maximum of 4.9% for ≥8 mm nodules.

ADVANCES IN KNOWLEDGE

The commonly accepted cut-off of 25% to determine nodule growth has the potential to be reduced for ≥8 mm nodules. This offers the possibility of reducing the interval for repeated CT scans in lung cancer screenings.

The effects of computed tomography with iterative reconstruction on solid pulmonary nodule volume quantification

Background

The objectives of this study were to evaluate the influence of iterative reconstruction (IR) on pulmonary nodule volumetry with chest computed tomography (CT).

Methods

Twenty patients (12 women and 8 men, mean age 61.9, range 32–87) underwent evaluation of pulmonary nodules with a 64-slice CT-scanner. Data were reconstructed using filtered back projection (FBP) and IR (Philips Healthcare, iDose⁴-levels 2, 4 and 6) at similar radiation dose. Volumetric nodule measurements were performed with semi-automatic software on thin slice reconstructions. Only solid pulmonary nodules were measured, no additional selection criteria were used for the nature of nodules. For intra-observer and inter-observer variability, measurements were performed once by one observer and twice by another observer. Algorithms were compared using the concordance correlation-coefficient (p_c) and Friedman-test, and post-hoc analysis with the Wilcoxon-signed ranks-test with Bonferroni-correction (significance-level p<0.017).

Results

Seventy-eight nodules were present including 56 small nodules (volume<200 mm³, diameter<8 mm) and 22 large nodules (volume≥200 mm³, diameter≥8 mm). No significant differences in measured pulmonary nodule volumes between FBP, iDose⁴-levels 2, 4 and 6 were found in both small nodules and large nodules. FBP and iDose⁴-levels 2, 4 and 6 were correlated with p_c-values of 0.98 or higher for both small and large nodules. P_c-values of intra-observer and inter-observer variability were 0.98 or higher.

Conclusions

Measurements of solid pulmonary nodule volume measured with standard-FBP were comparable with IR, regardless of the IR-level and no significant differences between measured volumes of both small and large solid nodules were found.

Low-dose CT: technique, reading methods and image interpretation

The National Lung Cancer Screening Trial has recently demonstrated that screening of high-risk populations with the use of low-dose computed tomography (LDCT) reduces lung cancer mortality^[1]. Based on this encouraging result, the National Comprehensive Cancer Network guidelines recommended LDCT for selected patients at high risk of lung cancer^[2]. This suggests that an increasing number of CT screening examinations will be performed. The LDCT technique is relatively simple but some CT parameters are important and should be accurately defined in order to achieve good diagnostic quality and minimize the delivered dose. In addition, LDCT examinations are not as easy to read as they may initially appear; different approaches and tools are available for nodule detection and measurement. Moreover, the management of positive results can be a complex process and can differ significantly from routine clinical practice. Therefore this paper deals with the LDCT technique, reading methods and interpretation in lung cancer screening, particularly for those radiologists who have little experience of the technique.

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.