Computer-aided volumetry of pulmonary nodules exhibiting ground-glass opacity at MDCT

Total intracranial hemorrhage volume measurement summating all compartments best in traumatic and nontraumatic intracranial bleeding

BACKGROUND AND PURPOSE

The ANNEXA-4 trial measured hemostatic efficacy of andexanet alfa in patients with major bleeding taking factor Xa inhibitors. A proportion of this was traumatic and nontraumatic intracranial bleeding. Different measurements were applied in the trial including volumetrics to assess for intracranial bleeding depending on the compartment involved. We aimed to determine the most reliable way to measure intracranial hemorrhage (ICrH) volume by comparing individual brain compartment and total ICrH volume.

METHODS

Thirty patients were randomly selected from the ANNEXA-4 database to assess measurement of ICrH volume by compartment and in total. Total and compartmental hemorrhage volumes were measured by five readers using Quantomo software. Each reader measured baseline hemorrhage volumes twice separated by 1 week. Twenty-eight different ANNEXA-4 subjects were also randomly selected to assess intra-rater reliability of total ICrH volume measurement change at baseline and 12-h follow up, performed by three readers twice to assess hemostatic efficacy categories used in ANNEXA-4.

RESULTS

Compartmental minimal detectable change percentages (MDC%) ranged between 9.72 and 224.13, with the greatest measurement error occurring in patients with a subdural hemorrhage. Total ICrH volume measurements had the lowest MDC%, which ranged between 6.57 and 33.52 depending on the reader.

CONCLUSION

Measurement of total ICrH volumes is more accurate than volume by compartment with less measurement error. Determination of hemostatic efficacy was consistent across readers, and within the same reader, as well as when compared to consensus read. Volumetric analysis of intracranial hemostatic efficacy is feasible and reliable when using total ICrH volumes.

Radiomics-based Machine Learning Methods for Volume Doubling Time Prediction of Pulmonary Ground-glass Nodules With Baseline Chest Computed Tomography

PURPOSE

Reliable prediction of volume doubling time (VDT) is essential for the personalized management of pulmonary ground-glass nodules (GGNs). We aimed to determine the optimal VDT prediction method by comparing different machine learning methods only based on the baseline chest computed tomography (CT) images.

MATERIALS AND METHODS

Seven classical machine learning methods were evaluated in terms of their stability and performance for VDT prediction. The VDT, calculated by the preoperative and baseline CT, was divided into 2 groups with a cutoff value of 400 days. A total of 90 GGNs from 3 hospitals constituted the training set, and 86 GGNs from the fourth hospital served as the external validation set. The training set was used for feature selection and model training, and the validation set was used to evaluate the predictive performance of the model independently.

RESULTS

The eXtreme Gradient Boosting showed the highest predictive performance (accuracy: 0.890±0.128 and area under the ROC curve (AUC): 0.896±0.134), followed by the neural network (NNet) (accuracy: 0.865±0.103 and AUC: 0.886±0.097). While regarding stability, the NNet showed the highest robustness against data perturbation (relative SDs [%] of mean AUC: 10.9%). Therefore, the NNet was chosen as the final model, achieving high accuracy of 0.756 in the external validation set.

CONCLUSION

The NNet is a promising machine learning method to predict the VDT of GGNs, which would assist in the personalized follow-up and treatment strategies for GGNs reducing unnecessary follow-up and radiation dose.

Natural progression of persistent pure ground-glass nodules 10 mm or smaller: long-term observation and risk factor assessment

PURPOSE

Semi-automatic segmentation was used to investigate the natural progression of pure ground-glass nodules (pGGNs) of 5-10 mm in long-term follow-up and to analyze independent risk factors for subsequent growth.

MATERIALS AND METHODS

A total of 154 pGGNs of 5-10 mm from 132 patients with 698 follow-up CT scans were retrospectively identified. Subsequently, enrolled pGGNs were semiautomatically segmented on initial and follow-up CT to obtain diameter, density and volume, thus calculating mass, volume doubling time (VDT), and mass doubling time (MDT). Kaplan‒Meier analysis and multivariate Cox proportional risk regression were performed to explore independent predictors of pGGN growth. We analyzed growth differences among different pathological results of pGGNs confirmed by surgery. The prognosis was analyzed using the total diameter or solid size of the nodules on the last preoperative CT.

RESULTS

Among the 85 (55.2%) pGGNs with growth, 5.9%, 51.8%, and 80.0% showed growth within 1, 3, and 5 years, respectively. The median VDT and MDT were 1206.4 (range 349.8-5134.4) days and 1161.3 (range 339.4-6630.4) days, respectively. The multivariate Cox risk regression analysis showed that mean CT attenuation (m-CTA) [hazard ratio (HR) = 2.098, p = 0.010] and roundness index (HR = 1.892, p = 0.021) were independent risk factors for pGGN growth. In total, 67.6% of surgically resected and growing pGGNs were invasive non-mucinous adenocarcinoma (IA), including 2 cases of endpoint events, showing a PSN with solid components of 5.6 mm and a solid nodule with a diameter of 19.9 mm.

CONCLUSIONS

pGGNs of 5-10 mm showed an indolent clinical course. Follow-up CT imaging of pGGNs in the latter half of the first two years should be a rational management strategy. Small pGGNs with a larger overall m-CTA and roundness index on baseline CT are more likely to grow.

Prediction of the Growth Rate of Early-Stage Lung Adenocarcinoma by Radiomics

Objectives

To investigate the value of imaging in predicting the growth rate of early lung adenocarcinoma.

Methods

From January 2012 to June 2018, 402 patients with pathology-confirmed lung adenocarcinoma who had two or more thin-layer CT follow-up images were retrospectively analyzed, involving 407 nodules. Two complete preoperative CT images and complete clinical data were evaluated. Training and validation sets were randomly assigned according to an 8:2 ratio. All cases were divided into fast-growing and slow-growing groups. Researchers extracted 1218 radiomics features from each volumetric region of interest (VOI). Then, radiomics features were selected by repeatability analysis and Analysis of Variance (ANOVA); Based on the Univariate and multivariate analyses, the significant radiographic features is selected in training set. A decision tree algorithm was conducted to establish the radiographic model, radiomics model and the combined radiographic-radiomics model. Model performance was assessed by the area under the curve (AUC) obtained by receiver operating characteristic (ROC) analysis.

Results

Sixty-two radiomics features and one radiographic features were selected for predicting the growth rate of pulmonary nodules. The combined radiographic-radiomics model (AUC 0.78) performed better than the radiographic model (0.727) and the radiomics model (0.710) in the validation set.

Conclusions

The model has good clinical application value and development prospects to predict the growth rate of early lung adenocarcinoma through the combined radiographic-radiomics model.

Diagnostic value and imaging features of multi-detector CT in lung adenocarcinoma with ground glass nodule patients

This study investigated the application value and imaging features of multi-detector CT (MDCT) in the treatment of lung adenocarcinoma with ground glass nodules (GGN). The medical data of 168 patients with pulmonary GGN in Shengli Oilfield Central Hospital from January 2013 to June 2015 were analyzed. Patients with microinvasive adenocarcinoma and invasive adenocarcinoma were included in group A (invasive lung adenocarcinoma, n=98), while patients with atypical adenomatous hyperplasia and adenocarcinoma in situ were included in group B (pre-invasive lung adenocarcinoma, n=70). The imaging features of MDCT were compared. ROC curves of the size of nidus and the size of solid component were drawn for the diagnosis of invasive lung adenocarcinoma. Logistic multivariate regression analysis was used to analyze the risk factors that affected invasive lung adenocarcinoma. There were significant differences in nidus, burr, and lobes of the patients between groups A and B. The size of nidus and the size of solid component of the patients in group A were significantly higher than those of the patients in group B. The AUCs of the size of the nidus and the size of the solid component of the invasive lung adenocarcinoma were 0.891 and 0.902, respectively. The AUC of the combined diagnosis was 0.984. Size of the nidus, size of the solid component, nature of the lesion, burr, and lobes were all risk factors for invasive lung adenocarcinoma. In patients with GGN, size of the nidus and size of the solid component can be used as excellent diagnostic parameters for invasive lung adenocarcinoma, and nidus size (≥9.8 mm), size of the solid component (≥0.9 mm), the mixed GGN nature of the nidus, burr and lobes can distinguish invasive lung adenocarcinoma and pre-invasive lesions.

Measurement accuracy of lung nodule volumetry in a phantom study: Effect of axial-volume scan and iterative reconstruction algorithm

An axial-volume scan with adaptive statistical iterative reconstruction-V (ASIR-V) is newly developed. Our goal was to identify the influence of axial-volume scan and ASIR-V on accuracy of automated nodule volumetry.An "adult' chest phantom containing various nodules was scanned using both helical and axial-volume modes at different dose settings using 256-slice CT. All CT scans were reconstructed using 30% and 50% blending of ASIR-V and filtered back projection. Automated nodule volumetry was performed using commercial software. The image noise, contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR) were measured.The axial-volume scan reduced radiation dose by 19.7% compared with helical scan at all radiation dose settings without affecting the accuracy of nodule volumetric measurement (P = .375). Image noise, CNR, and SNR were not significantly different between two scan modes (all, P > .05).The use of axial-volume scan with ASIR-V achieved effective radiation dose reduction while preserving the accuracy of nodule volumetry.

Qualitative and quantitative imaging features of pulmonary subsolid nodules: differentiating invasive adenocarcinoma from minimally invasive adenocarcinoma and preinvasive lesions

Background

To explore the role of qualitative and quantitative imaging features of pulmonary subsolid nodules (SSNs) in differentiating invasive adenocarcinoma (IAC) from minimally invasive adenocarcinoma (MIA) and preinvasive lesions.

Methods

We reviewed the clinical records of our institute from October 2010 to December 2015 and included 316 resected SSNs from 287 patients: 260 pure ground-glass nodules, 47 part-solid nodules with solid components ≤5 mm, and 9 ground-glass nodules (GGNs) with cystic airspaces. According to the pathologic review results, 307 SSNs in addition to nine GGNs with cystic airspaces were divided into two groups: A, including atypical adenomatous hyperplasia (AAH) (n=15), adenocarcinoma in situ (AIS) (n=56), and MIA (n=41); B, including 195 IACs. Univariate and binary logistic regression analyses were conducted to identify independent risk factors for IAC.

Results

Univariate analysis showed significant differences between groups regarding patient age, mean diameter, mean and relative computed tomography (CT) values, volume, mass (all P<0.001), and morphological features including lobulated sign (P<0.001), spiculated sign (P=0.028), vacuole sign/air bronchogram (P<0.001), and pleural retraction (P=0.017). Binary logistic regression and receiver operating characteristic analysis indicated the SSN mass as the only independent risk factor of IAC (odds ratio, 1.007; P<0.001), with an optimal cutoff value of 283.2 mg [area under curve (AUC): 0.859; sensitivity: 68.7%; specificity: 92.9%]. Among lepidic, acinar, and papillary adenocarcinomas, we found significant differences for the vacuole sign/air bronchogram (P=0.032) and mean and relative CT values (P<0.001). All nine GGNs with cystic airspaces were IACs.

Conclusions

The SSN mass with an optimal cutoff value of 283.2 mg may be reliable for differentiating IAC from MIA and preinvasive lesions.

Analysis of CT morphologic features and attenuation for differentiating among transient lesions, atypical adenomatous hyperplasia, adenocarcinoma in situ, minimally invasive and invasive adenocarcinoma presenting as pure ground-glass nodules

Thin-section computed tomography (TSCT) imaging biomarkers are uncertain to distinguish progressive adenocarcinoma from benign lesions in pGGNs. The purpose of this study was to evaluate the usefulness of TSCT characteristics for differentiating among transient (TRA) lesions, atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC) presenting as pure ground-glass nodules (pGGNs). Between January 2016 and January 2018, 255 pGGNs, including 64 TRA, 22 AAH, 37 AIS, 108 MIA and 24 IAC cases, were reviewed on TSCT images. Differences in TSCT characteristics were compared among these five subtypes of pGGNs. Logistic analysis was performed to identify significant factors for predicting MIA and IAC. Progressive pGGNs were more likely to be round or oval in shape, with clear margins, air bronchograms, vascular and pleural changes, creep growth, and bubble-like lucency than were non-progressive pGGNs. The optimal cut-off values of the maximum diameter for differentiating non-progressive from progressive pGGNs and IAC from non-IAC were 6.5 mm and 11.5 mm, respectively. For the prediction of IAC vs. non-IAC and non-progressive vs. progressive adenocarcinoma, the areas under the receiver operating characteristics curves were 0.865 and 0.783 for maximum diameter and 0.784 and 0.722 for maximum CT attenuation, respectively. The optimal cut-off values of maximum CT attenuation were -532 HU and -574 HU for differentiating non-progressive from progressive pGGNs and IAC from non-IAC, respectively. Maximum diameter, maximum attenuation and morphological characteristics could help distinguish TRA lesions from MIA and IAC but not from AAH. So, CT morphologic characteristics, diameter and attenuation parameters are useful for differentiating among pGGNs of different subtypes.

Long-term follow-up of persistent pulmonary pure ground-glass nodules with deep learning-assisted nodule segmentation

OBJECTIVE

To investigate the natural history of persistent pulmonary pure ground-glass nodules (pGGNs) with deep learning-assisted nodule segmentation.

METHODS

Between January 2007 and October 2018, 110 pGGNs from 110 patients with 573 follow-up CT scans were included in this retrospective study. pGGN automatic segmentation was performed on initial and all follow-up CT scans using the Dr. Wise system based on convolution neural networks. Subsequently, pGGN diameter, density, volume, mass, volume doubling time (VDT), and mass doubling time (MDT) were calculated automatically. Enrolled pGGNs were categorized into growth, 52 (47.3%), and non-growth, 58 (52.7%), groups according to volume growth. Kaplan-Meier analyses with the log-rank test and Cox proportional hazards regression analysis were conducted to analyze the cumulative percentages of pGGN growth and identify risk factors for growth.

RESULTS

The mean follow-up period of the enrolled pGGNs was 48.7 ± 23.8 months. The median VDT of the 52 pGGNs having grown was 1448 (range, 339-8640) days, and their median MDT was 1332 (range, 290-38,912) days. The 12-month, 24.7-month, and 60.8-month cumulative percentages of pGGN growth were 10%, 25.5%, and 51.1%, respectively, and they significantly differed among the initial diameter, volume, and mass subgroups (all p < 0.001). The growth pattern of pGGNs may conform to the exponential model. Lobulated sign (p = 0.044), initial mean diameter (p < 0.001), volume (p = 0.003), and mass (p = 0.023) predicted pGGN growth.

CONCLUSIONS

Persistent pGGNs showed an indolent course. Deep learning can assist in accurately elucidating the natural history of pGGNs. pGGNs with lobulated sign and larger initial diameter, volume, and mass are more likely to grow.

KEY POINTS

• The pure ground-glass nodule (pGGN) segmentation accuracy of the Dr. Wise system based on convolution neural networks (CNNs) was 96.5% (573/594). • The median volume doubling time (VDT) of 52 pure ground-glass nodules (pGGNs) having grown was 1448 days (range, 339-8640 days), and their median mass doubling time (MDT) was 1332 days (range, 290-38,912 days). The mean time to growth in volume was 854 ± 675 days (range, 116-2856 days). • The 12-month, 24.7-month, and 60.8-month cumulative percentages of pGGN growth were 10%, 25.5%, and 51.1%, respectively, and they significantly differed among the initial diameter, volume, and mass subgroups (all p values < 0.001). The growth pattern of pure ground-glass nodules may conform to exponential model.

Solid Indeterminate Pulmonary Nodules of Less Than 300 mm3: Application of Different Volume Doubling Time Cut-offs in Clinical Practice

In the British Thoracic Society guidelines for incidental pulmonary nodules, volumetric analysis has become the recommended method for growth assessment in solid indeterminate pulmonary nodules (SIPNs) <300 mm³. In these guidelines, two different volume doubling time (VDT) cut-offs, 400 and 600 days, were proposed to differentiate benign from malignant nodules. The present study aims to evaluate the performance of these VDT cut-offs in a group of SIPNs <300 mm³ which were incidentally detected in a routine clinical setting. During a 7-year period, we retrospectively selected 60 patients with a single SIPN <300 mm³. For each SIPN, the volume and VDT were calculated using semiautomatic software throughout the follow-up period, and the performance of the 400- and 600-day VDT cut-offs was compared. In the selected sample, there were 38 benign and 22 malignant nodules. In this group of nodules, the sensitivity, negative predictive value and accuracy of the 600-day VDT cut-off were higher than those of the 400-day VDT cut-off. Therefore, in the management of SIPNs <300 mm³ which were incidentally detected in a clinical setting, the 600-day VDT cut-off was better at differentiating benign from malignant nodules than the 400-day VDT cut-off, by reducing the number of false negatives.

Analysis of risk factors for stage I lung adenocarcinoma using low-dose high-resolution computed tomography

Risk factors for stage I lung adenocarcinoma were analyzed using low-dose high-resolution computed tomography (CT). The patients were divided into case group (stage I lung adenocarcinoma patients) and control group (benign pulmonary nodules patients). All patients were subjected to low-dose high-resolution CT. Multiple linear regression was performed to analyze the CT imaging features of the two groups. Stage I lung adenocarcinoma patients were significantly associated with nodular site (X3, upper left lobe) [95% CI (1.796, 54.695), p=0.008], nodule type (X4) (p<0.001), nodule size (X5) [95% CI (0.614, 0.803), p<0.001], spicule sign (X7) [95% CI (0.029, 0.580), p=0.008], lobulation sign (X8) [95% CI (0.048, 0.673), p=0.011]. The stepwise regression equation is: Logistic (p) =-12.009 + 2.294X3 - 0.327X4 - 0.354X5 - 2.042X7 - 1.713X8. Risk factors of low-dose and high-resolution CT imaging for patients with stage I lung adenocarcinoma are nodular site (upper left lobe), nodule type, nodule size, spicule sign, and lobulation sign.

What CT characteristics of lepidic predominant pattern lung adenocarcinomas correlate with invasiveness on pathology?

OBJECTIVES

The International Association for the Study of Lung Cancer, American Thoracic Society and European Respiratory Society lung adenocarcinoma classification in 2011 defined three lepidic predominant patterns including adenocarcinoma in situ, minimally invasive adenocarcinoma and lepidic predominant adenocarcinoma. We sought to correlate the radiology and pathology findings and identify any computed tomography (CT) features which can be associated with invasive growth.

MATERIALS AND METHODS

An institutional review board approved, retrospective study was conducted evaluating 63 patients with resected, pathologically confirmed, adenocarcinomas with predominant lepidic patterns. Preoperative CT images of the nodules were assessed using quantitative and qualitative radiographic descriptors while blinded to pathologic sub-classification and size. Maximum diameter was measured after evaluation of the axial, sagittal and coronal planes. Radiologic - pathologic associations were examined using Fisher's exact test, the Kruskal-Wallis test and the Spearman correlation coefficient (ρ).

RESULTS AND CONCLUSION

Increasing maximum diameter of the whole lesion (ground glass and solid component) on CT was significantly associated with invasiveness (p = .003), as was the maximum pathologic specimen diameter (p = .008). Larger diameter of the solid component on CT was also found in lepidic predominant adenocarcinoma compared to minimally invasive adenocarcinoma (median 10.5 vs 2 mm, p = .005). More invasive tumors had higher visual estimated percentage solid component compared to whole lesion measurement on CT (p = .014). CT and pathologic measurements were positively correlated, although only moderately (ρ = .66) for the maximum whole lesion size and fair (ρ = .49) for solid/invasive component maximum measurements. Larger whole lesion size and solid component size of lepidic predominant pattern adenocarcinomas are associated with lesion invasiveness, although radiologic and pathologic lesion measurements are only fair-moderately positively correlated.

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.

Lepidic Predominant Pulmonary Lesions (LPL): CT-based Distinction From More Invasive Adenocarcinomas Using 3D Volumetric Density and First-order CT Texture Analysis

RATIONALE AND OBJECTIVES

This study aimed to differentiate pathologically defined lepidic predominant lesions (LPL) from more invasive adenocarcinomas (INV) using three-dimensional (3D) volumetric density and first-order texture histogram analysis of surgically excised stage 1 lung adenocarcinomas.

MATERIALS AND METHODS

This retrospective study was institutional review board approved and Health Insurance Portability and Accountability Act compliant. Sixty-four cases of pathologically proven stage 1 lung adenocarcinoma surgically resected between September 2006 and October 2015, including LPL (n = 43) and INV (n = 21), were evaluated using high-resolution computed tomography. Quantitative measurements included nodule volume, percent solid volume (% solid), and first-order texture histogram analysis including skewness, kurtosis, entropy, and mean nodule attenuation within each histogram quartile. Binomial logistic regression models were used to identify the best set of parameters distinguishing LPL from INV.

RESULTS

Univariate analysis of 3D volumetric density and histogram features was statistically significant between LPL and INV groups (P < .05). Accuracy of a binomial logistic model to discriminate LPL from INV based on size and % solid was 85.9%. With optimized probability cutoff, the model achieves 81% sensitivity, 76.7% specificity, and area under the receiver operating characteristic curve of 0.897 (95% confidence interval, 0.821-0.973). An additional model based on size and mean nodule attenuation of the third quartile (Hu_Q3) of the histogram achieved similar accuracy of 81.3% and area under the receiver operating characteristic curve of 0.877 (95% confidence interval, 0.790-0.964).

CONCLUSIONS

Both 3D volumetric density and first-order texture analysis of stage 1 lung adenocarcinoma allow differentiation of LPL from more invasive adenocarcinoma with overall accuracy of 85.9%-81.3%, based on multivariate analyses of either size and % solid or size and Hu_Q3, respectively.

Quantitative assessment of nonsolid pulmonary nodule volume with computed tomography in a phantom study

Background

To assess the volumetric measurement of small (≤1 cm) nonsolid nodules with computed tomography (CT), focusing on the interaction of state of the art iterative reconstruction (IR) methods and dose with nodule densities, sizes, and shapes.

Methods

Twelve synthetic nodules [5 and 10 mm in diameter, densities of -800, -630 and -10 Hounsfield units (HU), spherical and spiculated shapes] were scanned within an anthropomorphic phantom. Dose [computed tomography scan dose index (CTDI_vol)] ranged from standard (4.1 mGy) to below screening levels (0.3 mGy). Data was reconstructed using filtered back-projection and two state-of-the-art IR methods (adaptive and model-based). Measurements were extracted with a previously validated matched filter-based estimator. Analysis of accuracy and precision was based on evaluation of percent bias (PB) and the repeatability coefficient (RC) respectively.

Results

Density had the most important effect on measurement error followed by the interaction of density with nodule size. The nonsolid -630 HU nodules had high accuracy and precision at levels comparable to solid (-10 HU) nonsolid, regardless of reconstruction method and with CTDI_vol as low as 0.6 mGy. PB was <5% and <11% for the 10- and 5-mm in nominal diameter -630 HU nodules respectively, and RC was <5% and <12% for the same nodules. For nonsolid -800 HU nodules, PB increased to <11% and <30% for the 10- and 5-mm nodules respectively, whereas RC increased slightly overall but varied widely across dose and reconstruction algorithms for the 5-mm nodules. Model-based IR improved measurement accuracy for the 5-mm, low-density (-800, -630 HU) nodules. For other nodules the effect of reconstruction method was small. Dose did not affect volumetric accuracy and only affected slightly the precision of 5-mm nonsolid nodules.

Conclusions

Reasonable values of both accuracy and precision were achieved for volumetric measurements of all 10-mm nonsolid nodules, and for the 5-mm nodules with -630 HU or higher density, when derived from scans acquired with below screening dose levels as low as 0.6 mGy and regardless of reconstruction algorithm.

Three-dimensional substructure measurements for the differential diagnosis of ground glass nodules

BACKGROUND

We analyzed the differences between maximum and peak computed tomography (CT) numbers (M-P), respectively representing the densities of the solid center and the main periphery of ground-glass nodules (GGNs), and the average change in M-P velocity (V(M-P)) during follow-up to differentiate between pre-invasive (PIA) and invasive adenocarcinoma (IAC).

METHODS

Data of 102 patients were retrospectively collected and analyzed in our study including 43 PIAs and 59 IACs. Diameters, total volumes, and the maximum and peak CT numbers in CT number histograms were measured and followed for at least 3 months. This study was registered retrospectively.

RESULTS

The M-P values for IACs were higher than those for PIAs (p = 0.001), with an area under the curve (AUC) of 0.810 and a threshold of 489.5 Hounsfield units (HU) in ROC analysis. The V(M-P) values for IACs were smaller than those for PIAs (p = 0.04), with an AUC of 0.805 and a threshold of 11.01 HU/day.

CONCLUSIONS

M-P and V(M-P) values may help distinguish IACs from PIAs by representing the changes in the sub-structural densities of GGNs during follow-up.

Pulmonary adenocarcinomas presenting as ground-glass opacities on multidetector CT: three-dimensional computer-assisted analysis of growth pattern and doubling time

PURPOSE

We aimed to evaluate the growth pattern and doubling time (DT) of pulmonary adenocarcinomas exhibiting ground-glass opacities (GGOs) on multidetector computed tomography (CT).

METHODS

The growth pattern and DT of 22 pulmonary adenocarcinomas exhibiting GGOs were retrospectively analyzed using three-dimensional semiautomatic software. Analysis of each lesion was based on calculations of volume and mass changes and their respective DTs throughout CT follow-up. Three-dimensional segmentation was performed by a single radiologist on each CT scan. The same observer and another radiologist independently repeated the segmentation at the baseline and the last CT scan to determine the variability of the measurements. The relationships among DTs, histopathology, and initial CT features of the lesions were also analyzed.

RESULTS

Pulmonary adenocarcinomas presenting as GGOs exhibited different growth patterns: some lesions grew rapidly and some grew slowly, whereas others alternated between periods of growth, stability, or shrinkage. A significant increase in volume and mass that exceeded the coefficient of repeatability of interobserver variability was observed in 72.7% and 84.2% of GGOs, respectively. The volume-DTs and mass-DTs were heterogeneous throughout the follow-up CT scan (range, -4293 to 21928 and -3113 to 17020 days, respectively), and their intra- and interobserver variabilities were moderately high. The volume-DTs and mass-DTs were not correlated with the initial CT features of GGOs; however, they were significantly shorter in invasive adenocarcinomas (P = 0.002 and P = 0.001, respectively).

CONCLUSION

Pulmonary adenocarcinomas exhibiting GGOs show heterogeneous growth patterns with a trend toward a progressive increase in size. DTs may be useful for predicting tumor aggressiveness.

[Threshold Segmentation of Pulmonary Subsolid Nodules on CT Images: Detection and Quantification of the Solid Component]

BACKGROUND

The detection and quantification of solid components in pulmonary subsolid nodules (SSN) are of vital importance on differential diagnosis, pathological speculation and prognosis prediction. However, no objective and wide-accepted criterion has been built up to now. The purpose of this study is to explore the optimal threshold that can be used for the detection and quantification of solid components in SSNs by using threshold segmentation method on computed tomography (CT) images.

METHODS

CT images of 102 SSNs were retrospectively analyzed. To establish a reference standard, the observers made judgments on whether the solid component existed in every SSN and did manual measurements of the volume of solid component with the help of software. Threshold segmentations of every nodule were then performed using different threshold settings and all of the measured volumes were assumed to be solid volumes, then solid-to-total volume ratios were calculated. The results were compared with the reference standards using the receiver operating characteristic curve and Wilcoxon test.

RESULTS

The application of thresholds as -250 HU or -300 HU resulted in high diagnostic value on the detection of solid component, with area under curve values as 0.982 and 0.977, respectively; the cut-off values of solid-to-total volume ratio were 1.10% and 6.14%, respectively; the median volumes of solid components were 202.7 mm3 (598.2 mm3), 247.1 mm3(696.0 mm3), which were not significantly different from the reference standard[199.5 mm3 (743.1 mm3)](P=0.125,1, 0.061,3).

CONCLUSIONS

Threshold segmentation on chest CT images is valuable to detect and quantify the solid component on SSNs, the thresholds as -250 HU and -300 HU are recommended.
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Volumes Learned: It Takes More Than Size to "Size Up" Pulmonary Lesions

RATIONALE AND OBJECTIVES

This study aimed to review the current understanding and capabilities regarding use of imaging for noninvasive lesion characterization and its relationship to lung cancer screening and treatment.

MATERIALS AND METHODS

Our review of the state of the art was broken down into questions about the different lung cancer image phenotypes being characterized, the role of imaging and requirements for increasing its value with respect to increasing diagnostic confidence and quantitative assessment, and a review of the current capabilities with respect to those needs.

RESULTS

The preponderance of the literature has so far been focused on the measurement of lesion size, with increasing contributions being made to determine the formal performance of scanners, measurement tools, and human operators in terms of bias and variability. Concurrently, an increasing number of investigators are reporting utility and predictive value of measures other than size, and sensitivity and specificity is being reported. Relatively little has been documented on quantitative measurement of non-size features with corresponding estimation of measurement performance and reproducibility.

CONCLUSIONS

The weight of the evidence suggests characterization of pulmonary lesions built on quantitative measures adds value to the screening for, and treatment of, lung cancer. Advanced image analysis techniques may identify patterns or biomarkers not readily assessed by eye and may also facilitate management of multidimensional imaging data in such a way as to efficiently integrate it into the clinical workflow.

Thin-section computed tomography-histopathologic comparisons of pulmonary focal interstitial fibrosis, atypical adenomatous hyperplasia, adenocarcinoma in situ, and minimally invasive adenocarcinoma with pure ground-glass opacity

OBJECTIVE

To retrospectively compare focal interstitial fibrosis (FIF), atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), and minimally invasive adenocarcinoma (MIA) with pure ground-glass opacity (GGO) using thin-section computed tomography (CT).

MATERIALS AND METHODS

Sixty pathologically confirmed cases were reviewed including 7 cases of FIF, 17 of AAH, 23of AIS, and 13 of MIA. All nodules kept pure ground glass appearances before surgical resection and their last time of thin-section CT imaging data before operation were collected. Differences of patient demographics and CT features were compared among these four types of lesions.

RESULTS

FIF occurred more frequently in males and smokers while the others occurred more frequently in female nonsmokers. Nodule size was significant larger in MIA (P<0.001, cut-off value=7.5mm). Nodule shape (P=0.045), margin characteristics (P<0.001), the presence of pleural indentation (P=0.032), and vascular ingress (P<0.001) were significant factors that differentiated the 4 groups. A concave margin was only demonstrated in a high proportion of FIF at 85.7% (P=0.002). There were no significant differences (all P>0.05) in age, malignant history, attenuation value, location, and presence of bubble-like lucency.

CONCLUSION

A nodule size >7.5mm increases the possibility of MIA. A concave margin could be useful for differentiation of FIF from the other malignant or pre-malignant GGO nodules. The presence of spiculation or pleural indentation may preclude the diagnosis of AAH.

Volume estimation of multidensity nodules with thoracic computed tomography

This work focuses on volume estimation of "multidensity" lung nodules in a phantom computed tomography study. Eight objects were manufactured by enclosing spherical cores within larger spheres of double the diameter but with a different density. Different combinations of outer-shell/inner-core diameters and densities were created. The nodules were placed within an anthropomorphic phantom and scanned with various acquisition and reconstruction parameters. The volumes of the entire multidensity object as well as the inner core of the object were estimated using a model-based volume estimator. Results showed percent volume bias across all nodules and imaging protocols with slice thicknesses [Formula: see text] ranging from [Formula: see text] to 6.6% for the entire object (standard deviation ranged from 1.5% to 7.6%), and within [Formula: see text] to 5.7% for the inner-core measurement (standard deviation ranged from 2.0% to 17.7%). Overall, the estimation error was larger for the inner-core measurements, which was expected due to the smaller size of the core. Reconstructed slice thickness was found to substantially affect volumetric error for both tasks; exposure and reconstruction kernel were not. These findings provide information for understanding uncertainty in volumetry of nodules that include multiple densities such as ground glass opacities with a solid component.

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.

Pulmonary ground-glass nodules diagnosis: mean change rate of peak CT number as a discriminative factor of pathology during a follow-up

OBJECTIVE

We aimed to analyse the peak CT number (PEAK) in CT number histogram of ground-glass nodules (GGN), meaning the most frequent density of pixels in the image of pulmonary nodule, based on three-dimensional (3D) reconstructive model pre-operatively, and the mean rate of PEAK change (V-PEAK) during a follow-up of GGN for differential diagnosis between pre-invasive adenocarcinoma (PIA) and invasive adenocarcinoma (IAC).

METHODS

CT number histogram of pixels in GGN was made automatically by 3D measurement software. Diameter, total volume, PEAK and V-PEAK were measured from CT data sets of different groups classified by pathology, subtype and number of GGN, respectively.

RESULTS

Among all 102 cases, 47 were PIA, including atypical adenomatous hyperplasia (n = 29) and adenocarcinoma in situ (n = 18), and 55 were IAC, including minimally IAC (MIA, n = 4). By Wilcoxon test, PEAK of IAC was significantly higher than that of PIA (p < 0.001). By receiver operating curve analysis, area under the curve (AUC) was 0.857 and threshold -820.50 Hounsfield units (HU) for differentiation between PIA and IAC. V-PEAK of IAC was unexpectedly remarkably smaller than that of PIA (p < 0.001) with AUC and threshold being 0.810 and -0.829 HU day(-1), respectively.

CONCLUSION

Pre-operative PEAK and V-PEAK, which interpret and evaluate the change of volume and density of pulmonary nodule simultaneously from both exterior and interior perspectives, can help to distinguish IAC from PIA.

ADVANCES IN KNOWLEDGE

This study provided researchers of GGN another perspective, taking both volume and density of nodules into consideration for pathological evaluation.

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.

Differentiating between Subsolid and Solid Pulmonary Nodules at CT: Inter- and Intraobserver Agreement between Experienced Thoracic Radiologists

PURPOSE

To quantify the reproducibility and accuracy of experienced thoracic radiologists in differentiating between subsolid and solid pulmonary nodules at CT.

MATERIALS AND METHODS

The institutional review board of Beth Israel Deaconess Medical Center approved this multicenter study. Six thoracic radiologists, with a mean of 21 years of experience in thoracic radiology (range, 17-22 years), selected images of 10 solid and 10 subsolid nodules to create a database of 120 nodules; this selection served as the reference standard. Each radiologist then interpreted 120 randomly ordered nodules in two different sessions that were separated by a minimum of 3 weeks. The radiologists classified whether or not each nodule was subsolid. Inter- and intraobserver agreement was assessed with a κ statistic. The number of correct classifications was calculated and correlated with nodule size by using Bland-Altman plots. The relationship between disagreement and nodule morphologic characteristics was analyzed by calculating the intraclass correlation coefficient.

RESULTS

Interobserver agreement (κ) was 0.619 (range, 0.469-0.745; 95% confidence interval (CI): 0.576, 0.663) and 0.670 (range, 0.440-0.839; 95% CI: 0.608, 0.733) for interpretation sessions 1 and 2, respectively. Intraobserver agreement (κ) was 0.792 (95% CI: 0.750, 0.833). Averaged for interpretation sessions, correct classification was achieved by all radiologists for 58% (70 of 120) of nodules. Radiologists agreed with their initial determination (the reference standard) in 77% of cases (range, 45%-100%). Nodule size weakly correlated with correct classification (long axis: Spearman rank correlation coefficient, rs = 0.161 and P = .049; short axis: rs = 0.128 and P = .163).

CONCLUSION

The reproducibility and accuracy of thoracic radiologists in classifying whether or not a nodule is subsolid varied in the retrospective study. This inconsistency may affect surveillance recommendations and prognostic determinations.

Radiologic Predictors for Clinical Stage IA Lung Adenocarcinoma with Ground Glass Components: A Multi-Center Study of Long-Term Outcomes

Objective

This study was to define preoperative predictors from radiologic findings for the pathologic risk groups based on long-term surgical outcomes, in the aim to help guide individualized patient management.

Methods

We retrospectively reviewed 321 consecutive patients with clinical stage IA lung adenocarcinoma with ground glass component on computed tomography (CT) scanning. Pathologic diagnosis for resection specimens was based on the 2011 IASLC/ATS/ERS classification of lung adenocarcinoma. Patients were classified into different pathologic risk grading groups based on their lymph node status, local regional recurrence and overall survival. Radiologic characteristics of the pulmonary nodules were re-evaluated by reconstructed three-dimension CT (3D-CT). Univariate and multivariate analysis identifies independent radiologic predictors from tumor diameter, total volume (TV), average CT value (AVG), and solid-to-tumor (S/T) ratio. Receiver operating characteristic curves (ROC) studies were carried out to determine the cutoff value(s) for the predictor(s). Univariate cox regression model was used to determine the clinical significance of the above findings.

Results

A total of 321 patients with clinical stage IA lung adenocarcinoma with ground glass components were included in our study. Patients were classified into two pathologic low- and high- risk groups based on their distinguished surgical outcomes. A total of 134 patients fell into the low-risk group. Univariate and multivariate analyses identified AVG (HR: 32.210, 95% CI: 3.020–79.689, P<0.001) and S/T ratio (HR: 12.212, 95% CI: 5.441–27.408, P<0.001) as independent predictors for pathologic risk grading. ROC curves studies suggested the optimal cut-off values for AVG and S/T ratio were-198 (area under the curve [AUC] 0.921), 2.9 (AUC 0.996) and 54% (AUC 0.907), respectively. The tumor diameter and TV were excluded for the low AUCs (0.778 and 0.767). Both the cutoff values of AVG and S/T ratio were correlated with pathologic risk classification (p<0.001). Univariate Cox regression model identified clinical risk classification (RR: 3.011, 95%CI: 0.796–7.882, P = 0.095) as a good predictor for recurrence-free survival (RFS) in patients with clinical stage IA lung adenocarcinoma. Statistical significance of 5-year OS and RFS was noted among clinical low-, moderate- and high-risk groups (log-rank, p = 0.024 and 0.010).

Conclusions

The AVG and the S/T ratio by reconstructed 3D-CT are important preoperative radiologic predictors for pathologic risk grading. The two cutoff values of AVG and S/T ratio are recommended in decision-making for patients with clinical stage IA lung adenocarcinoma with ground glass components.

Preoperative Prediction of Ki-67 Labeling Index By Three-dimensional CT Image Parameters for Differential Diagnosis Of Ground-Glass Opacity (GGO)

The aim of this study was to predict Ki-67 labeling index (LI) preoperatively by three-dimensional (3D) CT image parameters for pathologic assessment of GGO nodules. Diameter, total volume (TV), the maximum CT number (MAX), average CT number (AVG) and standard deviation of CT number within the whole GGO nodule (STD) were measured by 3D CT workstation. By detection of immunohistochemistry and Image Software Pro Plus 6.0, different Ki-67 LI were measured and statistically analyzed among preinvasive adenocarcinoma (PIA), minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC). Receiver operating characteristic (ROC) curve, Spearman correlation analysis and multiple linear regression analysis with cross-validation were performed to further research a quantitative correlation between Ki-67 labeling index and radiological parameters. Diameter, TV, MAX, AVG and STD increased along with PIA, MIA and IAC significantly and consecutively. In the multiple linear regression model by a stepwise way, we obtained an equation: prediction of Ki-67 LI=0.022*STD+0.001* TV+2.137 (R=0.595, R’s square=0.354, p<0.001), which can predict Ki-67 LI as a proliferative marker preoperatively. Diameter, TV, MAX, AVG and STD could discriminate pathologic categories of GGO nodules significantly. Ki-67 LI of early lung adenocarcinoma presenting GGO can be predicted by radiologic parameters based on 3D CT for differential diagnosis.

Differentiating pre- and minimally invasive from invasive adenocarcinoma using CT-features in persistent pulmonary part-solid nodules in Caucasian patients

OBJECTIVE

To retrospectively investigate the diagnostic value of pre-operative CT-features between pre/minimally invasive and invasive lesions in part-solid persistent pulmonary ground glass nodules in a Caucasian population.

MATERIALS AND METHODS

Retrospective review of two pre-operative CTs for 31 nodules in 30 patients. There were 10 adenocarcinomas in situ, 1 minimally invasive adenocarcinoma, 20 invasive adenocarcinomas. We analyzed the correlation between histopathology and the following CT-features: maximal axial diameter, maximal orthogonal axial diameter, height, density, size of solid component, air bronchogram, pleural retraction, nodule mass, disappearance rate and their evolution during follow-up.

RESULTS

In univariate analysis, invasive adenocarcinomas had a higher maximal height, density, solid component size, mass, a lower disappearance rate and presented more often with pleural retraction (p<0.05). After logistic regression performed with the uncorrelated parameters using a method of selection of variables, only the size of solid component remained significant, with 100% sensitivity for invasive adenocarcinoma when larger than 5mm.

CONCLUSION

Preoperative CT-features can help differentiating in situ and minimally invasive adenocarcinomas from invasive adenocarcinomas in Caucasian patients. A solid component larger than 5mm in diameter had 100% sensitivity for the diagnosis of invasive adenocarcinoma.

Volume estimation of low-contrast lesions with CT: a comparison of performances from a phantom study, simulations and theoretical analysis

Measurements of lung nodule volume with multi-detector computed tomography (MDCT) have been shown to be more accurate and precise compared to conventional lower dimensional measurements. Quantifying the size of lesions is potentially more difficult when the object-to-background contrast is low as with lesions in the liver. Physical phantom and simulation studies are often utilized to analyze the bias and variance of lesion size estimates because a ground truth or reference standard can be established. In addition, it may also be useful to derive theoretical bounds as another way of characterizing lesion sizing methods. The goal of this work was to study the performance of a MDCT system for a lesion volume estimation task with object-to-background contrast less than 50 HU, and to understand the relation among performances obtained from phantom study, simulation and theoretical analysis. We performed both phantom and simulation studies, and analyzed the bias and variance of volume measurements estimated by a matched-filter-based estimator. We further corroborated results with a theoretical analysis to estimate the achievable performance bound, which was the Cramer-Rao's lower bound (CRLB) of minimum variance for the size estimates. Results showed that estimates of non-attached solid small lesion volumes with object-to-background contrast of 31-46 HU can be accurate and precise, with less than 10.8% in percent bias and 4.8% in standard deviation of percent error (SPE), in standard dose scans. These results are consistent with theoretical (CRLB), computational (simulation) and empirical phantom bounds. The difference between the bounds is rather small (for SPE less than 1.9%) indicating that the theoretical- and simulation-based performance bounds can be good surrogates for physical phantom studies.

The revised lung adenocarcinoma classification-an imaging guide

Advances in our understanding of the pathology, radiology and clinical behaviour of peripheral lung adenocarcinomas facilitated a more robust terminology and classification of these lesions. The International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society (IASLC/ATS/ERS) classification introduced new terminology to better reflect this heterogeneous group of adenocarcinomas formerly known as bronchoalveolar cell carcinoma (BAC). There is now a clear distinction between pre-invasive, minimally invasive and frankly invasive lesions. The radiographic appearance of these ranges from pure ground glass nodules to solid mass lesions. Radiologists must be aware of the new classification in order to work alongside multidisciplinary colleagues to allow accurate staging and treatment. This article reviews the new classification of lung adenocarcinomas. Management options of these lesions with particular focus on radiological implications of the new classification will be reviewed.

Indeterminate pulmonary nodules: risk for having or for developing lung cancer?

This perspective discusses the report by Pinsky and colleagues, which addresses whether noncalcified pulmonary nodules identified on CT screening carry short- and long-term risk for lung cancer. We are facing challenges related to distinguishing a large majority of benign nodules from malignant ones and among those a majority of aggressive from indolent cancers. Key questions in determining individual probabilities of disease, given their history, findings on CT, and upcoming biomarkers of risk, remain most challenging. Reducing the false positives associated with current low-dose computed tomography practices and identification of individuals who need therapy and at what time during tumor surveillance could reduce costs and morbidities associated with unnecessary interventions.

The assessment of the role of baseline low-dose CT scan in patients at high risk of lung cancer

BACKGROUND

Despite the progress in contemporary medicine comprising diagnostic and therapeutic methods, lung cancer is still one of the biggest health concerns in many countries of the world. The main purpose of the study was to evaluate the detection rate of pulmonary nodules and lung cancer in the initial, helical low-dose CT of the chest as well as the analysis of the relationship between the size and the histopathological character of the detected nodules.

MATERIAL/METHODS

We retrospectively evaluated 1999 initial, consecutive results of the CT examinations performed within the framework of early lung cancer detection program initiated in Szczecin. The project enrolled persons of both sexes, aged 55-65 years, with at least 20 pack-years of cigarette smoking or current smokers. The analysis included assessment of the number of positive results and the evaluation of the detected nodules in relationship to their size. All of the nodules were classified into I of VI groups and subsequently compared with histopathological type of the neoplastic and nonneoplastic pulmonary lesions.

RESULTS

Pulmonary nodules were detected in 921 (46%) subjects. What is more, malignant lesions as well as lung cancer were significantly, more frequently discovered in the group of asymptomatic nodules of the largest dimension exceeding 15 mm.

CONCLUSIONS

The initial, low-dose helical CT of the lungs performed in high risk individuals enables detection of appreciable number of indeterminate pulmonary nodules. In most of the asymptomatic patients with histopathologically proven pulmonary nodules greater than 15 mm, the mentioned lesions are malignant, what warrants further, intensified diagnostics.

Accuracy of lung nodule volumetry in low-dose CT with iterative reconstruction: an anthropomorphic thoracic phantom study

OBJECTIVE

The purpose of this study was to assess accuracy of lung nodule volumetry in low-dose CT with application of iterative reconstruction (IR) according to nodule size, nodule density and CT tube currents, using artificial lung nodules within an anthropomorphic thoracic phantom.

METHODS

Eight artificial nodules (four diameters: 5, 8, 10 and 12 mm; two CT densities: -630 HU that represents ground-glass nodule and +100 HU that represents solid nodule) were randomly placed inside a thoracic phantom. Scans were performed with tube current-time product to 10, 20, 30 and 50 mAs. Images were reconstructed with IR and filtered back projection (FBP). We compared volume estimates to a reference standard and calculated the absolute percentage error (APE).

RESULTS

The APE of all nodules was significantly lower when IR was used than with FBP (7.5 ± 4.7% compared with 9.0 ±6.9%; p < 0.001). The effect of IR was more pronounced for smaller nodules (p < 0.001). IR showed a significantly lower APE than FBP in ground-glass nodules (p < 0.0001), and the difference was more pronounced at the lowest tube current (11.8 ± 5.9% compared with 21.3 ± 6.1%; p < 0.0001). The effect of IR was most pronounced for ground-glass nodules in the lowest CT tube current.

CONCLUSION

Lung nodule volumetry in low-dose CT by application of IR showed reliable accuracy in a phantom study. Lung nodule volumetry can be reliably applicable to all lung nodules including small, ground-glass nodules even in ultra-low-dose CT with application of IR.

ADVANCES IN KNOWLEDGE

IR significantly improved the accuracy of lung nodule volumetry compared with FBP particularly for ground-glass (-630 HU) nodules. Volumetry in low-dose CT can be utilized in patient with lung nodule work-up, and IR has benefit for small, ground-glass lung nodules in low-dose CT.

Volume and mass doubling times of persistent pulmonary subsolid nodules detected in patients without known malignancy

PURPOSE

To evaluate volume doubling time (VDT) and mass doubling time (MDT) of persistent pulmonary subsolid nodules (SSNs) followed-up with low-dose (LD) computed tomography (CT) in patients without a history of malignancy.

MATERIALS AND METHODS

This retrospective institutional review board-approved study, with waiver of patient informed consent, included 97 SSNs in 97 patients (45 men, 52 women; median age, 58 years; range, 37-87 years) in whom at least two LD CT scans were obtained, with 3-month or longer follow-up interval and median follow-up of 633 days. SSNs were categorized into pure ground-glass nodules (GGNs) (group A), part-solid GGNs with solid components of 5 mm or smaller (group B), and part-solid GGNs with solid components larger than 5 mm (group C). Three-dimensional manual segmentation for all SSNs was performed on initial and latest follow-up LD CT scans; subsequently, VDTs and MDTs were calculated and were compared among groups by using Kruskal-Wallis test, followed by the Dunn procedure with Bonferroni correction for volume-growing SSNs and mass-growing SSNs.

RESULTS

Volume growth was thus: 12 of 63 SSNs (19%), group A; nine of 23 SSNs (39%), group B; and eight of 11 SSNs (73%), group C. Median VDT was thus: 1832.3 days (range, 1230.7-4537.3 days), group A; 1228.5 days (range, 934.7-4617.7 days), group B; and 759.0 days (range, 376.4-941.5 days), group C. Mass growth was thus: 17 of 63 SSNs (27%), group A; 11 of 23 SSNs (48%), group B; and nine of 11 SSNs (82%), group C. Median MDT was 1556.1 days (range, 642.5-3564.5 days) for group A, 1199.9 days (range, 838.6-2578.7 days) for group B, and 627.7 days (range, 340.0-921.2 days) for group C. Median VDTs and MDTs of groups A and B were significantly longer than those of group C (P < .01).

CONCLUSION

Pure GGNs and part-solid GGNs with solid components of 5 mm or smaller show significantly longer VDTs and MDTs than do part-solid GGNs with solid components larger than 5 mm. Online supplemental material is available for this article.

Differentiation of lung neoplasms with lepidic growth and good prognosis from those with poor prognosis using computer-aided 3D volumetric CT analysis and FDG-PET

BACKGROUND

Many studies have reported that transverse computed tomography (CT) imaging findings correlate with prognosis of patients with small peripheral lung neoplasm with lepidic growth. However, no studies have examined this correlation with the aid of three-dimensional (3D) CT data.

PURPOSE

To determine the most efficacious imaging factor for differentiation of lepidic growth type lung neoplasms with good prognosis from those with poor prognosis.

MATERIAL AND METHODS

We evaluated CT findings, nodule patterns, SUVmax on FDG-PET/CT, as well as nodule volume and ratios of solid parts to nodule volume that were semi-automatically measured on CT images of 64 pulmonary nodules of ≤ 2 cm in 60 consecutive patients (24 men and 36 women; mean age, 65 years). For logistic modeling, we used all of the significant factors observed between the neoplasms with good and with poor prognosis as independent variables to estimate the statistically significant factors for discriminating invasive adenocarcinomas with lepidic growth (lesions with poor prognosis, n=42) from the other neoplasms, including preinvasive lesions (lesions with good prognosis, n=22), resulting in a recommendation for the optimal criterion for predicting lesions with poor prognosis.

RESULTS

The logistic regression model identified the ratio of the solid part to the whole volume of a pulmonary nodule as the only significant factor (P=0.04) for differentiating lepidic growth type lung neoplasms with good prognosis from those with poor prognosis. A ratio of 0.238 or more showed the highest discriminatory accuracy of 84% with 91% sensitivity and 76% specificity.

CONCLUSION

Computer-aided analyses of pulmonary nodules proved most useful for establishing the optimal criterion for differentiation of lepidic growth type lung neoplasms with good prognosis from those with poor prognosis.

Semi-automatic quantification of subsolid pulmonary nodules: comparison with manual measurements

Rationale

Accurate measurement of subsolid pulmonary nodules (SSN) is becoming increasingly important in the management of these nodules. SSNs were previously quantified with time-consuming manual measurements. The aim of the present study is to test the feasibility of semi-automatic SSNs measurements and to compare the results to the manual measurements.

Methods

In 33 lung cancer screening participants with 33 SSNs, the nodules were previously quantified by two observers manually. In the present study two observers quantified these nodules by using semi-automated nodule volumetry software. Nodules were quantified for effective diameter, volume and mass. The manual and semi-automatic measurements were compared using Bland-Altman plots and paired T tests. Observer agreement was calculated as an intraclass correlation coefficient. Data are presented as mean (SD).

Results

Semi-automated measurements were feasible in all 33 nodules. Nodule diameter, volume and mass were 11.2 (3.3) mm, 935 (691) ml and 379 (311) milligrams for observer 1 and 11.1 (3.7) mm, 986 (797) ml and 399 (344) milligrams for observer 2, respectively. Agreement between observers and within observer 1 for the semi-automatic measurements was good with an intraclass correlation coefficient >0.89. For observer 1 and observer 2, measured diameter was 8.8% and 10.3% larger (p<0.001), measured volume was 24.3% and 26.5% larger (p<0.001) and measured mass was 10.6% and 12.0% larger (p<0.001) with the semi-automatic program compared to the manual measurements.

Conclusion

Semi-automated measurement of the diameter, volume and mass of SSNs is feasible with good observer agreement. Semi-automated measurement makes quantification of mass and volume feasible in daily practice.

The association between baseline clinical-radiological characteristics and growth of pulmonary nodules with ground-glass opacity

INTRODUCTION

Pulmonary nodules with ground-glass opacity (GGO) are frequently encountered; there is little consensus on appropriate monitoring of them. The purpose of this study was to clarify which baseline clinical and radiological characteristics were associated with growth of these nodules.

METHODS

We retrospectively studied patients with pulmonary nodules that met the following criteria: (1) lesion diameter of ≤3 cm, (2) GGO proportion of ≥50%, and (3) observation without treatment in the prior 6 months. Between 1999 and 2013, 120 pulmonary lesions in 67 patients fulfilled inclusion criteria. We evaluated changes in lesion size on serial computed tomography. Two endpoints, "time to 2-mm growth" and "incidence of 2-mm growth", were analyzed using Cox proportional hazards and logistic regression models, respectively.

RESULTS

At the median observation period of 4.2 years, 34 lesions exhibited growth by ≥2 mm, whereas 86 remained unchanged. Smoking history and initial lesion diameter were statistically significant variables in both time-to-event and regression analyses. Hazard ratio (HR) for smoking history was 3.67 (P<0.01). Compared with those ≤1 cm, HRs for 1.1-2 cm and 2.1-3 cm lesions were 2.23 (P=0.08) and 5.08 (P=0.04), respectively. Odds ratio (OR) for smoking history was 6.51 (P<0.01); OR for lesion diameter of 1.1-3 cm (versus ≤1 cm) was 4.06 (P=0.02).

CONCLUSION

Smoking history and initial lesion diameter are robustly associated with GGO growth. These results suggest that large GGOs, especially in smokers, warrant close follow-up to accurately monitor lesion growth.

Minimum detectable change in lung nodule volume in a phantom CT study

RATIONALE AND OBJECTIVES

The change in volume of lung nodules is being examined as a measure of response to treatment. The aim of this study was to determine the minimum detectable change in nodule volume with the use of computed tomography.

MATERIALS AND METHODS

Four different layouts of synthetic nodules with different shapes but with the same size (5, 8, 9, or 10 mm) for each layout were placed within an anthropomorphic phantom and scanned with a 16-detector-row computed tomography scanner using multiple imaging parameters. Nodule volume estimates were determined using a previously developed matched-filter estimator. Analysis of volume change was then conducted as a detection problem. For each nodule size, the pooled distribution of volume estimates was shifted by a percentage c to simulate a changing nodule, while accounting for standard deviation. The value of c resulting in a prespecified area under the receiver operating characteristic curve (AUC) was deemed the minimum detectable change for that AUC value.

RESULTS

Both nodule size at baseline and choice of slice collimation protocol had an effect on the value of minimum detectable growth. For AUC = 0.95, the minimum detectable nodule growth in volume when using the thin-slice collimation protocol (16 × 0.75 mm) was 17%, 19%, and 15% for nodule sizes of 5, 8, and 9 mm, respectively.

CONCLUSIONS

Our results indicate that an approximate bound for detectable nodule growth in subcentimeter nodules may be relatively small, on the order of 20% or less in volume for a thin-slice CT acquisition protocol.

Nodule characterization: subsolid nodules

In this review, we focus on the radiologic, clinical, and pathologic aspects primarily of solitary subsolid pulmonary nodules. Particular emphasis will be placed on the pathologic classification and correlative computed tomography (CT) features of adenocarcinoma of the lung. The capabilities of fluorodeoxyglucose positron emission tomography-CT and histologic sampling techniques, including CT-guided biopsy, endoscopic-guided biopsy, and surgical resection, are discussed. Finally, recently proposed management guidelines by the Fleischner Society and the American College of Chest Physicians are reviewed.