Distribution of Solid Lung Nodules Presence and Size by Age and Sex in a Northern European Nonsmoking Population.

Background Most of the data regarding prevalence and size distribution of solid lung nodules originates from lung cancer screening studies that target high-risk populations or from Asian general cohorts. In recent years, the identification of lung nodules in non-high-risk populations, scanned for clinical indications, has increased. However, little is known about the presence of solid lung nodules in the Northern European nonsmoking population. Purpose To study the prevalence and size distribution of solid lung nodules by age and sex in a nonsmoking population. Materials and Methods Participants included nonsmokers (never or former smokers) from the population-based Imaging in Lifelines study conducted in the Northern Netherlands. Participants (age ≥ 45 years) with completed lung function tests underwent chest low-dose CT scans. Seven trained readers registered the presence and size of solid lung nodules measuring 30 mm3 or greater using semiautomated software. The prevalence and size of lung nodules (≥30 mm3), clinically relevant lung nodules (≥100 mm3), and actionable nodules (≥300 mm3) are presented by 5-year categories and by sex. Results A total of 10 431 participants (median age, 60.4 years [IQR, 53.8-70.8 years]; 56.6% [n = 5908] female participants; 46.1% [n = 4812] never smokers and 53.9% [n = 5619] former smokers) were included. Of these, 42.0% (n = 4377) had at least one lung nodule (male participants, 47.5% [2149 of 4523]; female participants, 37.7% [2228 of 5908]). The prevalence of lung nodules increased from age 45-49.9 years (male participants, 39.4% [219 of 556]; female participants, 27.7% [236 of 851]) to age 80 years or older (male participants, 60.7% [246 of 405]; female participants, 50.9% [163 of 320]). Clinically relevant lung nodules were present in 11.1% (1155 of 10 431) of participants, with prevalence increasing with age (male participants, 8.5%-24.4%; female participants, 3.7%-15.6%), whereas actionable nodules were present in 1.1%-6.4% of male participants and 0.6%-4.9% of female participants. Conclusion Lung nodules were present in a substantial proportion of all age groups in the Northern European nonsmoking population, with slightly higher prevalence for male participants than female participants. © RSNA, 2024 Supplemental material is available for this article.

Effect of emphysema on AI software and human reader performance in lung nodule detection from low-dose chest CT.

BACKGROUND: Emphysema influences the appearance of lung tissue in computed tomography (CT). We evaluated whether this affects lung nodule detection by artificial intelligence (AI) and human readers (HR). METHODS: Individuals were selected from the "Lifelines" cohort who had undergone low-dose chest CT. Nodules in individuals without emphysema were matched to similar-sized nodules in individuals with at least moderate emphysema. AI results for nodular findings of 30-100 mm3 and 101-300 mm3 were compared to those of HR; two expert radiologists blindly reviewed discrepancies. Sensitivity and false positives (FPs)/scan were compared for emphysema and non-emphysema groups. RESULTS: Thirty-nine participants with and 82 without emphysema were included (n = 121, aged 61 ± 8 years (mean ± standard deviation), 58/121 males (47.9%)). AI and HR detected 196 and 206 nodular findings, respectively, yielding 109 concordant nodules and 184 discrepancies, including 118 true nodules. For AI, sensitivity was 0.68 (95% confidence interval 0.57-0.77) in emphysema versus 0.71 (0.62-0.78) in non-emphysema, with FPs/scan 0.51 and 0.22, respectively (p = 0.028). For HR, sensitivity was 0.76 (0.65-0.84) and 0.80 (0.72-0.86), with FPs/scan of 0.15 and 0.27 (p = 0.230). Overall sensitivity was slightly higher for HR than for AI, but this difference disappeared after the exclusion of benign lymph nodes. FPs/scan were higher for AI in emphysema than in non-emphysema (p = 0.028), while FPs/scan for HR were higher than AI for 30-100 mm3 nodules in non-emphysema (p = 0.009). CONCLUSIONS: AI resulted in more FPs/scan in emphysema compared to non-emphysema, a difference not observed for HR. RELEVANCE STATEMENT: In the creation of a benchmark dataset to validate AI software for lung nodule detection, the inclusion of emphysema cases is important due to the additional number of FPs. KEY POINTS: • The sensitivity of nodule detection by AI was similar in emphysema and non-emphysema. • AI had more FPs/scan in emphysema compared to non-emphysema. • Sensitivity and FPs/scan by the human reader were comparable for emphysema and non-emphysema. • Emphysema and non-emphysema representation in benchmark dataset is important for validating AI.

Who is at risk of lung nodules on low-dose CT in a Western country? A population-based approach.

BACKGROUND: This population-based study aimed to identify the risk factors for lung nodules in a Western European general population. METHODS: We quantified the presence or absence of lung nodules among 12 055 participants of the Dutch population-based ImaLife (Imaging in Lifelines) study (age ≥45 years) who underwent low-dose chest computed tomography. Outcomes included the presence of 1) at least one solid lung nodule (volume ≥30 mm3) and 2) a clinically relevant lung nodule (volume ≥100 mm3). Fully adjusted multivariable logistic regression models were applied overall and stratified by smoking status to identify independent risk factors for the presence of nodules. RESULTS: Among the 12 055 participants (44.1% male; median age 60 years; 39.9% never-smokers; 98.7% White), we found lung nodules in 41.8% (5045 out of 12 055) and clinically relevant nodules in 11.4% (1377 out of 12 055); the corresponding figures among never-smokers were 38.8% and 9.5%, respectively. Factors independently associated with increased odds of having any lung nodule included male sex, older age, low educational level, former smoking, asbestos exposure and COPD. Among never-smokers, a family history of lung cancer increased the odds of both lung nodules and clinically relevant nodules. Among former and current smokers, low educational level was positively associated with lung nodules, whereas being overweight was negatively associated. Among current smokers, asbestos exposure and low physical activity were associated with clinically relevant nodules. CONCLUSIONS: The study provides a large-scale evaluation of lung nodules and associated risk factors in a Western European general population: lung nodules and clinically relevant nodules were prevalent, and never-smokers with a family history of lung cancer were a non-negligible group.

Predicted versus CT-derived total lung volume in a general population: The ImaLife study.

Predicted lung volumes based on the Global Lung Function Initiative (GLI) model are used in pulmonary disease detection and monitoring. It is unknown how well the predicted lung volume corresponds with computed tomography (CT) derived total lung volume (TLV). The aim of this study was to compare the GLI-2021 model predictions of total lung capacity (TLC) with CT-derived TLV. 151 female and 139 male healthy participants (age 45-65 years) were consecutively selected from a Dutch general population cohort, the Imaging in Lifelines (ImaLife) cohort. In ImaLife, all participants underwent low-dose, inspiratory chest CT. TLV was measured by an automated analysis, and compared to predicted TLC based on the GLI-2021 model. Bland-Altman analysis was performed for analysis of systematic bias and range between limits of agreement. To further mimic the GLI-cohort all analyses were repeated in a subset of never-smokers (51% of the cohort). Mean±SD of TLV was 4.7±0.9 L in women and 6.2±1.2 L in men. TLC overestimated TLV, with systematic bias of 1.0 L in women and 1.6 L in men. Range between limits of agreement was 3.2 L for women and 4.2 L for men, indicating high variability. Performing the analysis with never-smokers yielded similar results. In conclusion, in a healthy cohort, predicted TLC substantially overestimates CT-derived TLV, with low precision and accuracy. In a clinical context where an accurate or precise lung volume is required, measurement of lung volume should be considered.

CT-based emphysema characterization per lobe: A proof of concept.

PURPOSE: The Fleischner society criteria are global criteria to visually evaluate and classify pulmonary emphysema on CT. It may group heterogeneous disease severity within the same category, potentially obscuring clinically relevant differences in emphysema severity. This proof-of-concept study proposes to split emphysema into more categories and to assess each lobe separately, and applies this to two general population-based cohort samples to assess what information such an extension adds. METHOD: From a consecutive sample in two general population-based cohorts with low-dose chest CT, 117 participants with more than a trace of emphysema were included. Two independent readers performed an extended per-lobe classification and assessed overall severity semi-quantitatively. An emphysema sum score was determined by adding the severity score of all lobes. Inter-reader agreement was quantified with Krippendorff Alpha. RESULTS: Based on Fleischner society criteria, 69 cases had mild to severe centrilobular emphysema, and 90 cases had mild or moderate paraseptal emphysema (42 had both types of emphysema). The emphysema sum score was significantly different between mild (10.7 ± 4.3, range 2-22), moderate (20.1 ± 3.1, range: 15-24), and severe emphysema (23.6 ± 3.4, range: 17-28, p < 0.001), but ranges showed significant overlap. Inter-reader agreement for the extended classification and sum score was substantial (alpha 0.79 and 0.85, respectively). Distribution was homogenous across lobes in never-smokers, yet heterogenous in current smokers, with upper-lobe predominance. CONCLUSIONS: The proposed emphysema evaluation method adds information to the original Fleischner society classification. Individuals in the same Fleischner category have diverse emphysema sum scores, and lobar emphysema distribution differs between smoking groups.

CT characteristics of solid pulmonary nodules of never smokers versus smokers: A population-based study.

PURPOSE: Aim was to assess CT characteristics of lung nodules in never and former smokers compared to current smokers in a population-based setting. METHOD: We included individuals aged 45-60 years taking part in the ImaLife (Imaging in Lifelines) study, with at least one solid lung nodule (≥30 mm3) on low-dose chest CT. Qualitative (location, shape, margin, nodule type, attached structures) and quantitative (count, diameter, volume) nodule characteristics were evaluated. Based on Fleischner criteria, 'high risk' nodules were defined. To examine the association between smoking status and nodule CT characteristics of participants, multi-level multinomial logistic regression corrected for clustering of nodules within participants was performed, where all odds ratios (aORs) were adjusted for age and sex. RESULTS: Overall, 1,639 individuals (median age: 55.0, IQR:50.5-58.5, 50.5% men) were included, with 42.1% never smokers, 35.3% former smokers and 22.6% current smokers. A total of 3,222 solid nodules were identified; 39.7% of individuals had multiple nodules. Nodule size, location, type and attachment were similar for never compared to current smokers. The odds of nodules with an irregular shape and irregular margin was lower in never smokers (aOR:0.64, 95 %CI:0.44-0.93; aOR:0.60, 95 %CI:0.41-0.88, respectively) and former smokers (aOR:0.61, 95 %CI:0.41-0.90; aOR:0.57, 95 %CI:0.38-0.85, respectively) compared to current smokers. The odds of a detected nodule being 'high risk' was similar for never versus current smokers (never smokers: aOR = 0.90; 95% CI:0.73-1.11). CONCLUSIONS: CT-based characteristics of solid lung nodules in never and former smokers differed only slightly from current smokers. Among individuals with solid nodules, 'high-risk' nodules were equally common in never smokers and current smokers.

AI-Driven Model for Automatic Emphysema Detection in Low-Dose Computed Tomography Using Disease-Specific Augmentation.

The objective of this study is to evaluate the feasibility of a disease-specific deep learning (DL) model based on minimum intensity projection (minIP) for automated emphysema detection in low-dose computed tomography (LDCT) scans. LDCT scans of 240 individuals from a population-based cohort in the Netherlands (ImaLife study, mean age ± SD = 57 ± 6 years) were retrospectively chosen for training and internal validation of the DL model. For independent testing, LDCT scans of 125 individuals from a lung cancer screening cohort in the USA (NLST study, mean age ± SD = 64 ± 5 years) were used. Dichotomous emphysema diagnosis based on radiologists' annotation was used to develop the model. The automated model included minIP processing (slab thickness range: 1 mm to 11 mm), classification, and detection maps generation. The data-split for the pipeline evaluation involved class-balanced and imbalanced settings. The proposed DL pipeline showed the highest performance (area under receiver operating characteristics curve) for 11 mm slab thickness in both the balanced (ImaLife = 0.90 ± 0.05) and the imbalanced dataset (NLST = 0.77 ± 0.06). For ImaLife subcohort, the variation in minIP slab thickness from 1 to 11 mm increased the DL model's sensitivity from 75 to 88% and decreased the number of false-negative predictions from 10 to 5. The minIP-based DL model can automatically detect emphysema in LDCTs. The performance of thicker minIP slabs was better than that of thinner slabs. LDCT can be leveraged for emphysema detection by applying disease specific augmentation.

Improved precision of noise estimation in CT with a volume-based approach.

Assessment of image noise is a relevant issue in computed tomography (CT). Noise is routinely measured by the standard deviation of density values (Hounsfield units, HU) within a circular region of interest (ROI). We explored the effect of a spherical volume of interest (VOI) on noise measurements. Forty-nine chronic obstructive pulmonary disease patients underwent CT with clinical protocol (regular dose [RD], volumetric CT dose index [CTDIvol] 3.04 mGy, 64-slice unit), and ultra-low dose (ULD) protocol (median CTDIvol 0.38 mGy, dual-source unit). Noise was measured in 27 1-cm2 ROIs and 27 0.75-cm3 VOIs inside the trachea. Median true noise was 21 HU (range 17-29) for RD-CT and 33 HU (26-39) for ULD-CT. The VOI approach resulted in a lower mean distance between limits of agreement compared to ROI: 5.9 versus 10.0 HU for RD-CT (-40%); 4.7 versus 9.9 HU for ULD-CT (-53%). Mean systematic bias barely changed: -1.6 versus -0.9HU for RD-CT; 0.0 to 0.4HU for ULD-CT. The average measurement time was 6.8 s (ROI) versus 9.7 (VOI), independent of dose level. For chest CT, measuring noise with a VOI-based instead of a ROI-based approach reduces variability by 40-53%, without a relevant effect on systematic bias and measurement time.

Seasonal prevalence and characteristics of low-dose CT detected lung nodules in a general Dutch population.

We investigated whether presence and characteristics of lung nodules in the general population using low-dose computed tomography (LDCT) varied by season. Imaging in Lifelines (ImaLife) study participants who underwent chest LDCT-scanning between October 2018 and October 2019 were included in this sub-study. Hay fever season (summer) was defined as 1st April to 30th September and Influenza season (winter) as 1st October to 31st March. All lung nodules with volume of ≥ 30 mm3 (approximately 3 mm in diameter) were registered. In total, 2496 lung nodules were found in 1312 (38%) of the 3456 included participants (nodules per participant ranging from 1 to 21, median 1). In summer, 711 (54%) participants had 1 or more lung nodule(s) compared to 601 (46%) participants in winter (p = 0.002). Of the spherical, perifissural and left-upper-lobe nodules, relatively more were detected in winter, whereas of the polygonal-, irregular-shaped and centrally-calcified nodules, relatively more were detected in summer. Various seasonal diseases with inflammation as underlying pathophysiology may influence presence and characteristics of lung nodules. Further investigation into underlying pathophysiology using short-term LDCT follow-up could help optimize the management strategy for CT-detected lung nodules in clinical practice.

Lung cancer prediction by Deep Learning to identify benign lung nodules.

INTRODUCTION: Deep Learning has been proposed as promising tool to classify malignant nodules. Our aim was to retrospectively validate our Lung Cancer Prediction Convolutional Neural Network (LCP-CNN), which was trained on US screening data, on an independent dataset of indeterminate nodules in an European multicentre trial, to rule out benign nodules maintaining a high lung cancer sensitivity. METHODS: The LCP-CNN has been trained to generate a malignancy score for each nodule using CT data from the U.S. National Lung Screening Trial (NLST), and validated on CT scans containing 2106 nodules (205 lung cancers) detected in patients from from the Early Lung Cancer Diagnosis Using Artificial Intelligence and Big Data (LUCINDA) study, recruited from three tertiary referral centers in the UK, Germany and Netherlands. We pre-defined a benign nodule rule-out test, to identify benign nodules whilst maintaining a high sensitivity, by calculating thresholds on the malignancy score that achieve at least 99 % sensitivity on the NLST data. Overall performance per validation site was evaluated using Area-Under-the-ROC-Curve analysis (AUC). RESULTS: The overall AUC across the European centers was 94.5 % (95 %CI 92.6-96.1). With a high sensitivity of 99.0 %, malignancy could be ruled out in 22.1 % of the nodules, enabling 18.5 % of the patients to avoid follow-up scans. The two false-negative results both represented small typical carcinoids. CONCLUSION: The LCP-CNN, trained on participants with lung nodules from the US NLST dataset, showed excellent performance on identification of benign lung nodules in a multi-center external dataset, ruling out malignancy with high accuracy in about one fifth of the patients with 5-15 mm nodules.

Computed Tomography Screening for Early Lung Cancer, COPD and Cardiovascular Disease in Shanghai: Rationale and Design of a Population-based Comparative Study.

RATIONALE AND OBJECTIVES: To describe the rational and design of a population-based comparative study. The objective of the study is to assess the screening performance of volume-based management of CT-detected lung nodule in comparison to diameter-based management, and to improve the effectiveness of CT screening for chronic obstructive pulmonary disease (COPD) and cardiovascular disease (CVD), in addition to lung cancer, based on quantitative measurement of CT imaging biomarkers in a Chinese screening setting. MATERIALS AND METHODS: A population-based comparative study is being performed, including 10,000 asymptomatic participants between 40 and 74 years old from Shanghai urban population. Participants in the intervention group undergo a low-dose chest and cardiac CT scan at baseline and 1 year later, and are managed according to NELCIN-B3 protocol. Participants in the control group undergo a low-dose chest CT scan according to the routine CT protocol and are managed according to the clinical practice. Epidemiological data are collected through questionnaires. In the fourth year from baseline, the diagnosis of the three diseases will be collected. RESULTS: The unnecessary referral rate will be compared between NELCIN-B3 and standard protocol for managing early-detected lung nodules. The effectiveness of quantitative measurement of CT imaging biomarkers for early detection of lung cancer, COPD and CVD will be evaluated. CONCLUSION: We expect that the quantitative assessment of the CT imaging biomarkers will reduce the number of unnecessary referrals for early detected lung nodules, and will improve the early detection of COPD and CVD in a Chinese urban population. TRIAL REGISTRATION: ClinicalTrials.gov, NCT03988322. Registered on 14 June 2019.

Cardiovascular Risk Factors and Coronary Calcification in a Middle-aged Dutch Population: The ImaLife Study.

PURPOSE: To assess the presence of coronary artery calcium (CAC) and its association with cardiovascular risk factors and Systematic COronary Risk Evaluation (SCORE) risk in a middle-aged Dutch population. METHODS: Classic cardiovascular risk factors and CAC were analyzed in 4083 participants aged 45 to 60 years (57.9% women) from the population-based ImaLife study. CAC scores were quantified on noncontrast cardiac CT scans. Age-specific and sex-specific distribution of CAC categories (0, 1 to 99, 100 to 299, ≥300) and percentiles were determined. SCORE risk categories (<1%, ≥1% to 5%, and ≥5%) were compared with CAC distribution. Population attributable fractions (PAFs) of classic risk factors for CAC were estimated. RESULTS: CAC was present in 54.5% male and 26.5% female participants. The percentage of individuals with CAC increased with increasing age. Mean SCORE was 2.0% in men and 0.7% in women. In SCORE <1%, 32.7% of men and 17.1% of women had CAC. In men with SCORE ≥5%, 26.9% had no CAC. Only 0.1% of women had SCORE ≥5%. PAF of classic risk factors for CAC was 18.5% in men and 31.4% in women. PAF was highest for hypertension (in men 8.0%, 95% confidence interval, 4.2%-11.8%; in women 13.1%, 95% confidence interval, 7.9%-18.2%) followed by hypercholesterolemia and obesity. CONCLUSION: In this middle-aged cohort, more than half of the men and a quarter of the women had CAC. One out of 4 men at high risk (SCORE ≥5%) could be placed into a lower risk category owing to absence of CAC. Thus, adding CAC scoring to SCORE could have considerable effect on cardiovascular risk classification. Elimination of exposure to classic risk factors could reduce limited proportion of CAC in a middle-aged population.

High-pitch dual-source CT for coronary artery calcium scoring: A head-to-head comparison of non-triggered chest versus triggered cardiac acquisition.

BACKGROUND: To determine the effect of low-dose, high-pitch non-electrocardiographic (ECG)-triggered chest CT on coronary artery calcium (CAC) detection, quantification and risk stratification, compared to ECG-triggered cardiac CT. METHODS: We selected 1,000 participants from the ImaLife study, 50% with coronary calcification on cardiac CT. All participants underwent non-contrast cardiac CT followed by chest CT using third-generation dual-source technology. Reconstruction settings were equal for both acquisitions. CAC scores were determined by Agatston's method, and divided dichotomously (0, >0), and into risk categories (0, 1-99, 100-399, ≥400). We investigated the influence of heart rate and body mass index (BMI) on risk reclassification. RESULTS: Positive CAC scores on cardiac CT ranged from 1 to 6926 (median 39). Compared to cardiac CT, chest CT had sensitivity of 0.96 (95%CI 0.94-0.98) and specificity of 0.99 (95%CI 0.97-0.99) for CAC detection (κ = 0.95). In participants with coronary calcification on cardiac CT, CAC score on chest CT was lower than on cardiac CT (median 30 versus 40, p˂0.001). Agreement in CAC-based risk strata was excellent (weighted κ = 0.95). Sixty-five cases (6.5%) were reclassified by one risk category in chest CT, with fifty-five (84.6%) shifting downward. Higher BMI resulted in higher reclassification rate (13% for BMI ≥30 versus 5.2% for BMI <30, p = 0.001), but there was no effect of heart rate. CONCLUSION: Low-dose, high-pitch chest CT, using third-generation dual-source technology shows almost perfect agreement with cardiac CT in CAC detection and risk stratification. However, low-dose chest CT mainly underestimates the CAC score as compared to cardiac CT, and results in inaccurate risk categorization in BMI ≥30.

Evaluation of a novel deep learning-based classifier for perifissural nodules.

OBJECTIVES: To evaluate the performance of a novel convolutional neural network (CNN) for the classification of typical perifissural nodules (PFN). METHODS: Chest CT data from two centers in the UK and The Netherlands (1668 unique nodules, 1260 individuals) were collected. Pulmonary nodules were classified into subtypes, including "typical PFNs" on-site, and were reviewed by a central clinician. The dataset was divided into a training/cross-validation set of 1557 nodules (1103 individuals) and a test set of 196 nodules (158 individuals). For the test set, three radiologically trained readers classified the nodules into three nodule categories: typical PFN, atypical PFN, and non-PFN. The consensus of the three readers was used as reference to evaluate the performance of the PFN-CNN. Typical PFNs were considered as positive results, and atypical PFNs and non-PFNs were grouped as negative results. PFN-CNN performance was evaluated using the ROC curve, confusion matrix, and Cohen's kappa. RESULTS: Internal validation yielded a mean AUC of 91.9% (95% CI 90.6-92.9) with 78.7% sensitivity and 90.4% specificity. For the test set, the reader consensus rated 45/196 (23%) of nodules as typical PFN. The classifier-reader agreement (k = 0.62-0.75) was similar to the inter-reader agreement (k = 0.64-0.79). Area under the ROC curve was 95.8% (95% CI 93.3-98.4), with a sensitivity of 95.6% (95% CI 84.9-99.5), and specificity of 88.1% (95% CI 81.8-92.8). CONCLUSION: The PFN-CNN showed excellent performance in classifying typical PFNs. Its agreement with radiologically trained readers is within the range of inter-reader agreement. Thus, the CNN-based system has potential in clinical and screening settings to rule out perifissural nodules and increase reader efficiency. KEY POINTS: • Agreement between the PFN-CNN and radiologically trained readers is within the range of inter-reader agreement. • The CNN model for the classification of typical PFNs achieved an AUC of 95.8% (95% CI 93.3-98.4) with 95.6% (95% CI 84.9-99.5) sensitivity and 88.1% (95% CI 81.8-92.8) specificity compared to the consensus of three readers.

Early detection of heart function abnormality by native T1: a comparison of two T1 quantification methods.

OBJECTIVE: To compare the robustness of native T1 mapping using mean and median pixel-wise quantification methods. METHODS: Fifty-seven consecutive patients without overt signs of heart failure were examined in clinical routine for suspicion of cardiomyopathy. MRI included the acquisition of native T1 maps by a motion-corrected modified Look-Locker inversion recovery sequence at 1.5 T. Heart function status according to four established volumetric left ventricular (LV) cardio MRI parameter thresholds was used for retrospective separation into subgroups of normal (n = 26) or abnormal heart function (n = 31). Statistical normality of pixel-wise T1 was tested on each myocardial segment and mean and median segmental T1 values were assessed. RESULTS: Segments with normally distributed pixel-wise T1 (57/58%) showed no difference between mean and median quantification in either patient group, while differences were highly significant (p < 0.001) for the respective 43/42% non-normally distributed segments. Heart function differentiation between two patient groups was significant in 14 myocardial segments (p < 0.001-0.040) by median quantification compared with six (p < 0.001-0.042) by using the mean. The differences by median quantification were observed between the native T1 values of the three coronary artery territories of normal heart function patients (p = 0.023) and insignificantly in the abnormal patients (p = 0.053). CONCLUSION: Median quantification increases the robustness of myocardial native T1 definition, regardless of statistical normality of the data. Compared with the currently prevailing method of mean quantification, differentiation between LV segments and coronary artery territories is better and allows for earlier detection of heart function impairment. KEY POINTS: • Median pixel-wise quantification of native T1 maps is robust and can be applied regardless of the statistical distribution of data points. • Median quantification is more sensitive to early heart function abnormality compared with mean quantification. • The new method yields significant native T1 value differentiation between the three coronary artery territories.

Early imaging biomarkers of lung cancer, COPD and coronary artery disease in the general population: rationale and design of the ImaLife (Imaging in Lifelines) Study.

Lung cancer, chronic obstructive pulmonary disease (COPD), and coronary artery disease (CAD) are expected to cause most deaths by 2050. State-of-the-art computed tomography (CT) allows early detection of lung cancer and simultaneous evaluation of imaging biomarkers for the early stages of COPD, based on pulmonary density and bronchial wall thickness, and of CAD, based on the coronary artery calcium score (CACS), at low radiation dose. To determine cut-off values for positive tests for elevated risk and presence of disease is one of the major tasks before considering implementation of CT screening in a general population. The ImaLife (Imaging in Lifelines) study, embedded in the Lifelines study, is designed to establish the reference values of the imaging biomarkers for the big three diseases in a well-defined general population aged 45 years and older. In total, 12,000 participants will undergo CACS and chest acquisitions with latest CT technology. The estimated percentage of individuals with lung nodules needing further workup is around 1-2%. Given the around 10% prevalence of COPD and CAD in the general population, the expected number of COPD and CAD is around 1000 each. So far, nearly 4000 participants have been included. The ImaLife study will allow differentiation between normal aging of the pulmonary and cardiovascular system and early stages of the big three diseases based on low-dose CT imaging. This information can be finally integrated into personalized precision health strategies in the general population.

New Fissure-Attached Nodules in Lung Cancer Screening: A Brief Report From The NELSON Study.

INTRODUCTION: In incidence lung cancer screening rounds, new pulmonary nodules are regular findings. They have a higher lung cancer probability than baseline nodules. Previous studies have shown that baseline perifissural nodules (PFNs) represent benign lesions. Whether this is also the case for incident PFNs is unknown. This study evaluated newly detected nodules in the Dutch-Belgian randomized-controlled NELSON study with respect to incidence of fissure-attached nodules, their classification, and lung cancer probability. METHODS: Within the NELSON trial, 7557 participants underwent baseline screening between April 2004 and December 2006. Participants with new nodules detected after baseline were included. Nodules were classified based on location and attachment. Fissure-attached nodules were re-evaluated to be classified as typical, atypical, or non-PFN by two radiologists without knowledge of participant lung cancer status. RESULTS: One thousand four hundred eighty-four new nodules were detected in 949 participants (77.4% male, median age 59 years [interquartile range: 55-63 years]) in the second, third, and final NELSON screening round. Based on 2-year follow-up or pathology, 1393 nodules (93.8%) were benign. In total, 97 (6.5%) were fissure-attached, including 10 malignant nodules. None of the new fissure-attached malignant nodules was classified as typical or atypical PFN. CONCLUSIONS: In the NELSON study, 6.5% of incident lung nodules were fissure-attached. None of the lung cancers that originated from a new fissure-attached nodule in the incidence lung cancer screening rounds was classified as a typical or atypical PFN. Our results suggest that also in the case of a new PFN, it is highly unlikely that these PFNs will be diagnosed as lung cancer.

Potential for dose reduction in CT emphysema densitometry with post-scan noise reduction: a phantom study.

OBJECTIVE: The aim of this phantom study was to investigate the effect of scan parameters and noise suppression techniques on the minimum radiation dose for acceptable image quality for CT emphysema densitometry. METHODS: The COPDGene phantom was scanned on a third generation dual-source CT system with 16 scan setups (CTDIvol 0.035-10.680 mGy). Images were reconstructed at 1.0/0.7 mm slice thickness/increment, with three kernels (one soft, two hard), filtered backprojection and three grades of third-generation iterative reconstruction (IR). Additionally, deep learning-based noise suppression software was applied. Main outcomes: overlap in area of the normalized histograms of CT density for the emphysema insert and lung material, and the radiation dose required for a maximum of 4.3% overlap (defined as acceptable image quality). RESULTS: In total, 384 scan reconstructions were analyzed. Decreasing radiation dose resulted in an exponential increase of the overlap in normalized histograms of CT density. The overlap was 11-91% for the lowest dose setting (CTDIvol 0.035mGy). The soft kernel reconstruction showed less histogram overlap than hard filter kernels. IR and noise suppression also reduced overlap. Using intermediate grade IR plus noise suppression software allowed for 85% radiation dose reduction while maintaining acceptable image quality. CONCLUSION: CT density histogram overlap can quantify the degree of discernibility of emphysema and healthy lung tissue. Noise suppression software, IR, and soft reconstruction kernels substantially decrease the dose required for acceptable image quality. ADVANCES IN KNOWLEDGE: Noise suppression software, IR, and soft reconstruction kernels allow radiation dose reduction by 85% while still allowing differentiation between emphysema and normal lung tissue.

Methods of computed tomography screening and management of lung cancer in Tianjin: design of a population-based cohort study.

OBJECTIVE: European lung cancer screening studies using computed tomography (CT) have shown that a management protocol based on measuring lung nodule volume and volume doubling time (VDT) is more specific for early lung cancer detection than a diameter-based protocol. However, whether this also applies to a Chinese population is unclear. The aim of this study is to compare the diagnostic performance of a volume-based protocol with a diameter-based protocol for lung cancer detection and optimize the nodule management criteria for a Chinese population. METHODS: This study has a population-based, prospective cohort design and includes 4000 participants from the Hexi district of Tianjin, China. Participants will undergo low-dose chest CT at baseline and after 1 year. Initially, detected lung nodules will be evaluated for diameter and managed according to a routine diameter-based protocol (Clinical Practice Guideline in Oncology for Lung Cancer Screening, Version 2.2018). Subsequently, lung nodules will be evaluated for volume and management will be simulated according to a volume-based protocol and VDT (a European lung nodule management protocol). Participants will be followed up for 4 years to evaluate lung cancer incidence and mortality. The primary outcome is the diagnostic performance of the European volume-based protocol compared to diameter-based management regarding lung nodules detected using low-dose CT. RESULTS: The diagnostic performance of volume- and diameter-based management for lung nodules in a Chinese population will be estimated and compared. CONCLUSIONS: Through the study, we expect to improve the management of lung nodules and early detection of lung cancer in Chinese populations.