Recent advances in the three-dimensional (3D) printing industry have enabled clinicians to explore the use of 3D printing in preprocedural planning, biomedical tissue modeling, and direct implantable device manufacturing. Despite the increased adoption of rapid prototyping and additive manufacturing techniques in the health-care field, many physicians lack the technical skill set to use this exciting and useful technology. Additionally, the growth in the 3D printing sector brings an ever-increasing number of 3D printers and printable materials. Therefore, it is important for clinicians to keep abreast of this rapidly developing field in order to benefit. In this Ahead of the Curve, we review the history of 3D printing from its inception to the most recent biomedical applications. Additionally, we will address some of the major barriers to wider adoption of the technology in the medical field. Finally, we will provide an initial guide to 3D modeling and printing by demonstrating how to design a personalized airway prosthesis via 3D Slicer. We hope this information will reduce the barriers to use and increase clinician participation in the 3D printing health-care sector.

Emphysema is one of the hallmarks of Chronic Obstructive Pulmonary Disorder (COPD), a devastating lung disease often caused by smoking. Emphysema appears on Computed Tomography (CT) scans as a variety of textures that correlate with disease subtypes. It has been shown that the disease subtypes and textures are linked to physiological indicators and prognosis, although neither is well characterized clinically. Most previous computational approaches to modeling emphysema imaging data have focused on supervised classification of lung textures in patches of CT scans. In this work, we describe a generative model that jointly captures heterogeneity of disease subtypes and of the patient population. We also describe a corresponding inference algorithm that simultaneously discovers disease subtypes and population structure in an unsupervised manner. This approach enables us to create image-based descriptors of emphysema beyond those that can be identified through manual labeling of currently defined phenotypes. By applying the resulting algorithm to a large data set, we identify groups of patients and disease subtypes that correlate with distinct physiological indicators.

BACKGROUND: Cardiovascular diseases are frequent and a major cause of death in patients with chronic obstructive pulmonary disease (COPD). In the general population, various fat depots including abdominal visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), and liver fat have been linked to increased risk of cardiovascular diseases. We hypothesize that these adipose tissue compartments are associated with myocardial infarction (MI) in patients with COPD. METHODS: We collected measures of VAT and SAT areas and liver attenuation on the computed tomography scan of the chest from 1267 patients with COPD. MI was a self-reported physician-diagnosed outcome. The association between fat depots and self-reported history of MI was assessed by logistic regression analysis in which the patients within the 2 lowest tertiles of VAT and SAT areas were the reference group. RESULTS: Eighty three patients (6.6%) reported a history of MI at the time of enrollment. Compared to patients who did not have an MI episode, those who had a prior MI had a higher VAT area (mean ± SD, 303.4 ± 208.5 vs. 226.8 ± 172.6 cm; P=0.002) with no differences in SAT area and liver fat. After adjustment for age, gender, obesity, pack years of smoking, hypertension, high cholesterol, and diabetes, patients within the upper tertile (vs. those in the lower tertiles) of VAT area had increased odds of MI (odds ratio [OR] 1.86, 95% confidence interval [CI] 1.02 - 3.41). CONCLUSION: Increased abdominal visceral fat is independently associated with a history of MI in individuals with COPD.

PURPOSE: To investigate the prevalence and CT image characteristics of anterior mediastinal masses in a population-based cohort and their association with the demographics of the participants. MATERIALS AND METHODS: Chest CT scans of 2571 Framingham Heart Study participants (mean age 58.9 years, 51% female) were evaluated by two board-certified radiologists with expertise in thoracic imaging for the presence of anterior mediastinal masses, their shape, contour, location, invasion of adjacent structures, fat content, and calcification. For participants with anterior mediastinal masses, a previous cardiac CT scan was reviewed for interval size change of the masses, when available. The demographics of the participants were studied for any association with the presence of anterior mediastinal masses. RESULTS: Of 2571, 23 participants (0.9%, 95% CI: 0.6 to 1.3) had anterior mediastinal masses on CT. The most common CT characteristics were oval shape, lobular contour, and midline location, showing soft tissue density (median 32.1 HU). Fat content was detected in a few cases (9%, 2/23). Six out of eight masses with available prior cardiac CT scans demonstrated an interval growth over a median period of 6.5 years. No risk factors for anterior mediastinal masses were detected among participants' demographics, including age, sex, BMI, and cigarette smoking. CONCLUSIONS: The prevalence of anterior mediastinal masses is 0.9% in the Framingham Heart Study. Those masses may increase in size when observed over 5-7 years. Investigation of clinical significance in incidentally found anterior mediastinal masses with a longer period of follow-up would be necessary.

BACKGROUND AND PURPOSE: Right Ventricular to Left Ventricular (RV/LV) diameter ratio has been shown to be a prognostic biomarker for patients suffering from acute Pulmonary Embolism (PE). While Computed Tomography Pulmonary Angiography (CTPA) images used to confirm a clinical suspicion of PE do include information of the heart, a numerical RV/LV diameter ratio is not universally reported, likely because of lack in training, inter-reader variability in the measurements, and additional effort by the radiologist. This study designs and validates a completely automated Computer Aided Detection (CAD) system to compute the axial RV/LV diameter ratio from CTPA images so that the RV/LV diameter ratio can be a more objective metric that is consistently reported in patients for whom CTPA diagnoses PE. MATERIALS AND METHODS: The CAD system was designed specifically for RV/LV measurements. The system was tested in 198 consecutive CTPA patients with acute PE. Its accuracy was evaluated using reference standard RV/LV radiologist measurements and its prognostic value was established for 30-day PE-specific mortality and a composite outcome of 30-day PE-specific mortality or the need for intensive therapies. The study was Institutional Review Board (IRB) approved and HIPAA compliant. RESULTS: The CAD system analyzed correctly 92.4% (183/198) of CTPA studies. The mean difference between automated and manually computed axial RV/LV ratios was 0.03±0.22. The correlation between the RV/LV diameter ratio obtained by the CAD system and that obtained by the radiologist was high (r=0.81). Compared to the radiologist, the CAD system equally achieved high accuracy for the composite outcome, with areas under the receiver operating characteristic curves of 0.75 vs. 0.78. Similar results were found for 30-days PE-specific mortality, with areas under the curve of 0.72 vs. 0.75. CONCLUSIONS: An automated CAD system for determining the CT derived RV/LV diameter ratio in patients with acute PE has high accuracy when compared to manual measurements and similar prognostic significance for two clinical outcomes.

PURPOSE: The purpose of this work is to develop a fully automated pipeline to compute aorta morphology and calcification measures in large cohorts of CT scans that can be used to investigate the potential of these measures as imaging biomarkers of cardiovascular disease. METHODS: The first step of the automated pipeline is aorta segmentation. The algorithm the authors propose first detects an initial aorta boundary by exploiting cross-sectional circularity of aorta in axial slices and aortic arch in reformatted oblique slices. This boundary is then refined by a 3D level-set segmentation that evolves the boundary to the location of nearby edges. The authors then detect the aortic calcifications with thresholding and filter out the false positive regions due to nearby high intensity structures based on their anatomical location. The authors extract the centerline and oblique cross sections of the segmented aortas and compute the aorta morphology and calcification measures of the first 2500 subjects from COPDGene study. These measures include volume and number of calcified plaques and measures of vessel morphology such as average cross-sectional area, tortuosity, and arch width. RESULTS: The authors computed the agreement between the algorithm and expert segmentations on 45 CT scans and obtained a closest point mean error of 0.62 ± 0.09 mm and a Dice coefficient of 0.92 ± 0.01. The calcification detection algorithm resulted in an improved true positive detection rate of 0.96 compared to previous work. The measurements of aorta size agreed with the measurements reported in previous work. The initial results showed associations of aorta morphology with calcification and with aging. These results may indicate aorta stiffening and unwrapping with calcification and aging. CONCLUSIONS: The authors have developed an objective tool to assess aorta morphology and aortic calcium plaques on CT scans that may be used to provide information about the presence of cardiovascular disease and its clinical impact in smokers.

BACKGROUND: COPD includes the chronic bronchitis (CB) and emphysema phenotypes. Although it is generally assumed that emphysema or chronic airflow obstruction (CAO) is associated with worse quality of life (QOL) than is CB, this assumption has not been tested. METHODS: The current study's analyses from the Lovelace Smokers' Cohort (LSC) were validated in the COPD Gene Cohort (COPDGene). CB without CAO (CB only) was defined as self-reported cough productive of phlegm for ≥ 3 mo/y for 2 consecutive years and postbronchodilator FEV1/FVC ≥ 70%. CAO without CB (CAO only) was defined as a postbronchodilator FEV1/FVC < 70% with no evidence of CB. QOL outcomes were obtained from the St. George's Respiratory Questionnaire (SGRQ) and the 36-Item Short Form Health Survey (SF-36) questionnaires. A priori covariates included age, sex, pack-years of smoking, current smoking, and FEV1. RESULTS: Smokers with CB without CAO (LSC = 341; COPDGene = 523) were younger and had a greater BMI and less smoking exposure than did those with CAO only (LSC = 302; COPDGene = 2,208). Compared with the latter group, QOL scores were worse for those with CB only. Despite similar SGRQ Activity and SF-36 Role Physical and Physical Functioning, SGRQ Symptoms and Impact scores and SF-36 emotional and social measures were worse in the CB-only group, in both cohorts. After adjustment for covariates, the CB-only group remained a significant predictor for "worse" symptoms and emotional and social measures. CONCLUSIONS: To our knowledge, this analysis is the first to suggest that among subjects with COPD, those with CB only present worse QOL symptoms and mental well-being than do those with CAO only.

IMPORTANCE: Airflow obstruction on spirometry is universally used to define chronic obstructive pulmonary disease (COPD), and current or former smokers without airflow obstruction may assume that they are disease free. OBJECTIVE: To identify clinical and radiologic evidence of smoking-related disease in a cohort of current and former smokers who did not meet spirometric criteria for COPD, for whom we adopted the discarded label of Global Initiative for Obstructive Lung Disease (GOLD) 0. DESIGN, SETTING, AND PARTICIPANTS: Individuals from the Genetic Epidemiology of COPD (COPDGene) cross-sectional observational study completed spirometry, chest computed tomography (CT) scans, a 6-minute walk, and questionnaires. Participants were recruited from local communities at 21 sites across the United States. The GOLD 0 group (n = 4388) (ratio of forced expiratory volume in the first second of expiration [FEV1] to forced vital capacity >0.7 and FEV1 ≥80% predicted) from the COPDGene study was compared with a GOLD 1 group (n = 794), COPD groups (n = 3690), and a group of never smokers (n = 108). Recruitment began in January 2008 and ended in July 2011. MAIN OUTCOMES AND MEASURES: Physical function impairments, respiratory symptoms, CT abnormalities, use of respiratory medications, and reduced respiratory-specific quality of life. RESULTS: One or more respiratory-related impairments were found in 54.1% (2375 of 4388) of the GOLD 0 group. The GOLD 0 group had worse quality of life (mean [SD] St George's Respiratory Questionnaire total score, 17.0 [18.0] vs 3.8 [6.8] for the never smokers; P < .001) and a lower 6-minute walk distance, and 42.3% (127 of 300) of the GOLD 0 group had CT evidence of emphysema or airway thickening. The FEV1 percent predicted distribution and mean for the GOLD 0 group were lower but still within the normal range for the population. Current smoking was associated with more respiratory symptoms, but former smokers had greater emphysema and gas trapping. Advancing age was associated with smoking cessation and with more CT findings of disease. Individuals with respiratory impairments were more likely to use respiratory medications, and the use of these medications was associated with worse disease. CONCLUSIONS AND RELEVANCE: Lung disease and impairments were common in smokers without spirometric COPD. Based on these results, we project that there are 35 million current and former smokers older than 55 years in the United States who may have unrecognized disease or impairment. The effect of chronic smoking on the lungs and the individual is substantially underestimated when using spirometry alone.

BACKGROUND: Evidence suggests that individuals with interstitial lung abnormalities (ILA) on a chest computed tomogram (CT) may have an increased risk to develop a clinically significant interstitial lung disease (ILD). Although methods used to identify individuals with ILA on chest CT have included both automated quantitative and qualitative visual inspection methods, there has been not direct comparison between these two methods. To investigate this relationship, we created lung density metrics and compared these to visual assessments of ILA. METHODS: To provide a comparison between ILA detection methods based on visual assessment we generated measures of high attenuation areas (HAAs, defined by attenuation values between -600 and -250 Hounsfield Units) in >4500 participants from both the COPDGene and Framingham Heart studies (FHS). Linear and logistic regressions were used for analyses. RESULTS: Increased measures of HAAs (in ≥ 10 % of the lung) were significantly associated with ILA defined by visual inspection in both cohorts (P < 0.0001); however, the positive predictive values were not very high (19 % in COPDGene and 13 % in the FHS). In COPDGene, the association between HAAs and ILA defined by visual assessment were modified by the percentage of emphysema and body mass index. Although increased HAAs were associated with reductions in total lung capacity in both cohorts, there was no evidence for an association between measurement of HAAs and MUC5B promoter genotype in the FHS. CONCLUSION: Our findings demonstrate that increased measures of lung density may be helpful in determining the severity of lung volume reduction, but alone, are not strongly predictive of ILA defined by visual assessment. Moreover, HAAs were not associated with MUC5B promoter genotype.

RATIONALE: Interstitial lung disease (ILD), a leading cause of morbidity and mortality in rheumatoid arthritis (RA), is highly prevalent, yet RA-ILD is underrecognized. OBJECTIVES: To identify clinical risk factors, autoantibodies, and biomarkers associated with the presence of RA-ILD. METHODS: Subjects enrolled in Brigham and Women's Hospital Rheumatoid Arthritis Sequential Study (BRASS) and American College of Rheumatology (ACR) cohorts were evaluated for ILD. Regression models were used to assess the association between variables of interest and RA-ILD. Receiver operating characteristic curves were generated in BRASS to determine if a combination of clinical risk factors and autoantibodies can identify RA-ILD and if the addition of investigational biomarkers is informative. This combinatorial signature was subsequently tested in ACR. MEASUREMENTS AND MAIN RESULTS: A total of 113 BRASS subjects with clinically indicated chest computed tomography scans (41% with a spectrum of clinically evident and subclinical RA-ILD) and 76 ACR subjects with research or clinical scans (51% with a spectrum of RA-ILD) were selected. A combination of age, sex, smoking, rheumatoid factor, and anticyclic citrullinated peptide antibodies was strongly associated with RA-ILD (areas under the curve, 0.88 for BRASS and 0.89 for ACR). Importantly, a combinatorial signature including matrix metalloproteinase 7, pulmonary and activation-regulated chemokine, and surfactant protein D significantly increased the areas under the curve to 0.97 (P = 0.002, BRASS) and 1.00 (P = 0.016, ACR). Similar trends were seen for both clinically evident and subclinical RA-ILD. CONCLUSIONS: Clinical risk factors and autoantibodies are strongly associated with the presence of clinically evident and subclinical RA-ILD on computed tomography scan in two independent RA cohorts. A biomarker signature composed of matrix metalloproteinase 7, pulmonary and activation-regulated chemokine, and surfactant protein D significantly strengthens this association. These findings may facilitate identification of RA-ILD at an earlier stage, potentially leading to decreased morbidity and mortality.

BACKGROUND: Exercise impairment is a central feature of chronic obstructive pulmonary disease (COPD), and a minimal clinically important difference (MCID) for 6-min walk distance (6MWD) decline (>30 m) has been associated with increased mortality. The predictors of the MCID are not fully known. We hypothesize that physiological factors and radiographic measures predict the MCID. METHODS: We assessed 121 COPD subjects during 2 years using clinical variables, computed tomographic (CT) measures of emphysema, and functional measures including diffusion lung capacity for carbon monoxide (DLCO). The association between an MCID for 6MWD and clinical, CT, and physiologic predictors was assessed using logistic analysis. The C-statistic was used to assess the predictive ability of the models. RESULTS: Forty seven (39%) subjects had an MCID. In an imaging-based model, log emphysema and age were the best predictors of MCID (emphysema Odds Ratio [OR] 2.47 95%CI [1.28-4.76]). In a physiologic model, DLCO, age, and male gender were selected the best predictors (DLCO OR 1.19 [1.08-1.31]). The C-statistic for the ability of these models to predict an MCID was 0.71 and 0.75, respectively. CONCLUSION: In COPD patients the burden of emphysema on CT scan and DLCO predict a clinically meaningful decline in exercise capacity.

This paper proposes an inference method well-suited to large sets of medical images. The method is based upon a framework where distinctive 3D scale-invariant features are indexed efficiently to identify approximate nearest-neighbor (NN) feature matches-in O (log N) computational complexity in the number of images N. It thus scales well to large data sets, in contrast to methods based on pair-wise image registration or feature matching requiring O(N) complexity. Our theoretical contribution is a density estimator based on a generative model that generalizes kernel density estimation and K-nearest neighbor (KNN) methods.. The estimator can be used for on-the-fly queries, without requiring explicit parametric models or an off-line training phase. The method is validated on a large multi-site data set of 95,000,000 features extracted from 19,000 lung CT scans. Subject-level classification identifies all images of the same subjects across the entire data set despite deformation due to breathing state, including unintentional duplicate scans. State-of-the-art performance is achieved in predicting chronic pulmonary obstructive disorder (COPD) severity across the 5-category GOLD clinical rating, with an accuracy of 89% if both exact and one-off predictions are considered correct.

We present a generative probabilistic approach to discovery of disease subtypes determined by the genetic variants. In many diseases, multiple types of pathology may present simultaneously in a patient, making quantification of the disease challenging. Our method seeks common co-occurring image and genetic patterns in a population as a way to model these two different data types jointly. We assume that each patient is a mixture of multiple disease subtypes and use the joint generative model of image and genetic markers to identify disease subtypes guided by known genetic influences. Our model is based on a variant of the so-called topic models that uncover the latent structure in a collection of data. We derive an efficient variational inference algorithm to extract patterns of co-occurrence and to quantify the presence of heterogeneous disease processes in each patient. We evaluate the method on simulated data and illustrate its use in the context of Chronic Obstructive Pulmonary Disease (COPD) to characterize the relationship between image and genetic signatures of COPD subtypes in a large patient cohort.

RATIONALE: Chronic obstructive pulmonary disease (COPD) is defined by the presence of airflow limitation on spirometry, yet subjects with COPD can have marked differences in computed tomography imaging. These differences may be driven by genetic factors. We hypothesized that a genome-wide association study (GWAS) of quantitative imaging would identify loci not previously identified in analyses of COPD or spirometry. In addition, we sought to determine whether previously described genome-wide significant COPD and spirometric loci were associated with emphysema or airway phenotypes. OBJECTIVES: To identify genetic determinants of quantitative imaging phenotypes. METHODS: We performed a GWAS on two quantitative emphysema and two quantitative airway imaging phenotypes in the COPDGene (non-Hispanic white and African American), ECLIPSE (Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints), NETT (National Emphysema Treatment Trial), and GenKOLS (Genetics of COPD, Norway) studies and on percentage gas trapping in COPDGene. We also examined specific loci reported as genome-wide significant for spirometric phenotypes related to airflow limitation or COPD. MEASUREMENTS AND MAIN RESULTS: The total sample size across all cohorts was 12,031, of whom 9,338 were from COPDGene. We identified five loci associated with emphysema-related phenotypes, one with airway-related phenotypes, and two with gas trapping. These loci included previously reported associations, including the HHIP, 15q25, and AGER loci, as well as novel associations near SERPINA10 and DLC1. All previously reported COPD and a significant number of spirometric GWAS loci were at least nominally (P < 0.05) associated with either emphysema or airway phenotypes. CONCLUSIONS: Genome-wide analysis may identify novel risk factors for quantitative imaging characteristics in COPD and also identify imaging features associated with previously identified lung function loci.

RATIONALE: Few studies have examined associations between long-term exposure to fine particulate matter (PM2.5) and lung function decline in adults. OBJECTIVES: To determine if exposure to traffic and PM2.5 is associated with longitudinal changes in lung function in a population-based cohort in the Northeastern United States, where pollution levels are relatively low. METHODS: FEV1 and FVC were measured up to two times between 1995 and 2011 among 6,339 participants of the Framingham Offspring or Third Generation studies. We tested associations between residential proximity to a major roadway and PM2.5 exposure in 2001 (estimated by a land-use model using satellite measurements of aerosol optical thickness) and lung function. We examined differences in average lung function using mixed-effects models and differences in lung function decline using linear regression models. Current smokers were excluded. Models were adjusted for age, sex, height, weight, pack-years, socioeconomic status indicators, cohort, time, season, and weather. MEASUREMENTS AND MAIN RESULTS: Living less than 100 m from a major roadway was associated with a 23.2 ml (95% confidence interval [CI], -44.4 to -1.9) lower FEV1 and a 5.0 ml/yr (95% CI, -9.0 to -0.9) faster decline in FEV1 compared with more than 400 m. Each 2 μg/m(3) increase in average of PM2.5 was associated with a 13.5 ml (95% CI, -26.6 to -0.3) lower FEV1 and a 2.1 ml/yr (95% CI, -4.1 to -0.2) faster decline in FEV1. There were similar associations with FVC. Associations with FEV1/FVC ratio were weak or absent. CONCLUSIONS: Long-term exposure to traffic and PM2.5, at relatively low levels, was associated with lower FEV1 and FVC and an accelerated rate of lung function decline.

The Prevent Cancer Foundation Lung Cancer Workshop XI: Tobacco-Induced Disease: Advances in Policy, Early Detection and Management was held in New York, NY on May 16 and 17, 2014. The two goals of the Workshop were to define strategies to drive innovation in precompetitive quantitative research on the use of imaging to assess new therapies for management of early lung cancer and to discuss a process to implement a national program to provide high quality computed tomography imaging for lung cancer and other tobacco-induced disease. With the central importance of computed tomography imaging for both early detection and volumetric lung cancer assessment, strategic issues around the development of imaging and ensuring its quality are critical to ensure continued progress against this most lethal cancer.