COPD biomarkers and phenotypes: opportunities for better outcomes with precision imaging

Free-Breathing Functional Pulmonary Proton MRI: A Novel Approach Using Voxel-Wise Lung Ventilation (VOLVE) Assessment in Healthy Volunteers and Patients With Chronic Obstructive Pulmonary Disease

BACKGROUND

In respiratory medicine, there is a need for sensitive measures of regional lung function that can be performed using standard imaging technology, without the need for inhaled or intravenous contrast agents.

PURPOSE

To describe VOxel-wise Lung VEntilation (VOLVE), a new method for quantifying regional lung ventilation (V) and perfusion (Q) using free-breathing proton MRI, and to evaluate VOLVE in healthy never-smokers, healthy people with smoking history, and people with chronic obstructive pulmonary disease (COPD).

STUDY TYPE

Prospective pilot.

POPULATION

Twelve healthy never-smoker participants (age 30.3 ± 12.5 years, five male), four healthy participants with smoking history (>10 pack-years) (age 42.5 ± 18.3 years, one male), and 12 participants with COPD (age 62.8 ± 11.1 years, seven male).

FIELD STRENGTH/SEQUENCE

Single-slice free-breathing two-dimensional fast field echo sequence at 3 T.

ASSESSMENT

A novel postprocessing was developed to evaluate the MR signal changes in the lung parenchyma using a linear regression-based approach, which makes use of all the data in the time series for maximum sensitivity. V/Q-weighted maps were produced by computing the cross-correlation, lag and gradient between the respiratory/cardiac phase time course and lung parenchyma signal time courses. A comparison of histogram median and skewness values and spirometry was performed.

STATISTICAL TESTS

Kruskal-Wallis tests with Dunn's multiple comparison tests to compare VOLVE metrics between groups; Spearman correlation to assess the correlation between MRI and spirometry-derived parameters; and Bland-Altman analysis and coefficient of variation to evaluate repeatability were used. A P-value <0.05 was considered significant.

RESULTS

Significant differences between the groups were found for ventilation between healthy never-smoker and COPD groups (median XCCV, LagV, and GradV) and perfusion (median XCCQ, LagQ, and GradQ). Minimal bias and no significant differences between intravisit scans were found (P range = 0.12-0.97).

DATA CONCLUSION

This preliminary study showed that VOLVE has potential to provide metrics of function quantification.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 1.

Spatial Resolution Fidelity Comparison Between Energy Integrating and Deep Silicon Photon Counting CT: Implications for Pulmonary Imaging

PURPOSE

We investigated spatial resolution loss away from isocenter for a prototype deep silicon photon-counting detector (PCD) CT scanner and compare with a clinical energy-integrating detector (EID) CT scanner.

MATERIALS AND METHODS

We performed three scans on a wire phantom at four positions (isocenter, 6.7, 11.8, and 17.1 cm off isocenter). The acquisition modes were 120 kV EID CT, 120 kV high-definition (HD) EID CT, and 120 kV PCD CT. HD mode used double the projection view angles per rotation as the "regular" EID scan mode. The diameter of the wire was calculated by taking the full width of half max (FWHM) of a profile drawn over the radial and azimuthal directions of the wire. Change in wire diameter appearance was assessed by calculating the ratio of the radial and azimuthal diameter relative to isocenter. t tests were used to make pairwise comparisons of the wire diameter ratio with each acquisition and mean ratios' difference from unity.

RESULTS

Deep silicon PCD CT had statistically smaller (P<0.05) changes in diameter ratio for both radial and azimuthal directions compared with both regular and HD EID modes and was not statistically different from unity (P<0.05). Maximum increases in FWMH relative to isocenter were 36%, 12%, and 1% for regular EID, HD EID, and deep silicon PCD, respectively.

CONCLUSION

Deep silicon PCD CT exhibits less change in spatial resolution in both the radial and azimuthal directions compared with EID CT.

Chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) is a major cause of morbidity, mortality, and health-care use worldwide. COPD is caused by exposure to inhaled noxious particles, notably tobacco smoke and pollutants. However, the broad range of factors that increase the risk of development and progression of COPD throughout the life course are increasingly being recognised. Innovations in omics and imaging techniques have provided greater insight into disease pathobiology, which might result in advances in COPD prevention, diagnosis, and treatment. Although few novel treatments have been approved for COPD in the past 5 years, advances have been made in targeting existing therapies to specific subpopulations using new biomarker-based strategies. Additionally, COVID-19 has undeniably affected individuals with COPD, who are not only at higher risk for severe disease manifestations than healthy individuals but also negatively affected by interruptions in health-care delivery and social isolation. This Seminar reviews COPD with an emphasis on recent advances in epidemiology, pathophysiology, imaging, diagnosis, and treatment.

Artificial intelligence in functional imaging of the lung

Artificial intelligence (AI) is transforming the way we perform advanced imaging. From high-resolution image reconstruction to predicting functional response from clinically acquired data, AI is promising to revolutionize clinical evaluation of lung performance, pushing the boundary in pulmonary functional imaging for patients suffering from respiratory conditions. In this review, we overview the current developments and expound on some of the encouraging new frontiers. We focus on the recent advances in machine learning and deep learning that enable reconstructing images, quantitating, and predicting functional responses of the lung. Finally, we shed light on the potential opportunities and challenges ahead in adopting AI for functional lung imaging in clinical settings.

Precision medicine in chronic obstructive pulmonary disease: how far have we come?

PURPOSE OF REVIEW

In this review, we will discuss the current status and recent developments in precision medicine in chronic obstructive pulmonary disease (COPD) through the lens of treatable traits.

RECENT FINDINGS

Although the term 'treatable traits' in the treatment of COPD is relatively recent, this concept has been used for many years if one considers interventions such as long-term oxygen therapy or alpha-1 antitrypsin replacement therapy. Recent advances have included expanding the definition of COPD to include a broader population of people with lower respiratory disease but not meeting the strict criteria for obstruction, advances in imaging to aid in the diagnosis and treatment of COPD, advances in understanding symptoms and exacerbations to define severity, using biomarkers to guide therapy and better understanding and addressing polymorbidity and frailty. In addition, there is a concerted effort to use these concepts to identify COPD patients earlier in the disease process wherein disease modification may be possible.

SUMMARY

Focusing on subsets of patients with COPD with certain characteristics should lead to better outcomes and fewer adverse effects from treatment.

VIDEO ABSTRACT

http://links.lww.com/COPM/A30.

Detection and staging of chronic obstructive pulmonary disease using a computed tomography-based weakly supervised deep learning approach

OBJECTIVES

Chronic obstructive pulmonary disease (COPD) is underdiagnosed globally. The present study aimed to develop weakly supervised deep learning (DL) models that utilize computed tomography (CT) image data for the automated detection and staging of spirometry-defined COPD.

METHODS

A large, highly heterogeneous dataset was established, consisting of 1393 participants retrospectively recruited from outpatient, inpatient, and physical examination center settings of four large public hospitals in China. All participants underwent both inspiratory chest CT scans and pulmonary function tests. CT images, spirometry data, demographic information, and clinical information of each participant were collected. An attention-based multi-instance learning (MIL) model for COPD detection was trained using CT scans from 837 participants. External validation of the COPD detection was performed with 620 low-dose CT (LDCT) scans acquired from the National Lung Screening Trial (NLST) cohort. A multi-channel 3D residual network was further developed to categorize GOLD stages among confirmed COPD patients.

RESULTS

The attention-based MIL model used for COPD detection achieved an area under the receiver operating characteristic curve (AUC) of 0.934 (95% CI: 0.903, 0.961) on the internal test set and 0.866 (95% CI: 0.805, 0.928) on the LDCT subset acquired from the NLST. The multi-channel 3D residual network was able to correctly grade 76.4% of COPD patients in the test set (423/553) using the GOLD scale.

CONCLUSIONS

The proposed chest CT-DL approach can automatically identify spirometry-defined COPD and categorize patients according to the GOLD scale. As such, this approach may be an effective case-finding tool for COPD diagnosis and staging.

KEY POINTS

• Chronic obstructive pulmonary disease is underdiagnosed globally, particularly in developing countries. • The proposed chest computed tomography (CT)-based deep learning (DL) approaches could accurately identify spirometry-defined COPD and categorize patients according to the GOLD scale. • The chest CT-DL approach may be an alternative case-finding tool for COPD identification and evaluation.

Bronchial wall parameters on CT in healthy never-smoking, smoking, COPD, and asthma populations: a systematic review and meta-analysis

Objective

Research on computed tomography (CT) bronchial parameter measurements shows that there are conflicting results on the values for bronchial parameters in the never-smoking, smoking, asthma, and chronic obstructive pulmonary disease (COPD) populations. This review assesses the current CT methods for obtaining bronchial wall parameters and their comparison between populations.

Methods

A systematic review of MEDLINE and Embase was conducted following PRISMA guidelines (last search date 25th October 2021). Methodology data was collected and summarised. Values of percentage wall area (WA%), wall thickness (WT), summary airway measure (Pi10), and luminal area (Ai) were pooled and compared between populations.

Results

A total of 169 articles were included for methodologic review; 66 of these were included for meta-analysis. Most measurements were obtained from multiplanar reconstructions of segmented airways (93 of 169 articles), using various tools and algorithms; third generation airways in the upper and lower lobes were most frequently studied. COPD (12,746) and smoking (15,092) populations were largest across studies and mostly consisted of men (median 64.4%, IQR 61.5 – 66.1%). There were significant differences between populations; the largest WA% was found in COPD (mean SD 62.93 ± 7.41%, n = 6,045), and the asthma population had the largest Pi10 (4.03 ± 0.27 mm, n = 442). Ai normalised to body surface area (Ai/BSA) (12.46 ± 4 mm², n = 134) was largest in the never-smoking population.

Conclusions

Studies on CT-derived bronchial parameter measurements are heterogenous in methodology and population, resulting in challenges to compare outcomes between studies. Significant differences between populations exist for several parameters, most notably in the wall area percentage; however, there is a large overlap in their ranges.

Key Points

• Diverse methodology in measuring airways contributes to overlap in ranges of bronchial parameters among the never-smoking, smoking, COPD, and asthma populations.

• The combined number of never-smoking participants in studies is low, limiting insight into this population and the impact of participant characteristics on bronchial parameters.

• Wall area percent of the right upper lobe apical segment is the most studied (87 articles) and differentiates all except smoking vs asthma populations.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00330-022-08600-1.

Pulmonary functional MRI: Detecting the structure-function pathologies that drive asthma symptoms and quality of life

Pulmonary functional MRI (PfMRI) using inhaled hyperpolarized, radiation-free gases (such as ³ He and ¹²⁹ Xe) provides a way to directly visualize inhaled gas distribution and ventilation defects (or ventilation heterogeneity) in real time with high spatial (~mm³ ) resolution. Both gases enable quantitative measurement of terminal airway morphology, while ¹²⁹ Xe uniquely enables imaging the transfer of inhaled gas across the alveolar-capillary tissue barrier to the red blood cells. In patients with asthma, PfMRI abnormalities have been shown to reflect airway smooth muscle dysfunction, airway inflammation and remodelling, luminal occlusions and airway pruning. The method is rapid (8-15 s), cost-effective (~$300/scan) and very well tolerated in patients, even in those who are very young or very ill, because unlike computed tomography (CT), positron emission tomography and single-photon emission CT, there is no ionizing radiation and the examination takes only a few seconds. However, PfMRI is not without limitations, which include the requirement of complex image analysis, specialized equipment and additional training and quality control. We provide an overview of the three main applications of hyperpolarized noble gas MRI in asthma research including: (1) inhaled gas distribution or ventilation imaging, (2) alveolar microstructure and finally (3) gas transfer into the alveolar-capillary tissue space and from the tissue barrier into red blood cells in the pulmonary microvasculature. We highlight the evidence that supports a deeper understanding of the mechanisms of asthma worsening over time and the pathologies responsible for symptoms and disease control. We conclude with a summary of approaches that have the potential for integration into clinical workflows and that may be used to guide personalized treatment planning.

Bullous Parametric Response Map for Functional Localization of COPD

Advanced bronchoscopic lung volume reduction treatment (BLVR) is now a routine care option for treating patients with severe emphysema. Patterns of low attenuation clusters indicating emphysema and functional small airway disease (fSAD) on paired CT, which may provide additional insights to the target selection of the segmental or subsegmental lobe of the treatments, require further investigation. The low attenuation clusters (LACS) were segmented to identify the scalar and spatial distribution of the lung destructions, in terms of 10 fractions scales of low attenuation density (LAD) located in upper lobes and lower lobes. The LACs of functional small airway disease (fSAD) were delineated by applying the technique of parametric response map (PRM) on the co-registered CT image data. Both emphysematous LACs of inspiratory CT and fSAD LACs on expiratory CT were used to derive the coefficients of the predictive model for estimating the airflow limitation. The voxel-wise severity is then predicted using the regional LACs on the co-registered CT to indicate the functional localization, namely, the bullous parametric response map (BPRM). A total of 100 subjects, 88 patients with mild to very severe COPD and 12 control participants with normal lung functions (FEV₁/FVC % > 70%), were evaluated. Pearson’s correlations between FEV₁/FVC% and LAV%_HU-950 of severe emphysema are − 0.55 comparing to − 0.67 and − 0.62 of LAV%_HU-856 of air-trapping and LAV%_fSAD respectively. Pearson’s correlation between FEV₁/FVC% and FEV₁/FVC% predicted by the proposed model using LAD% of HU-950 and fSAD on BPRM is 0.82 (p < 0.01). The result of the Bullous Parametric Response Map (BPRM) is capable of identifying the less functional area of the lung, where the BLVR treatment is aimed at removing from a hyperinflated area of emphysematous regions.

Computed tomography-identified phenotypes of small airway obstructions in chronic obstructive pulmonary disease

ABSTRACT

Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease characteristic of small airway inflammation, obstruction, and emphysema. It is well known that spirometry alone cannot differentiate each separate component. Computed tomography (CT) is widely used to determine the extent of emphysema and small airway involvement in COPD. Compared with the pulmonary function test, small airway CT phenotypes can accurately reflect disease severity in patients with COPD, which is conducive to improving the prognosis of this disease. CT measurement of central airway morphology has been applied in clinical, epidemiologic, and genetic investigations as an inference of the presence and severity of small airway disease. This review will focus on presenting the current knowledge and methodologies in chest CT that aid in identifying discrete COPD phenotypes.

Quantitative CT metrics are associated with longitudinal lung function decline and future asthma exacerbations: Results from SARP-3

BACKGROUND

Currently, there is limited knowledge regarding which imaging assessments of asthma are associated with accelerated longitudinal decline in lung function.

OBJECTIVES

We aimed to assess whether quantitative computed tomography (qCT) metrics are associated with longitudinal decline in lung function and morbidity in asthma.

METHODS

We analyzed 205 qCT scans of adult patients with asthma and calculated baseline markers of airway remodeling, lung density, and pointwise regional change in lung volume (Jacobian measures) for each participant. Using multivariable regression models, we then assessed the association of qCT measurements with the outcomes of future change in lung function, future exacerbation rate, and changes in validated measurements of morbidity.

RESULTS

Greater baseline wall area percent (β = -0.15 [95% CI = -0.26 to -0.05]; P < .01), hyperinflation percent (β = -0.25 [95% CI = -0.41 to -0.09]; P < .01), and Jacobian gradient measurements (cranial-caudal β = 10.64 [95% CI = 3.79-17.49]; P < .01; posterior-anterior β = -9.14, [95% CI = -15.49 to -2.78]; P < .01) were associated with more severe future lung function decline. Additionally, greater wall area percent (rate ratio = 1.06 [95% CI = 1.01-1.10]; P = .02) and air trapping percent (rate ratio =1.01 [95% CI = 1.00-1.02]; P = .03), as well as lower decline in the Jacobian determinant mean (rate ratio = 0.58 [95% CI = 0.41-0.82]; P < .01) and Jacobian determinant standard deviation (rate ratio = 0.52 [95% CI = 0.32-0.85]; P = .01), were associated with a greater rate of future exacerbations. However, imaging metrics were not associated with clinically meaningful changes in scores on validated asthma morbidity questionnaires.

CONCLUSIONS

Baseline qCT measures of more severe airway remodeling, more small airway disease and hyperinflation, and less pointwise regional change in lung volumes were associated with future lung function decline and asthma exacerbations.

Defect distribution index: A novel metric for functional lung MRI in cystic fibrosis

Purpose

Lung impairment from functional MRI is frequently assessed as defect percentage. The defect distribution, however, is currently not quantified. The purpose of this work was to develop a novel measure that quantifies how clustered or scattered defects in functional lung MRI appear, and to evaluate it in pediatric cystic fibrosis.

Theory

The defect distribution index (DDI) calculates a score for each lung voxel categorized as defected. The index increases according to how densely and how far an expanding circle around a defect voxel contains more than 50% defect voxels.

Methods

Fractional ventilation and perfusion maps of 53 children with cystic fibrosis were previously acquired with matrix pencil decomposition MRI. In this work, the DDI is compared to a visual score of 3 raters who evaluated how clustered the lung defects appear. Further, spearman correlations between DDI and lung function parameters were determined.

Results

The DDI strongly correlates with the visual scoring (r = 0.90 for ventilation; r = 0.88 for perfusion; P < .0001). Although correlations between DDI and defect percentage are moderate to strong (r = 0.61 for ventilation; r = 0.75 for perfusion; P < .0001), the DDI distinguishes between patients with comparable defect percentage.

Conclusion

The DDI is a novel measure for functional lung MRI. It provides complementary information to the defect percentage because the DDI assesses defect distribution rather than defect size. The DDI is applicable to matrix pencil MRI data of cystic fibrosis patients and shows very good agreement with human perception of defect distributions.

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Pulmonary Functional Imaging: Part 2-State-of-the-Art Clinical Applications and Opportunities for Improved Patient Care

Pulmonary functional imaging may be defined as the regional quantification of lung function by using primarily CT, MRI, and nuclear medicine techniques. The distribution of pulmonary physiologic parameters, including ventilation, perfusion, gas exchange, and biomechanics, can be noninvasively mapped and measured throughout the lungs. This information is not accessible by using conventional pulmonary function tests, which measure total lung function without viewing the regional distribution. The latter is important because of the heterogeneous distribution of virtually all lung disorders. Moreover, techniques such as hyperpolarized xenon 129 and helium 3 MRI can probe lung physiologic structure and microstructure at the level of the alveolar-air and alveolar-red blood cell interface, which is well beyond the spatial resolution of other clinical methods. The opportunities, challenges, and current stage of clinical deployment of pulmonary functional imaging are reviewed, including applications to chronic obstructive pulmonary disease, asthma, interstitial lung disease, pulmonary embolism, and pulmonary hypertension. Among the challenges to the deployment of pulmonary functional imaging in routine clinical practice are the need for further validation, establishment of normal values, standardization of imaging acquisition and analysis, and evidence of patient outcomes benefit. When these challenges are addressed, it is anticipated that pulmonary functional imaging will have an expanding role in the evaluation and management of patients with lung disease.

Emphysema Index Based on Hyperpolarized 3He or 129Xe Diffusion MRI: Performance and Comparison with Quantitative CT and Pulmonary Function Tests

Background Apparent diffusion coefficient (ADC) maps of inhaled hyperpolarized gases have shown promise in the characterization of emphysema in patients with chronic obstructive pulmonary disease (COPD), yet an easily interpreted quantitative metric beyond mean and standard deviation has not been established. Purpose To introduce a quantitative framework with which to characterize emphysema burden based on hyperpolarized helium 3 (³He) and xenon 129 (¹²⁹Xe) ADC maps and compare its diagnostic performance with CT-based emphysema metrics and pulmonary function tests (PFTs). Materials and Methods Twenty-seven patients with mild, moderate, or severe COPD and 13 age-matched healthy control subjects participated in this retrospective study. Participants underwent CT and multiple b value diffusion-weighted ³He and ¹²⁹Xe MRI examinations and standard PFTs between August 2014 and November 2017. ADC-based emphysema index was computed separately for each gas and b value as the fraction of lung voxels with ADC values greater than in the healthy group 99th percentile. The resulting values were compared with quantitative CT results (relative lung area <-950 HU) as the reference standard. Diagnostic performance metrics included area under the receiver operating characteristic curve (AUC). Spearman rank correlations and Wilcoxon rank sum tests were performed between ADC-, CT-, and PFT-based metrics, and intraclass correlation was performed between repeated measurements. Results Thirty-six participants were evaluated (mean age, 60 years ± 6 [standard deviation]; 20 women). ADC-based emphysema index was highly repeatable (intraclass correlation coefficient > 0.99) and strongly correlated with quantitative CT (r = 0.86, P < .001 for ³He; r = 0.85, P < .001 for ¹²⁹Xe) with high AUC (≥0.93; 95% confidence interval [CI]: 0.85, 1.00). ADC emphysema indices were also correlated with percentage of predicted diffusing capacity of lung for carbon monoxide (r = -0.81, P < .001 for ³He; r = -0.80, P < .001 for ¹²⁹Xe) and percentage of predicted residual lung volume divided by total lung capacity (r = 0.65, P < .001 for ³He; r = 0.61, P < .001 for ¹²⁹Xe). Conclusion Emphysema index based on hyperpolarized helium 3 or xenon 129 diffusion MRI provides a repeatable measure of emphysema burden, independent of gas or b value, with similar diagnostic performance as quantitative CT or pulmonary function metrics. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Schiebler and Fain in this issue.

Airway morphometry in COPD with bronchiectasis: a view on all airway generations

The pathophysiological processes underlying bronchiectasis in chronic obstructive pulmonary disease (COPD) are not understood. In COPD, both small and large airways are progressively lost. It is currently not known to what extent the different airway generations of patients with COPD and bronchiectasis are involved.

COPD explant lungs with bronchiectasis were compared to COPD explant lungs without bronchiectasis and unused donor lungs as controls. In order to investigate all airway generations, a multimodal imaging approach using different resolutions was conducted. Per group, five lungs were frozen (n=15) and underwent computed tomography (CT) imaging for large airway evaluation, with four tissue cores per lung imaged for measurements of the terminal bronchioles. Two additional lungs per group (n=6) were air-dried for lobar microCT images that allow airway segmentation and three-dimensional quantification of the complete airway tree.

COPD lungs with bronchiectasis had significantly more airways compared to COPD lungs without bronchiectasis (p<0.001), with large airway numbers similar to control lungs. This difference was present in both upper and lower lobes. Lack of tapering was present (p=0.010) and larger diameters were demonstrated in lower lobes with bronchiectasis (p=0.010). MicroCT analysis of tissue cores showed similar reductions of tissue percentage, surface density and number of terminal bronchioles in both COPD groups compared to control lungs.

Although terminal bronchioles were equally reduced in COPD lungs with and without bronchiectasis, significantly more large and small airways were found in COPD lungs with bronchiectasis.

Airway inflammation in COPD: progress to precision medicine

Chronic obstructive pulmonary disease (COPD) is a significant cause of morbidity and mortality worldwide, and its prevalence is increasing. Airway inflammation is a consistent feature of COPD and is implicated in the pathogenesis and progression of COPD, but anti-inflammatory therapy is not first-line treatment. The inflammation has many guises and phenotyping this heterogeneity has revealed different patterns. Neutrophil-associated COPD with activation of the inflammasome, T1 and T17 immunity is the most common phenotype with eosinophil-associated T2-mediated immunity in a minority and autoimmunity observed in more severe disease. Biomarkers have enabled targeted anti-inflammatory strategies and revealed that corticosteroids are most effective in those with evidence of eosinophilic inflammation, whereas, in contrast to severe asthma, response to anti-interleukin-5 biologicals in COPD has been disappointing, with smaller benefits for the same intensity of eosinophilic inflammation questioning its role in COPD. Biological therapies beyond T2-mediated inflammation have not demonstrated benefit and in some cases increased risk of infection, suggesting that neutrophilic inflammation and inflammasome activation might be largely driven by bacterial colonisation and dysbiosis. Herein we describe current and future biomarker approaches to assess inflammation in COPD and how this might reveal tractable approaches to precision medicine and unmask important host-environment interactions leading to airway inflammation.

Deep semantic lung segmentation for tracking potential pulmonary perfusion biomarkers in chronic obstructive pulmonary disease (COPD): The multi-ethnic study of atherosclerosis COPD study

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is associated with high morbidity and mortality. Identification of imaging biomarkers for phenotyping is necessary for future treatment and therapy monitoring. However, translation of visual analytic pipelines into clinics or their use in large-scale studies is significantly slowed by time-consuming postprocessing steps.

PURPOSE

To implement an automated tool chain for regional quantification of pulmonary microvascular blood flow in order to reduce analysis time and user variability.

STUDY TYPE

Prospective.

POPULATION

In all, 90 MRI scans of 63 patients, of which 31 had a COPD with a mean Global Initiative for Chronic Obstructive Lung Disease status of 1.9 ± 0.64 (μ ± σ).

FIELD STRENGTH/SEQUENCE

1.5T dynamic gadolinium-enhanced MRI measurement using 4D dynamic contrast material-enhanced (DCE) time-resolved angiography acquired in a single breath-hold in inspiration. [Correction added on August 20, 2019, after first online publication: The field strength in the preceding sentence was corrected.] ASSESSMENT: We built a 3D convolutional neural network for semantic segmentation using 29 manually segmented perfusion maps. All five lobes of the lung are denoted, including the middle lobe. Evaluation was performed on 61 independent cases from two sites of the Multi-Ethnic Study of Arteriosclerosis (MESA)-COPD study. We publish our implementation of a model-free deconvolution filter according to Sourbron et al for 4D DCE MRI scans as open source.

STATISTICAL TEST

Cross-validation 29/61 (# training / # testing), intraclass correlation coefficient (ICC), Spearman ρ, Pearson r, Sørensen-Dice coefficient, and overlap.

RESULTS

Segmentations and derived clinical parameters were processed in ~90 seconds per case on a Xeon E5-2637v4 workstation with Tesla P40 GPUs. Clinical parameters and predicted segmentations exhibit high concordance with the ground truth regarding median perfusion for all lobes with an ICC of 0.99 and a Sørensen-Dice coefficient of 93.4 ± 2.8 (μ ± σ).

DATA CONCLUSION

We present a robust end-to-end pipeline that allows for the extraction of perfusion-based biomarkers for all lung lobes in 4D DCE MRI scans by combining model-free deconvolution with deep learning.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:571-579.

Introduction to precision medicine in COPD

Although there has been tremendous growth in our understanding of chronic obstructive pulmonary disease (COPD) and its pathophysiology over the past few decades, the pace of therapeutic innovation has been extremely slow. COPD is now widely accepted as a heterogeneous condition with multiple phenotypes and endotypes. Thus, there is a pressing need for COPD care to move from the current “one-size-fits-all” approach to a precision medicine approach that takes into account individual patient variability in genes, environment and lifestyle. Precision medicine is enabled by biomarkers that can: 1) accurately identify subgroups of patients who are most likely to benefit from therapeutics and those who will only experience harm (predictive biomarkers); 2) predict therapeutic responses to drugs at an individual level (response biomarkers); and 3) segregate patients who are at risk of poor outcomes from those who have relatively stable disease (prognostic biomarkers). In this essay, we will discuss the current concept of precision medicine and its relevance for COPD and explore ways to implement precision medicine for millions of patients across the world with COPD.