Proteomics and Machine Learning in the Prediction and Explanation of Low Pectoralis Muscle Area.

Low muscle mass is associated with numerous adverse outcomes independent of other associated comorbid diseases. We aimed to predict and understand an individual's risk for developing low muscle mass using proteomics and machine learning. We identified 8 biomarkers associated with low pectoralis muscle area (PMA). We built 3 random forest classification models that used either clinical measures, feature selected biomarkers, or both to predict development of low PMA. The area under the receiver operating characteristic curve for each model was: clinical-only = 0.646, biomarker-only = 0.740, and combined = 0.744. We displayed the heterogenetic nature of an individual's risk for developing low PMA and identified 2 distinct subtypes of participants who developed low PMA. While additional validation is required, our methods for identifying and understanding individual and group risk for low muscle mass could be used to enable developments in the personalized prevention of low muscle mass.

Erratum: Automated CT Staging of Chronic Obstructive Pulmonary Disease Severity for Predicting Disease Progression and Mortality with a Deep Learning Convolutional Neural Network.

[This corrects the article DOI: 10.1148/ryct.2021200477.].

Unsupervised Learning Identifies Computed Tomographic Measurements as Primary Drivers of Progression, Exacerbation, and Mortality in Chronic Obstructive Pulmonary Disease.

Rationale: Chronic obstructive pulmonary disease (COPD) is a heterogeneous syndrome with phenotypic manifestations that tend to be distributed along a continuum. Unsupervised machine learning based on broad selection of imaging and clinical phenotypes may be used to identify primary variables that define disease axes and stratify patients with COPD. Objectives: To identify primary variables driving COPD heterogeneity using principal component analysis and to define disease axes and assess the prognostic value of these axes across three outcomes: progression, exacerbation, and mortality. Methods: We included 7,331 patients between 39 and 85 years old, of whom 40.3% were Black and 45.8% were female smokers with a mean of 44.6 pack-years, from the COPDGene (Genetic Epidemiology of COPD) phase I cohort (2008-2011) in our analysis. Out of a total of 916 phenotypes, 147 continuous clinical, spirometric, and computed tomography (CT) features were selected. For each principal component (PC), we computed a PC score based on feature weights. We used PC score distributions to define disease axes along which we divided the patients into quartiles. To assess the prognostic value of these axes, we applied logistic regression analyses to estimate 5-year (n = 4,159) and 10-year (n = 1,487) odds of progression. Cox regression and Kaplan-Meier analyses were performed to estimate 5-year and 10-year risk of exacerbation (n = 6,532) and all-cause mortality (n = 7,331). Results: The first PC, accounting for 43.7% of variance, was defined by CT measures of air trapping and emphysema. The second PC, accounting for 13.7% of variance, was defined by spirometric and CT measures of vital capacity and lung volume. The third PC, accounting for 7.9% of the variance, was defined by CT measures of lung mass, airway thickening, and body habitus. Stratification of patients across each disease axis revealed up to 3.2-fold (95% confidence interval [CI] 2.4, 4.3) greater odds of 5-year progression, 5.4-fold (95% CI 4.6, 6.3) greater risk of 5-year exacerbation, and 5.0-fold (95% CI 4.2, 6.0) greater risk of 10-year mortality between the highest and lowest quartiles. Conclusions: Unsupervised learning analysis of the COPDGene cohort reveals that CT measurements may bolster patient stratification along the continuum of COPD phenotypes. Each of the disease axes also individually demonstrate prognostic potential, predictive of future forced expiratory volume in 1 second decline, exacerbation, and mortality.

CNN-based Deformable Registration Facilitates Fast and Accurate Air Trapping Measurements at Inspiratory and Expiratory CT.

PURPOSE: To develop a convolutional neural network (CNN)-based deformable lung registration algorithm to reduce computation time and assess its potential for lobar air trapping quantification. MATERIALS AND METHODS: In this retrospective study, a CNN algorithm was developed to perform deformable registration of lung CT (LungReg) using data on 9118 patients from the COPDGene Study (data collected between 2007 and 2012). Loss function constraints included cross-correlation, displacement field regularization, lobar segmentation overlap, and the Jacobian determinant. LungReg was compared with a standard diffeomorphic registration (SyN) for lobar Dice overlap, percentage voxels with nonpositive Jacobian determinants, and inference runtime using paired t tests. Landmark colocalization error (LCE) across 10 patients was compared using a random effects model. Agreement between LungReg and SyN air trapping measurements was assessed using intraclass correlation coefficient. The ability of LungReg versus SyN emphysema and air trapping measurements to predict Global Initiative for Chronic Obstructive Lung Disease (GOLD) stages was compared using area under the receiver operating characteristic curves. RESULTS: Average performance of LungReg versus SyN showed lobar Dice overlap score of 0.91-0.97 versus 0.89-0.95, respectively (P < .001); percentage voxels with nonpositive Jacobian determinant of 0.04 versus 0.10, respectively (P < .001); inference run time of 0.99 second (graphics processing unit) and 2.27 seconds (central processing unit) versus 418.46 seconds (central processing unit) (P < .001); and LCE of 7.21 mm versus 6.93 mm (P < .001). LungReg and SyN whole-lung and lobar air trapping measurements achieved excellent agreement (intraclass correlation coefficients > 0.98). LungReg versus SyN area under the receiver operating characteristic curves for predicting GOLD stage were not statistically different (range, 0.88-0.95 vs 0.88-0.95, respectively; P = .31-.95). CONCLUSION: CNN-based deformable lung registration is accurate and fully automated, with runtime feasible for clinical lobar air trapping quantification, and has potential to improve diagnosis of small airway diseases.Keywords: Air Trapping, Convolutional Neural Network, Deformable Registration, Small Airway Disease, CT, Lung, Semisupervised Learning, Unsupervised Learning Supplemental material is available for this article. © RSNA, 2021 An earlier incorrect version of this article appeared online. This article was corrected on December 22, 2021.

Erratum: CNN-based Deformable Registration Facilitates Fast and Accurate Air Trapping Measurements at Inspiratory and Expiratory CT.

[This corrects the article DOI: 10.1148/ryai.2021210211.].

Inhibition of Complement Factor 3 in Geographic Atrophy with NGM621: Phase 1 Dose-Escalation Study Results.

PURPOSE: To evaluate the safety and tolerability of single and multiple intravitreal injections of NGM621 in patients with geographic atrophy (GA) and to characterize the pharmacokinetics and immunogenic potential. DESIGN: Multicenter, open-label, single- and multiple-dose phase 1 study. METHODS: Fifteen patients enrolled at 4 sites in the United States. Participants had GA secondary to age-related macular degeneration, lesion size ≥2.5 mm2, best-corrected visual acuity of 4 to 54 letters (20/80 to 20/800 Snellen equivalent) in the study eye, and no history of choroidal neovascularization in either eye. Patients who met eligibility criteria were treated in a single ascending-dose phase (2 mg, 7.5 mg, and 15 mg) or received 2 doses of NGM621 (15 mg) 4 weeks apart in the multidose phase and were monitored for 12 weeks (85 days). Assessments included adverse events, best-corrected visual acuity, low-luminance visual acuity, vital signs, clinical laboratory evaluations, GA lesion area as measured by fundus autofluorescence, spectral domain optical coherence tomography, and pharmacokinetic, immunogenicity, and pharmacodynamic assessments. RESULTS: All 15 participants completed the 12-week study. There were no serious adverse events, no drug-related adverse events, and no choroidal neovascularization developed in either eye. Mean visual acuity and GA lesion area appeared stable through week 12 for all cohorts. Pharmacokinetic analyses indicated that NGM621 serum exposures appeared to be dose proportional, and no antidrug antibodies were identified at any of the evaluated time points. CONCLUSIONS: In this small, open-labeled, 12-week phase 1 study, NGM621 was safe and tolerable when administered intravitreally up to 15 mg..

Automated CT Staging of Chronic Obstructive Pulmonary Disease Severity for Predicting Disease Progression and Mortality with a Deep Learning Convolutional Neural Network.

PURPOSE: To develop a deep learning-based algorithm to stage the severity of chronic obstructive pulmonary disease (COPD) through quantification of emphysema and air trapping on CT images and to assess the ability of the proposed stages to prognosticate 5-year progression and mortality. MATERIALS AND METHODS: In this retrospective study, an algorithm using co-registration and lung segmentation was developed in-house to automate quantification of emphysema and air trapping from inspiratory and expiratory CT images. The algorithm was then tested in a separate group of 8951 patients from the COPD Genetic Epidemiology study (date range, 2007-2017). With measurements of emphysema and air trapping, bivariable thresholds were determined to define CT stages of severity (mild, moderate, severe, and very severe) and were evaluated for their ability to prognosticate disease progression and mortality using logistic regression and Cox regression. RESULTS: On the basis of CT stages, the odds of disease progression were greatest among patients with very severe disease (odds ratio [OR], 2.67; 95% CI: 2.02, 3.53; P < .001) and were elevated in patients with moderate disease (OR, 1.50; 95% CI: 1.22, 1.84; P = .001). The hazard ratio of mortality for very severe disease at CT was 2.23 times the normal ratio (95% CI: 1.93, 2.58; P < .001). When combined with Global Initiative for Chronic Obstructive Lung Disease (GOLD) staging, patients with GOLD stage 2 disease had the greatest odds of disease progression when the CT stage was severe (OR, 4.48; 95% CI: 3.18, 6.31; P < .001) or very severe (OR, 4.72; 95% CI: 3.13, 7.13; P < .001). CONCLUSION: Automated CT algorithms can facilitate staging of COPD severity, have diagnostic performance comparable with that of spirometric GOLD staging, and provide further prognostic value when used in conjunction with GOLD staging.Supplemental material is available for this article.© RSNA, 2021See also commentary by Kalra and Ebrahimian in this issue.

Imaging the Enzyme 11ß-Hydroxysteroid Dehydrogenase Type 1 with PET: Evaluation of the Novel Radiotracer 11C-AS2471907 in Human Brain.

The 11ß-hydroxysteroid dehydrogenase type 1 (11ß-HSD1) enzyme converts cortisone to cortisol and participates in the regulation of glucocorticoid levels in tissues. 11ß-HSD1 is expressed in the liver, kidney, adipose tissue, placenta, and brain. 11ß-HSD1 is a target for treatment of depression, anxiety, posttraumatic stress disorder, and also against age-related cognitive function and memory loss. In this study, we evaluated the radiotracer 11C-AS2471907 (3-(2-chlorophenyl)-4-(methyl-11C)-5-[2-[2,4,6-trifluorophenoxy]propan-2-yl]-4H-1,2,4-triazole) to image 11ß-HSD1 availability in the human brain with PET. Methods: Fifteen subjects were included in the study. All subjects underwent one 2-h scan after a bolus administration of 11C-AS2471907. Two subjects underwent an additional scan after blockade with the selective and high-affinity 11ß-HSD1 inhibitor ASP3662 to evaluate 11C-AS2471907 nondisplaceable distribution volume. Five subjects also underwent an additional scan to evaluate the within-day test-retest variability of 11C-AS2471907 volumes of distribution (VT). Results:11C-AS2471907 time-activity curves were best fitted by the 2-tissue-compartment (2TC) model. 11C-AS2471907 exhibited a regionally varying pattern of uptake throughout the brain. The VT of 11C-AS2471907 ranged from 3.7 ± 1.5 mL/cm3 in the caudate nucleus to 14.5 ± 5.3 mL/cm3 in the occipital cortex, with intermediate values in the amygdala, white matter, cingulum, insula, frontal cortex, putamen, temporal and parietal cortices, cerebellum, and thalamus (from lowest to highest VT). From the blocking scans, nondisplaceable distribution volume was determined to be 0.16 ± 0.04 mL/cm3 for 11C-AS2471907. Thus, nearly all uptake was specific and the binding potential ranged from 22 in the caudate to 90 in the occipital cortex. Test-retest variability of 2TC VT values was less than 10% in most large cortical regions (14% in parietal cortex) and ranged from 14% (cerebellum) to 51% (amygdala) in other regions. The intraclass correlation coefficient of 2TC VT values ranged from 0.55 in the white matter to 0.98 in the cerebellum. Conclusion:11C-AS2471907 has a high fraction of specific binding in vivo in humans and reasonable within-day reproducibility of binding parameters.

Safety, Pharmacokinetics, and Pharmacodynamics of ASP3662, a Novel 11ß-Hydroxysteroid Dehydrogenase Type 1 Inhibitor, in Healthy Young and Elderly Subjects.

Inhibition of the enzyme 11ß-hydroxysteroid dehydrogenase type 1 (11ß-HSD1) represents a potential mechanism for improving pain conditions. ASP3662 is a potent and selective inhibitor of 11ß-HSD1. Two phase I clinical studies were conducted to assess the safety, tolerability, pharmacokinetics (PKs), and pharmacodynamics (PDs) of single and multiple ascending doses of ASP3662 in healthy young and elderly non-Japanese and young Japanese subjects. Nonlinear, more than dose-proportional PKs were observed for ASP3662 after single-dose administration, particularly at lower doses (≤ 6 mg); the PKs at steady state were dose proportional, although the time to ASP3662 steady state was dose dependent at lower doses (≤ 2 mg). Similar PKs were observed among young Japanese, young non-Japanese, and elderly non-Japanese subjects. Specific inhibition of 11ß-HSD1 occurred after both single and multiple doses of ASP3662. A marked dissociation between PKs and PDs was observed after single but not multiple doses of ASP3662. ASP3662 was generally safe and well tolerated.

Effects of noninvasive ventilation on sleep outcomes in amyotrophic lateral sclerosis.

STUDY OBJECTIVES: The objective was to study the effects on noninvasive ventilation on sleep outcomes in patient with ALS, specifically oxygenation and overall sleep quality. METHODS: Patients with ALS who met criteria for initiation of NIV were studied with a series of 2 home PSG studies, one without NIV and a follow-up study while using NIV. Primary outcome was a change in the maximum overnight oxygen saturation; secondary outcomes included change in mean overnight oxygen saturation, apnea and hypopnea indexes, sleep latency, sleep efficiency, sleep arousals, and sleep architecture. RESULTS: A total of 94 patients with ALS were screened for eligibility; 15 were enrolled; and 12 completed study procedures. Maximum overnight oxygen saturation improved by 7.0% (p = 0.01) and by 6.7% during REM sleep (p = 0.02) with NIV. Time spent below 90% oxygen saturation was also significant-ly better with NIV (30% vs 19%, p < 0.01), and there was trend for improvement in mean overnight saturation (1.5%, p = 0.06). Apnea index (3.7 to 0.7), hypopnea index (6.2 to 5.7), and apnea hypopnea index (9.8 to 6.3) did not significantly improve after introducing NIV. NIV had no effect on sleep efficiency (mean change 10%), arousal index (7 to 12), or sleep stage distribution (Friedman chi-squared = 0.40). CONCLUSIONS: NIV improved oxygenation but showed no significant effects on sleep efficiency, sleep arousals, restful sleep, or sleep architecture. The net impact of these changes for patients deserves further study in a larger group of ALS patients.