Search

Predicting increased risk of future healthcare utilization in chronic obstructive pulmonary disease (COPD) patients is an important goal for improving patient management. Our objective was to determine the importance of computed tomography (CT) lung imaging measurements relative to other demographic and clinical measurements for predicting future health services use with machine learning in COPD. In this retrospective study, lung function measurements and chest CT images were acquired from Canadian Cohort of Obstructive Lung Disease study participants from 2010 to 2017 (https://clinicaltrials.gov , NCT00920348). Up to two follow-up visits (1.5- and 3-year follow-up) were performed and participants were asked for details related to healthcare utilization. Healthcare utilization was defined as any COPD hospitalization or emergency room visit due to respiratory problems in the 12 months prior to the follow-up visits. CT analysis was performed (VIDA Diagnostics Inc.); a total of 108 CT quantitative emphysema, airway and vascular measurements were investigated. A hybrid feature selection method with support vector machine classifier was used to predict healthcare utilization. Performance was determined using accuracy, F1-measure and area under the receiver operating characteristic curve (AUC) and Matthews's correlation coefficient (MC). Of the 527 COPD participants evaluated, 179 (35%) used healthcare services at follow-up. There were no significant differences between the participants with or without healthcare utilization at follow-up for age (p = 0.50), sex (p = 0.44), BMI (p = 0.05) or pack-years (p = 0.76). The accuracy for predicting subsequent healthcare utilization was 80% ± 3% (F1-measure = 74%, AUC = 0.80, MC = 0.6) when all measurements were considered, 76% ± 6% (F1-measure = 72%, AUC = 0.77, MC = 0.55) for CT measurements alone and 65% ± 5% (F1-measure = 60%, AUC = 0.67, MC = 0.34) for demographic and lung function measurements alone. The combination of CT lung imaging and conventional measurements leads to greater prediction accuracy of subsequent health services use than conventional measurements alone, and may provide needed prognostic information for patients suffering from COPD. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: The relationships between computed tomography (CT) pulmonary vascularity and MRI ventilation is not well-understood in chronic obstructive pulmonary disease (COPD) patients. Our objective was to evaluate CT pulmonary vascular and MRI ventilation measurements in ex-smokers and to investigate their associations and how such measurements change over time.Materials and Methods: Ninety ex-smokers (n = 41 without COPD 71 ± 10 years and n = 49 COPD 71 ± 8 years) provided written informed-consent to an ethics-board approved protocol and underwent imaging and pulmonary-function-tests twice, 31 ± 7 months apart. 3He MRI was acquired to generate ventilation-defect-percent (VDP). CT measurements of the relative area-of-the-lung with attenuation <-950 Hounsfield units (RA950), pulmonary vascular total-blood-volume (TBV) and percent of vessels with radius < one voxel (PV1) were evaluated.Results: At baseline, there were significant differences in RA950 (p = 0.0001), VDP (p = 0.0001), total-blood-volume (p = 0.0001) and PV1 (p = 0.01) between ex-smokers and COPD participants as well as for VDP (p = 0.0001) in COPD participants with and without emphysema. The annual FEV1 change (-40 ± 93 mL/year) was not different among participant subgroups (p = 0.87), but the annual RA950 (p = 0.01) and PV1 (p = 0.007) changes were significantly different in participants with an accelerated annual FEV1 decline as compared to participants with a diminished annual FEV1 decline. There were significant but weak relationships for PV1 with FEV1%pred (p = 0.02), FEV1/FVC (p = 0.001), and log RA950 (p = 0.0001), but not VDP (p=0.20). The mean change in PV1 was also weakly but significantly related to the change in RA950 (p = 0.02).Conclusion: CT pulmonary vascular measurements were significantly different in ex-smokers and participants with COPD and related to RA950 but not VDP worsening over 2.5 years. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: To improve intra- and interobserver variability and enable the use of functional magnetic resonance imaging (MRI) for multicenter, multiobserver studies, we generated a semiautomated segmentation method for hyperpolarized helium-3 (3He) MRI. Therefore the objective of this study was to compare the reproducibility and spatial agreement of manual and semiautomated segmentation of 3He MRI ventilation defect volume (VDV) and ventilation volume (VV) in subjects with asthma, chronic obstructive pulmonary disease (COPD), and cystic fibrosis (CF). Materials and Methods: The multistep semiautomated segmentation method we developed employed hierarchical K-means clustering to classify 3He MRI pixel intensity values into five user-determined clusters ranging from signal void to hyperintense. A seeded region-growing algorithm was also used to segment the 1H MRI thoracic cavity for coregistration to the 3He cluster-map, generating VDV and VV. Results: We compared manual segmentation performed by an expert observer and semiautomated measurements of 3He MRI VDV and observed strong significant correlations between the volumes generated using each method (asthma, n = 5, r = 0.89, P < .0001; COPD, n = 5, r = 0.84, P < .0001; CF, n = 5, r = 0.89, P < .0001). Semiautomated VDV had high interobserver reproducibility (coefficient of variation [CV] = 7%, intraclass correlation coefficient [ICC] = 0.96); intraobserver reproducibility was significantly higher for semiautomated (CV = 5%, ICC = 1.00) compared to manual VDV (CV = 12%, ICC = 0.98). Spatial agreement for VV determined using the Dice coefficient (D) was also high for all disease states (asthma, D = 0.95; COPD, D = 0.88; CF, D = 0.90). Conclusions: Semiautomated segmentation 3He MRI provides excellent inter- and intraobserver precision with high spatial and quantitative agreement with manual measurements enabling its use in longitudinal studies. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: The structural lung features that characterize individuals with preserved ratio impaired spirometry (PRISm) that remain stable overtime are unknown. The objective of this study was to use machine learning models with computed tomography (CT) imaging to classify stable PRISm from stable controls and stable COPD and identify discriminative features., Materials and Methods: A total of 596 participants that did not transition between control, PRISm and COPD groups at baseline and 3-year follow-up were evaluated: n = 274 with normal lung function (stable control), n = 22 stable PRISm, and n = 300 stable COPD. Investigated features included: quantitative CT (QCT) features (n = 34), such as total lung volume (%TLC CT ) and percentage of ground glass and reticulation (%GG+Reticulation texture ), as well as Radiomic (n = 102) features, including varied intensity zone distribution grainy texture (GLDZM ZDV ). Logistic regression machine learning models were trained using various feature combinations (Base, Base+QCT, Base+Radiomic, Base+QCT+Radiomic). Model performances were evaluated using area under receiver operator curve (AUC) and comparisons between models were made using DeLong test; feature importance was ranked using Shapley Additive Explanations values., Results: Machine learning models for all feature combinations achieved AUCs between 0.63-0.84 for stable PRISm vs. stable control, and 0.65-0.92 for stable PRISm vs. stable COPD classification. Models incorporating imaging features outperformed those trained solely on base features (p < 0.05). Compared to stable control and COPD, those with stable PRISm exhibited decreased %TLC CT and increased %GG+Reticulation texture and GLDZM ZDV ., Conclusion: These findings suggest that reduced lung volumes, and elevated high-density and ground glass/reticulation patterns on CT imaging are associated with stable PRISm., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Wan C. Tan MD reports financial support was provided by Canadian Institute of Heath Research(CIHR), Astra Zeneca Canada Ltd., Boehringer Ingelheim Canada Ltd., GlaxoSmithKline Canada Ltd., Merck Canada Inc., Novartis Pharmaceuticals Canada Inc., Nycomed Canada Inc., Pfizer Canada Ltd. Jean Bourbeau MD reports financial support was provided by Canadian Institute of Heath Research (CIHR), Réseau en santé respiratoire du FRQS, McGill University, McGill University Health Centre, AstraZeneca Canada Ltd., Boehringer Ingelheim Canada Ltd., GlaxoSmithKline Canada Ltd., Grifols Canada Ltd., Novartis Pharmaceuticals Canada Inc., Sanofi, Trudell Canada Ltd., COVIS Pharma Canada Ltd. Miranda Kirby PhD reports financial support was provided by Natural Sciences and Engineering Research Council (NSERC) Discovery Grant, The Early Researchers Award Program, The Canada Research Chair Program (Tier II). Miranda Kirby PhD reports a relationship with VIDA Diagnostics Inc that includes consulting or advisory. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Rationale and Objectives: It remains difficult to predict longitudinal outcomes in long-COVID, even with chest CT and functional MRI. 129 Xe MRI reflects airway dysfunction, measured using ventilation defect percent (VDP) and in long-COVID patients, MRI VDP was abnormal, suggestive of airways disease. While MRI VDP and quality-of-life improved 15-month post-COVID infection, both remained abnormal. To better understand the relationship of airways disease and quality-of-life improvements in patients with long-COVID, we extracted 129 Xe ventilation MRI textures and generated machine-learning models in an effort to predict improved quality-of-life, 15-month post-infection., Materials and Methods: Long-COVID patients provided written-informed consent to 3-month and 15-month post-infection visits. Pyradiomics was used to extract 129 Xe ventilation MRI texture features, which were ranked using a Random-Forest classifier. Top-ranking features were used in classification models to dichotomize patients based on St. George's Respiratory Questionnaire (SGRQ) score improvement greater than the minimal-clinically-important-difference (MCID). Classification performance was evaluated using the area under the receiver-operator-characteristic-curve (AUC), sensitivity, and specificity., Results: 120 texture features were extracted from 129 Xe ventilation MRI in 44 long-COVID participants (54 ± 14 years), including 30 (52 ± 12 years) with ΔSGRQ≥MCID and 14 (58 ± 18 years) with ΔSGRQ129 Xe MRI ventilation textures explained improved SGRQ-scores 12 months later, and outperformed clinical models. Their unique spatial-intensity information helps build our understanding about long-COVID airway dysfunction., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Dr. Grace Parraga reports financial support was provided by Ontario Ministry of Health and Long-Term Care. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Rationale and Objectives: The relationships between computed tomography (CT) pulmonary vascularity and MRI ventilation is not well-understood in chronic obstructive pulmonary disease (COPD) patients. Our objective was to evaluate CT pulmonary vascular and MRI ventilation measurements in ex-smokers and to investigate their associations and how such measurements change over time., Materials and Methods: Ninety ex-smokers (n = 41 without COPD 71 ± 10 years and n = 49 COPD 71 ± 8 years) provided written informed-consent to an ethics-board approved protocol and underwent imaging and pulmonary-function-tests twice, 31 ± 7 months apart. 3 He MRI was acquired to generate ventilation-defect-percent (VDP). CT measurements of the relative area-of-the-lung with attenuation <-950 Hounsfield units (RA 950 ), pulmonary vascular total-blood-volume (TBV) and percent of vessels with radius < one voxel (PV 1 ) were evaluated., Results: At baseline, there were significant differences in RA 950 (p = 0.0001), VDP (p = 0.0001), total-blood-volume (p = 0.0001) and PV 1 (p = 0.01) between ex-smokers and COPD participants as well as for VDP (p = 0.0001) in COPD participants with and without emphysema. The annual FEV 1 change (-40 ± 93 mL/year) was not different among participant subgroups (p = 0.87), but the annual RA 950 (p = 0.01) and PV 1 (p = 0.007) changes were significantly different in participants with an accelerated annual FEV 1 decline as compared to participants with a diminished annual FEV 1 decline. There were significant but weak relationships for PV 1 with FEV 1%pred (p = 0.02), FEV 1 /FVC (p = 0.001), and log RA 950 (p = 0.0001), but not VDP (p=0.20). The mean change in PV 1 was also weakly but significantly related to the change in RA 950 (p = 0.02)., Conclusion: CT pulmonary vascular measurements were significantly different in ex-smokers and participants with COPD and related to RA 950 but not VDP worsening over 2.5 years., (Copyright © 2020 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)