{"title":"129Xe MRI 通气纹理与长期 COVID 患者生活质量的纵向改善","authors":"","doi":"10.1016/j.acra.2024.03.014","DOIUrl":null,"url":null,"abstract":"<div><h3>Rationale and Objectives</h3><div>It remains difficult to predict longitudinal outcomes in long-COVID, even with chest CT and functional MRI. <sup>129</sup>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 <sup>129</sup>Xe ventilation MRI textures and generated machine-learning models in an effort to predict improved quality-of-life, 15-month post-infection.</div></div><div><h3>Materials and Methods</h3><div>Long-COVID patients provided written-informed consent to 3-month and 15-month post-infection visits. Pyradiomics was used to extract <sup>129</sup>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.</div></div><div><h3>Results</h3><div>120 texture features were extracted from <sup>129</sup>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 ΔSGRQ<!--> <!--><<!--> <!-->MCID. An MRI-texture model (AUC<!--> <!-->=<!--> <!-->0.89) outperformed a clinical-measurement model (AUC<!--> <!-->=<!--> <!-->0.72) for predicting improved SGRQ, 12 months later. Top-performing textures correlated with MRI VDP (<em>P</em> < .05), central-airways resistance (<em>P</em> < .05), forced-vital-capacity (ρ = .37, <em>P</em> = .01) and diffusing-capacity for carbon-monoxide (ρ = .39, <em>P</em> = .03).</div></div><div><h3>Conclusion</h3><div>A machine learning model exclusively trained on <sup>129</sup>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.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":null,"pages":null},"PeriodicalIF":3.8000,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"129Xe MRI Ventilation Textures and Longitudinal Quality-of-Life Improvements in Long-COVID\",\"authors\":\"\",\"doi\":\"10.1016/j.acra.2024.03.014\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Rationale and Objectives</h3><div>It remains difficult to predict longitudinal outcomes in long-COVID, even with chest CT and functional MRI. <sup>129</sup>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 <sup>129</sup>Xe ventilation MRI textures and generated machine-learning models in an effort to predict improved quality-of-life, 15-month post-infection.</div></div><div><h3>Materials and Methods</h3><div>Long-COVID patients provided written-informed consent to 3-month and 15-month post-infection visits. Pyradiomics was used to extract <sup>129</sup>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.</div></div><div><h3>Results</h3><div>120 texture features were extracted from <sup>129</sup>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 ΔSGRQ<!--> <!--><<!--> <!-->MCID. An MRI-texture model (AUC<!--> <!-->=<!--> <!-->0.89) outperformed a clinical-measurement model (AUC<!--> <!-->=<!--> <!-->0.72) for predicting improved SGRQ, 12 months later. Top-performing textures correlated with MRI VDP (<em>P</em> < .05), central-airways resistance (<em>P</em> < .05), forced-vital-capacity (ρ = .37, <em>P</em> = .01) and diffusing-capacity for carbon-monoxide (ρ = .39, <em>P</em> = .03).</div></div><div><h3>Conclusion</h3><div>A machine learning model exclusively trained on <sup>129</sup>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.</div></div>\",\"PeriodicalId\":50928,\"journal\":{\"name\":\"Academic Radiology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.8000,\"publicationDate\":\"2024-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Academic Radiology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1076633224001569\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Academic Radiology","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1076633224001569","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
129Xe MRI Ventilation Textures and Longitudinal Quality-of-Life Improvements in Long-COVID
Rationale and Objectives
It remains difficult to predict longitudinal outcomes in long-COVID, even with chest CT and functional MRI. 129Xe 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 129Xe 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 129Xe 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 129Xe ventilation MRI in 44 long-COVID participants (54 ± 14 years), including 30 (52 ± 12 years) with ΔSGRQ ≥ MCID and 14 (58 ± 18 years) with ΔSGRQ < MCID. An MRI-texture model (AUC = 0.89) outperformed a clinical-measurement model (AUC = 0.72) for predicting improved SGRQ, 12 months later. Top-performing textures correlated with MRI VDP (P < .05), central-airways resistance (P < .05), forced-vital-capacity (ρ = .37, P = .01) and diffusing-capacity for carbon-monoxide (ρ = .39, P = .03).
Conclusion
A machine learning model exclusively trained on 129Xe 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.
期刊介绍:
Academic Radiology publishes original reports of clinical and laboratory investigations in diagnostic imaging, the diagnostic use of radioactive isotopes, computed tomography, positron emission tomography, magnetic resonance imaging, ultrasound, digital subtraction angiography, image-guided interventions and related techniques. It also includes brief technical reports describing original observations, techniques, and instrumental developments; state-of-the-art reports on clinical issues, new technology and other topics of current medical importance; meta-analyses; scientific studies and opinions on radiologic education; and letters to the Editor.