129Xe MRI Ventilation Textures and Longitudinal Quality-of-Life Improvements in Long-COVID

Harkiran K Kooner; Maksym Sharma; Marrissa J McIntosh; Inderdeep Dhaliwal; J Michael Nicholson; Miranda Kirby; Sarah Svenningsen; Grace Parraga

doi:10.1016/j.acra.2024.03.014

¹²⁹Xe MRI Ventilation Textures and Longitudinal Quality-of-Life Improvements in Long-COVID

Acad Radiol. 2024 Apr 17:S1076-6332(24)00156-9. doi: 10.1016/j.acra.2024.03.014. Online ahead of print.

Authors

Harkiran K Kooner¹, Maksym Sharma¹, Marrissa J McIntosh¹, Inderdeep Dhaliwal², J Michael Nicholson², Miranda Kirby³, Sarah Svenningsen⁴, Grace Parraga⁵

Affiliations

¹ Robarts Research Institute, Western University, London, Canada; Department of Medical Biophysics, Western University, London, Canada.
² Division of Respirology, Department of Medicine, Western University, London, Canada.
³ Department of Physics, Toronto Metropolitan University, Toronto, Canada.
⁴ Division of Respirology, Department of Medicine, McMaster University and Firestone Institute for Respiratory Health, St. Joseph's Health Care, Hamilton, Canada.
⁵ Robarts Research Institute, Western University, London, Canada; Department of Medical Biophysics, Western University, London, Canada; Division of Respirology, Department of Medicine, Western University, London, Canada. Electronic address: gparraga@uwo.ca.

PMID: 38637239
DOI: 10.1016/j.acra.2024.03.014

Abstract

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.