Xe 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. 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 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 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 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 (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 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.