Effect of CFTR modulator therapy with elexacaftor/tezacaftor/ivacaftor on pulmonary ventilation derived by 3D phase-resolved functional lung MRI in cystic fibrosis patients

A total of 23 CF patients (13 female, mean age ± standard deviation (SD), 21.0 ± 9.3 years, mean FEV₁ % pred. at baseline 87.8%) were recruited in this prospective observational single-center study. The local ethics committee of Hannover Medical School (8922_B0_S_2020) approved the study and informed written consent was obtained prior to the examination from each study participant and/or their legal guardians. Participants were included when they (1) were older than 12 years, (2) were compound-heterozygous for F508del and a minimal function mutation or homozygous for F508del, and (3) had no prior exposure to ETI. The following exclusion criteria were applied: (1) pulmonary exacerbation at baseline or follow-up, (2) history of solid organ transplantation, (3) pregnancy, (4) claustrophobia, (5) metallic implantations incompatible with MR imaging. Morphological and functional 3D PREFUL MRI, PFT, and N₂-MBW were evaluated at baseline and 8–16 weeks after initiation of ETI therapy. The morphological MRI images, spirometry data, and LCI values of the included CF patients have been reported in a previous study [15].

MR examinations were performed on a 1.5-T scanner (MAGNETOM Avanto; Siemens Healthcare). The MR protocol consisted of the following sequences [15]: (1) T2-weighted half-Fourier acquisition single-shot turbo spin echo (HASTE) sequences in axial and coronal orientation, acquired by breath-hold maneuvers; (2) T2-weighted turbo spin echo sequences in PROPELLER technique (BLADE), acquired in axial and coronal orientation by using respiratory navigator triggering; (3) T1-weighted volumetric interpolated breath-hold examination (VIBE) sequences, acquired in axial and coronal orientation. (4) 2D non-contrast-enhanced PREFUL technique to assess lung perfusion and 3D PREFUL technique to asses lung ventilation.

For 3D PREFUL, an 8-min measurement [11] was used to assess regional lung ventilation of the whole lung. The following sequence parameters were used for the 3D PREFUL: echo time (TE) 0.81 ms, repetition time (TR) 1.9 ms, flip angle 3.5°, field of view (FOV) 45 × 45 cm², matrix size 112 × 112 interpolated to 224 × 224, 52–60 partitions, 6/8 partial Fourier along the partition dimension, pixel bandwidth 1500 Hz/pixel, slice thickness 4 mm interpolated to 2 mm.

Morphological MR images were evaluated based on a well-established semi-quantitative scoring system [16]. Briefly, the global MRI score consists of morphology score and perfusion score. The morphology score is divided into the following subscores: bronchial wall thickening/bronchiectasis, mucus plugging, abscesses/sacculations, consolidations, and special findings, i.e., pleural affection. The perfusion scores were derived by the subtraction of the baseline images (contrast-free) from the images with maximal contrast in the lung tissue [16]. All subscores including the perfusion score are evaluated for each lobe with values of (a) 0 (no abnormality), (b) 1 (< 50% of the lobe affected), and (c) 2 (≥ 50% of the lobe affected). Considering lingula as a sixth lobe, the global MRI score ranges from 0 (normal lung) to 72 (maximally affected lung).

The dynamically acquired 3D PREFUL data were reconstructed to ~40 breathing phases and each phase was registered towards end-inspiration image [17]. Using 3D PREFUL registered images, ventilation defect maps derived from static regional ventilation (RVent) [18] and dynamic cross-correlation (CC) [9] maps based on previously published thresholds were calculated [14]. CC maps assess the dynamics of ventilation using regional flow-volume loops during tidal breathing. Furthermore, ventilation defect percentage (VDP) percent values were computed from both ventilation defect maps as the number of voxels with ventilation below the threshold divided by the total number of voxels of the segmented lung volume. To analyze the treatment effect on a regional level, morphological images of the second visit were co-registered to the morphological end-inspiratory image of the first visit. The treatment response maps (TRMs) were then calculated by subtracting the ventilation defect map after therapy from the baseline ventilation defect maps. Post-treatment improved ventilation volume (IVV_RVent and IVV_CC) was calculated as the number of positively changed voxels in TRMs (absolute amount of ventilation defect voxels at baseline measurement which resolved after ETI therapy in the follow-up measurement) multiplied by lung volume in milliliters. Both volumes were normalized to body surface area (BSA) at baseline. For image analysis, the lung parenchyma of the 3D PREFUL baseline end-inspiratory reconstructed image was automatically segmented using a 3D convolutional neuronal network (CNN) and manually corrected if needed. The details of the 3D CNN network were described previously [17].

Spirometry outcomes (forced expiratory volume in 1 second (FEV₁) and mid-expiratory flow at 25% of forced vital capacity (MEF25)) were obtained according to American Thoracic Society (ATS)/European Respiratory Society (ERS) guidelines [19] with global lung function initiative (GLI) reference values [20]. Lung clearance index (LCI) was assessed using the N₂-MBW technique according to ATS/ERS guidelines [21].

For the 3D PREFUL regional parameters of RVent and CC, mean values and SD values inside lung parenchyma were computed. Further ventilation impairment was assessed using 3D PREFUL by a total lung VDP and post-treatment IVV values in milliliters.

The treatment effect on all derived MRI and clinical outcome parameters was analyzed using paired Wilcoxon signed-rank tests.

MRI scans and PFT measurements were successfully performed in all 23 CF patients. Two out of 23 CF patients did not comply with N₂-MBW measurement at the follow-up visit. Due to coronavirus pandemic-related restrictions, the follow-up measurement of seven out of 23 CF patients (30%) had to be postponed up to 19 weeks with one patient exception (28 weeks).

Therapy with ETI significantly improved all ventilation markers derived by 3D PREFUL MRI (range of improvement 1.9–28.1%, all p < 0.0056, Table 1) as well as MRI global, morphology, and perfusion scores (range of improvement 9.5–31.9%, all p < 0.02, Table 1), except for the 3D PREFUL–derived mean RVent parameter (improvement 0.6%, p = 0.16). As reported previously [15], FEV₁, MEF25, and LCI significantly improved after therapy (range of improvement 22.4–74.7%, all p < 0.0001, Table 1). Representative 3D PREFUL ventilation parameters at baseline and post-ETI treatment initiation are depicted in Fig. 1. Treatment effects on 3D PREFUL ventilation parameters are depicted in Fig. 2.

There was no significant correlation between absolute and relative treatment changes of 3D PREFUL MRI ventilation parameters (mean RVent, SD RVent, mean CC, SD CC, VDP_RVent, and VDP_CC) with FEV₁, MEF25, LCI, and MRI scores (p > 0.05), except relative change of SD RVent parameter, which was significantly correlated with relative change of LCI (r = 0.48, p = 0.0275). Figure 3 shows regional TRMs derived by 3D PREFUL for two study participants. Looking only at positive changes in TRMs, the mean improved ventilation volume normalized to BSA was 147 (SD = 94) and 129 (SD = 114) mL/m² for RVent (IVV_RVent) and CC (IVV_CC) parameters, respectively. The IVV_RVent normalized to BSA was significantly correlated with relative treatment changes of MEF25 and mucus plugging MRI score (all |r| > 0.48, all p < 0.219, Table 2).

In post-treatment analyses, 3D PREFUL–derived VDP values significantly correlated with spirometry, LCI, global score, morphology score, and perfusion score (all |r| > 0.44, all p < 0.0348, Table 3, Fig. 4), except for correlation of VDP_CC with global and morphology score. CC values derived by 3D PREFUL correlated significantly with FEV₁, MEF25, and LCI (all |r| > 0.49, all p < 0.0253, Table 3).