MRI assessment and guidance for regionally targeted pulmonary interventions
Notice bibliographique
Résumé
A 12-year-old male with granulomatosis with polyangiitis presented with debilitating shortness of breath. He was referred for flexible bronchoscopy with endoscopic balloon dilation.1 Two initial bronchoscopies revealed obliterative membranes occluding all segments in the right upper lobe (RUL) and the superior segment of the left lower lobe (LLL) with severe diffuse stenosis of lobar bronchi. During these bronchoscopies, the RUL posterior segment was opened and the right middle lobe (RML) underwent balloon dilation. Following the second bronchoscopy, informed assent/consent was obtained from the patient/family for Xe magnetic resonance imaging (MRI) under a protocol approved by the local Institutional Review Board (2014-5279) with FDA IND (123,477). Four subsequent bronchoscopy interventions were completed where endoscopic cryodevitalization was performed in areas of stenosis and membranes were opened and dilated. These subsequent four interventions were prioritized for specific lobes/segments, informed in part by the HP 129Xe MRI findings in consultation with the clinical care team. Xe functional lung MRI was acquired between the second and third bronchoscopy and after the sixth bronchoscopy. 129Xe ventilation images were used to calculate ventilation defect percentages (VDP) and changes in regional ventilation.2 Measures of the apparent diffusion coefficient for 129Xe in the lung airspace were modeled to derive the mean linear intercept (LM), the distance between gas exchange surfaces in the acinar airway complex.3 Additional information about 129Xe MRI can be found in the supplement. Before the MRI, the patient had a percent-predicted forced expiratory volume in the first second (FEV1) = 49%, forced vital capacity (FVC) = 79%, forced expiratory flow between 25% and 75% of vital capacity (FEF25–75) = 26%, and FEV1/FVC = 52%. The first MRI revealed a VDP = 26% (age-matched healthy controls ≤5%, Figures 1 and 2) and an LM = 174 μm (age-matched healthy control ≈ 160 μm, Figure 3).4 Based on this ventilation image (Figure 2A), six segments were prioritized: anterior segment of the left upper lobe (LUL, VDPLUL = 42%), superior/inferior segments of the lingula (VDPlingula = 20%), superior segment of the LLL (VDPLLL = 5%) and anterior/apical segments of the RUL (VDPRUL = 67%). During the interventions, five of the targeted airways were successfully opened/dilated (Figure 1); the sixth, inferior lingula, could not be found. Additionally, at the third bronchoscopy, the RUL lobe was completely occluded and not preserved due to risk associated with the location of the membrane (near the pulmonary artery and aorta) and no clear airway lumen. Following the sixth bronchoscopy, FEV1, FVC, FEF25–75, and FEV1/FVC improved by 13, 14, 3, and 4 percentage points. 129Xe MRI ventilation showed regions of improvement and worsening, resulting in a decrease of only 3% in VDP (Figure 2B,C). For the targeted airways, VDP improved by 4% in the LUL and 11% in the lingula while staying constant (change <±2%) in the LLL. 129Xe diffusion imaging indicated a small increase of LM to 182 μm, primarily due to the right lung (RL mean = 197 μm; 164 μm for lower lung; Figure 3). 129Xe ventilation improved in the successfully dilated segments. However, the right lower lobe worsened due to progressive bronchiectasis. The RUL, which reclosed, resulted in compensatory hyperinflation of the RML, evidenced by a 12% increase in regional ventilation and an increase in LM for the RML (174–197 μm). The FEV1 change was relatively small as it measures the global respiratory system and is unable to decouple intervention from functional decline. With 129Xe MRI, the bronchoscopist could determine if potential lung function gains outweighed risks for addressing a specific obstruction, plan future interventions efficiently, and accurately determine regional response to interventions, improving the patient's symptoms. In comparison, pulmonary function testing was unable to detect the extent of improvement due to a competing functional decline. Importantly, the patient-centered improvements were excellent: He rapidly transitioned from debilitating shortness of breath to playing basketball again. In the future, more personalized interventions are possible, especially in complex cases, with similar improvement in patient symptoms. Matthew M. Willmering: Conceptualization; methodology; formal analysis; investigation; data curation; writing—original draft; writing—review and editing; visualization. Abdullah S. Bdaiwi: Methodology; formal analysis; writing—review and editing. Kimberly R. Kaspy: Investigation; writing—review and editing. John Racadio: Investigation; writing—review and editing. Evans M. Machogu: Data curation; writing—review and editing. Olivia A. Kwan: Data curation; writing—review and editing. Pi Chun Cheng: Data curation; writing—review and editing. Jason C. Woods: Conceptualization; methodology; writing—review and editing; supervision; funding acquisition. Erik B. Hysinger: Conceptualization; methodology; investigation; data curation; writing—original draft; writing—review and editing; visualization; funding acquisition; supervision. This work was funded by the National Institutes of Health (R01 HL1446689). The authors would like to thank Priyanka Desirazu for research coordination; Carter McMaster for preparing HP 129Xe gas; Megan Schmitt for patient monitoring during the scans; and Kaley Bridgewater, Kyle Crowe, Kelsey Murphy, and J. Matthew Lanier for their help operating the magnetic resonance imaging scanner. Matthew M. Willmering is a consultant to Polarean. Jason C. Woods is a consultant to Polarean. The data that support the findings of this study are available from the corresponding author upon reasonable request. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».