Chronic Lung Allograft Dysfunction Post Lung Transplantation: A Review of Computed Tomography Quantitative Methods for Detection and Follow-Up
Abstract
:1. Introduction
2. Airway Measurement Methods: Computer-Assisted Airway Morphometry
3. Lung Parenchyma Methods: Assessment of Lung Volume and Attenuation, and Their Variations between Inspiration and Expiration
4. Conclusions
Author Contributions
Funding
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Chambers, D.C.; Yusen, R.D.; Cherikh, W.S.; Goldfarb, S.B.; Kucheryavaya, A.Y.; Khusch, K.; Levvey, B.J.; Lund, L.H.; Meiser, B.; Rossano, J.W.; et al. The Registry of the International Society for Heart and Lung Transplantation: Thirty-Fourth Adult Lung and Heart-Lung Transplantation Report—2017; Focus Theme: Allograft Ischemic Time. J. Heart Lung. Transplant. 2017, 36, 1047–1059. [Google Scholar] [CrossRef] [Green Version]
- Verleden, G.M.; Glanville, A.R.; Lease, E.D.; Fisher, A.J.; Calabrese, F.; Corris, P.A.; Ensor, C.R.; Gottlieb, J.; Hachem, R.R.; Lama, V.; et al. Chronic Lung Allograft Dysfunction: Definition, Diagnostic Criteria, and Approaches to Treatment—A Consensus Report from the Pulmonary Council of the ISHLT. J. Heart Lung. Transplant. 2019, 38, 493–503. [Google Scholar] [CrossRef] [Green Version]
- Hota, P.; Dass, C.; Kumaran, M.; Simpson, S. High-Resolution CT Findings of Obstructive and Restrictive Phenotypes of Chronic Lung Allograft Dysfunction: More Than Just Bronchiolitis Obliterans Syndrome. Am. J. Roentgenol. 2018, 211, W13–W21. [Google Scholar] [CrossRef] [PubMed]
- DerHovanessian, A.; Todd, J.L.; Zhang, A.; Li, N.; Mayalall, A.; Finlen Copeland, C.A.; Shino, M.; Pavlisko, E.N.; Wallace, W.D.; Gregson, A.; et al. Validation and Refinement of Chronic Lung Allograft Dysfunction Phenotypes in Bilateral and Single Lung Recipients. Ann. Am. Thorac. Soc. 2016, 13, 627–635. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Levy, L.; Huszti, E.; Renaud-Picard, B.; Berra, G.; Kawashima, M.; Takahagi, A.; Fuchs, E.; Ghany, R.; Moshkelgosha, S.; Keshavjee, S.; et al. Risk Assessment of Chronic Lung Allograft Dysfunction Phenotypes: Validation and Proposed Refinement of the 2019 International Society for Heart and Lung Transplantation Classification System. J. Heart Lung. Transplant. 2020, 39, 761–770. [Google Scholar] [CrossRef]
- Suhling, H.; Dettmer, S.; Greer, M.; Fuehner, T.; Avsar, M.; Haverich, A.; Welte, T.; Gottlieb, J. Phenotyping Chronic Lung Allograft Dysfunction Using Body Plethysmography and Computed Tomography. Am. J. Transplant. 2016, 16, 3163–3170. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Philippot, Q.; Debray, M.-P.; Bun, R.; Frija-Masson, J.; Bunel, V.; Morer, L.; Roux, A.; Picard, C.; Jebrak, G.; Dauriat, G.; et al. Use of CT-SCAN Score and Volume Measures to Early Identify Restrictive Allograft Syndrome in Single Lung Transplant Recipients. J. Heart Lung. Transplant. 2020, 39, 125–133. [Google Scholar] [CrossRef]
- Doellinger, F.; Weinheimer, O.; Zwiener, I.; Mayer, E.; Buhl, R.; Fahlenkamp, U.L.; Dueber, C.; Achenbach, T. Differences of Airway Dimensions between Patients with and without Bronchiolitis Obliterans Syndrome after Lung Transplantation-Computer-Assisted Quantification of Computed Tomography. Eur. J. Radiol. 2016, 85, 1414–1420. [Google Scholar] [CrossRef]
- Dettmer, S.; Peters, L.; de Wall, C.; Schaefer-Prokop, C.; Schmidt, M.; Warnecke, G.; Gottlieb, J.; Wacker, F.; Shin, H. Bronchial Wall Measurements in Patients after Lung Transplantation: Evaluation of the Diagnostic Value for the Diagnosis of Bronchiolitis Obliterans Syndrome. PLoS ONE 2014, 9, e93783. [Google Scholar] [CrossRef] [Green Version]
- Verleden, S.E.; Vos, R.; Vandermeulen, E.; Ruttens, D.; Bellon, H.; Heigl, T.; Van Raemdonck, D.E.; Verleden, G.M.; Lama, V.; Ross, B.D.; et al. Parametric Response Mapping of Bronchiolitis Obliterans Syndrome Progression after Lung Transplantation. Am. J. Transplant. 2016, 16, 3262–3269. [Google Scholar] [CrossRef] [Green Version]
- Belloli, E.A.; Degtiar, I.; Wang, X.; Yanik, G.A.; Stuckey, L.J.; Verleden, S.E.; Kazerooni, E.A.; Ross, B.D.; Murray, S.; Galbán, C.J.; et al. Parametric Response Mapping as an Imaging Biomarker in Lung Transplant Recipients. Am. J. Respir. Crit. Care Med. 2017, 195, 942–952. [Google Scholar] [CrossRef] [Green Version]
- Barbosa, E.J.M.; Lanclus, M.; Vos, W.; Van Holsbeke, C.; De Backer, W.; De Backer, J.; Lee, J. Machine Learning Algorithms Utilizing Quantitative CT Features May Predict Eventual Onset of Bronchiolitis Obliterans Syndrome After Lung Transplantation. Acad. Radiol. 2018, 25, 1201–1212. [Google Scholar] [CrossRef] [Green Version]
- Tissot, A.; Danger, R.; Claustre, J.; Magnan, A.; Brouard, S. Early Identification of Chronic Lung Allograft Dysfunction: The Need of Biomarkers. Front. Immunol. 2019, 10, 1681. [Google Scholar] [CrossRef] [Green Version]
- Gazourian, L.; Ash, S.; Meserve, E.E.K.; Diaz, A.; Estepar, R.S.J.; El-Chemaly, S.Y.; Rosas, I.O.; Divo, M.; Fuhlbrigge, A.L.; Camp, P.C.; et al. Quantitative Computed Tomography Assessment of Bronchiolitis Obliterans Syndrome after Lung Transplantation. Clin. Transplant. 2017, 31. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Solyanik, O.; Hollmann, P.; Dettmer, S.; Kaireit, T.; Schaefer-Prokop, C.; Wacker, F.; Vogel-Claussen, J.; Shin, H. Quantification of Pathologic Air Trapping in Lung Transplant Patients Using CT Density Mapping: Comparison with Other CT Air Trapping Measures. PLoS ONE 2015, 10, e0139102. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Barbosa, E.M.; Simpson, S.; Lee, J.C.; Tustison, N.; Gee, J.; Shou, H. Multivariate Modeling Using Quantitative CT Metrics May Improve Accuracy of Diagnosis of Bronchiolitis Obliterans Syndrome after Lung Transplantation. Comput. Biol. Med. 2017, 89, 275–281. [Google Scholar] [CrossRef] [PubMed]
- Dettmer, S.; Suhling, H.; Klingenberg, I.; Otten, O.; Kaireit, T.; Fuge, J.; Kuhnigk, J.M.; Gottlieb, J.; Haverich, A.; Welte, T.; et al. Lobe-Wise Assessment of Lung Volume and Density Distribution in Lung Transplant Patients and Value for Early Detection of Bronchiolitis Obliterans Syndrome. Eur. J. Radiol. 2018, 106, 137–144. [Google Scholar] [CrossRef] [PubMed]
- Horie, M.; Salazar, P.; Saito, T.; Binnie, M.; Brock, K.; Yasufuku, K.; Azad, S.; Keshavjee, S.; Martinu, T.; Paul, N. Quantitative Chest CT for Subtyping Chronic Lung Allograft Dysfunction and Its Association with Survival. Clin. Transplant. 2018, 32, e13233. [Google Scholar] [CrossRef] [PubMed]
- Galbán, C.J.; Boes, J.L.; Bule, M.; Kitko, C.L.; Couriel, D.R.; Johnson, T.D.; Lama, V.; Telenga, E.D.; van den Berge, M.; Rehemtulla, A.; et al. Parametric Response Mapping as an Indicator of Bronchiolitis Obliterans Syndrome after Hematopoietic Stem Cell Transplantation. Biol. Blood Marrow Transplant. 2014, 20, 1592–1598. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Saito, T.; Horie, M.; Sato, M.; Nakajima, D.; Shoushtarizadeh, H.; Binnie, M.; Azad, S.; Hwang, D.M.; Machuca, T.N.; Waddell, T.K.; et al. Low-Dose Computed Tomography Volumetry for Subtyping Chronic Lung Allograft Dysfunction. J. Heart Lung. Transplant. 2016, 35, 59–66. [Google Scholar] [CrossRef]
- Saito, M.; Chen-Yoshikawa, T.F.; Nakamoto, Y.; Kayawake, H.; Tokuno, J.; Ueda, S.; Yamagishi, H.; Gochi, F.; Okabe, R.; Takahagi, A.; et al. Unilateral Chronic Lung Allograft Dysfunction Assessed by Biphasic Computed Tomographic Volumetry in Bilateral Living-Donor Lobar Lung Transplantation. Transplant. Direct 2018, 4, e398. [Google Scholar] [CrossRef] [PubMed]
- Weinheimer, O.; Achenbach, T.; Bletz, C.; Duber, C.; Kauczor, H.U.; Heussel, C.P. About Objective 3-d Analysis of Airway Geometry in Computerized Tomography. IEEE Trans. Med. Imaging 2008, 27, 64–74. [Google Scholar] [CrossRef]
- Achenbach, T.; Weinheimer, O.; Biedermann, A.; Schmitt, S.; Freudenstein, D.; Goutham, E.; Kunz, R.P.; Buhl, R.; Dueber, C.; Heussel, C.P. MDCT Assessment of Airway Wall Thickness in COPD Patients Using a New Method: Correlations with Pulmonary Function Tests. Eur. Radiol. 2008, 18, 2731–2738. [Google Scholar] [CrossRef] [PubMed]
- Achenbach, T.; Weinheimer, O.; Buschsieweke, C.; Heussel, C.P.; Thelen, M.; Kauczor, H.U. Fully automatic detection and quantification of emphysema on thin section MD-CT of the chest by a new and dedicated software. Rofo 2004, 176, 1409–1415. [Google Scholar] [CrossRef] [PubMed]
- Horie, M.; Levy, L.; Houbois, C.; Salazar, P.; Saito, T.; Pakkal, M.; O’Brien, C.; Sajja, S.; Brock, K.; Yasufuku, K.; et al. Lung Density Analysis Using Quantitative Chest CT for Early Prediction of Chronic Lung Allograft Dysfunction. Transplantation 2019, 103, 2645–2653. [Google Scholar] [CrossRef] [PubMed]
- Chassagnon, G.; Vakalopoulou, M.; Paragios, N.; Revel, M.-P. Artificial Intelligence Applications for Thoracic Imaging. Eur. J. Radiol. 2020, 123, 108774. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Chassagnon, G.; Vakalopolou, M.; Paragios, N.; Revel, M.-P. Deep Learning: Definition and Perspectives for Thoracic Imaging. Eur. Radiol. 2020, 30, 2021–2030. [Google Scholar] [CrossRef]
- Chambers, D.C.; Cherikh, W.S.; Harhay, M.O.; Hayes, D.; Hsich, E.; Khush, K.K.; Meiser, B.; Potena, L.; Rossano, J.W.; Toll, A.E.; et al. The International Thoracic Organ Transplant Registry of the International Society for Heart and Lung Transplantation: Thirty-Sixth Adult Lung and Heart-Lung Transplantation Report-2019; Focus Theme: Donor and Recipient Size Match. J. Heart Lung. Transplant. 2019, 38, 1042–1055. [Google Scholar] [CrossRef]
Phenotypes | Physiological Changes | Radiological Changes |
---|---|---|
CLAD (Chronic lung allograft dysfunction) | Persistent ≥ 20% decline in FEV1 (on the basis of 2 FEV1 values at least 3 weeks apart) compared with the baseline value, defined as the mean of the best 2 post-operative FEV1 measurement values, in the absence of other etiologies such as infection or acute rejection | |
BOS (Bronchiolitis obliterans syndrome) | Persistent ≥ 20% decline in FEV1 compared with the baseline value (=CLAD definition) AND obstructive ventilatory defect (FEV1/forced vital capacity [FVC] < 0.7) | |
RAS (Restrictive allograft syndrome) | Persistent ≥ 20% decline in FEV1 compared with the baseline value (=CLAD definition) AND ≥10% decline in TLC relative to baseline | Persistent opacities on chest imaging |
Mixed phenotype | Persistent ≥ 20% decline in FEV1 compared with the baseline value (=CLAD definition) AND combination of obstructive and restrictive ventilatory defect (FEV1/FVC < 0.7 and a TLC ≤ 90% of baseline) | Persistent opacities on chest imaging |
Undefined phenotype (1) | Persistent ≥ 20% decline in FEV1 compared with the baseline value (=CLAD definition) AND combination of obstructive and restrictive ventilatory defect (FEV1/FVC < 0.7 and a TLC ≤ 90% of baseline) | |
Undefined phenotype (2) | Persistent ≥ 20% decline in FEV1 compared with the baseline value (=CLAD definition) AND obstructive ventilatory defect (FEV1/FVC < 0.7) and no decline in TLC | Persistent opacities on chest imaging |
Author Year | Year | Study Design/Number of Patients | Time of CT Evaluation | Software | Main Quantification Parameters | Main Results |
---|---|---|---|---|---|---|
Airway measurement methods | ||||||
Dettmer [9] | 2014 | Prospective study 141 patients (25 BOS+) | 6, 12, 24 months after LTx | MeVis Airway Examiner | WT, WA%, WTdiff between inspiration and expiration on two selected bronchi B01 and B10 | Greater WA% on inspiration in BOS+ |
Doellinger [8] | 2016 | Retrospective study 26 patients (12 BOS+) | All available CT scans after LTx | YACTA module v.1.0.7.16 | ∆WT and ∆WA%: temporal change of WT and WA%; | Temporal changes of WT and WA% showed significant differences between BOS+ and BOS− |
Gazourian [14] | 2017 | Retrospective study 66 patients (20 controls, 22 BOS non progressors and 24 BOS progressors) | non-volumetric CT closest to baseline FEV1 and 2 follow-up CT scans | Airway Inspector (www.airwayinspector.org) | Internal lumen perimeter Lumen airway Airway vessel (A/V) ratio | Increase in the A/V ratio on follow-up CT scans for BOS progressors |
Barbosa [12] | 2018 | Retrospective study 71 patients (41 BOS+) | 2 CT scans (>3 months apart) | Mimics, TGrid 14.0 and Fluent 14.0 | Airway volumes Airway resistances | Increase in central airway volume on expiratory CT in BOS+ Smaller airway volumes and airway surfaces and higher airway resistances at baseline in BOS developers |
Vascular measurement method | ||||||
Gazourian [14] | 2017 | Retrospective study 22 patients (13 BOS+) | 2 volumetric CT angiographies after LTx | Bronchi: Airway Inspector Vessels: Upper thresholding (cut-off level of −500 HU) and use of a connected components technique | Vessel cross sectional area (CSA) Airway lumen area Airway/Vascular ratio (A/V ratio) | Overtime decrease in CSA in BOS+ Overtime Increase in A/V ratio in BOS+ |
Parenchyma-based methods: quantification of air trapping | ||||||
Belloli [11] | 2017 | Retrospective study (22 BOS+ and controls matched by time from LTx; 52 BOS+) | Date of BOS | Lung segmentation: In-house algorithm Insp/Expiratory registration algorithm: Elastix | Parametric response mapping (PRM): Density-based quantification of air trapping (PRMfSAD) and parenchymal disease (PRMPD) | FEV1 decline associated with higher PRMfSAD FEV1 and FVC decline associated with higher PRMPD PRMfSAD ≥ 30% strongest predictor of death |
Verleden [10] | 2016 | Retrospective study 40 patients (20 BOS+) | CT scans before, at the time of and after of the diagnostic of BOS | Lung segmentation: In-house algorithm Insp/Expiratory registration algorithm: Elastix | Density-based quantification of air trapping (PRMfSAD), parenchymal disease (PRMPD) and normal lung (PRMNormal) | Increase in PRMfSAD and decrease in PRMNormal in BOS+ No difference in PRMfSAD between BOS- and BOS+ before the diagnosis of BOS |
Solyanik [15] | 2015 | Prospective study 147 patients (34 with air-trapping) | CT at 6 months after LTx | Not mentioned | Density-based quantification of air trapping (EXP-790 HU to -950 HU, E/I-MLD) Density mapping: voxel-to-voxel insp/expiration mapping | DM has the highest correlation to RV/TLC (r = 0.663, p < 0.001) DM and E/I-ratio MLD showed better correlation with RV/TLC than EXP-790HU to -950HU |
Barbosa [16] | 2017 | Retrospective study 174 patients (98 BOS+) | CTs within 9 years after LTx | ANTs package | - Lung volume in inspiration and expiration - Lung volume difference between insp and expiration - Density-based quantification of air trapping (EXP<-856 HU, voxel volume with <75 HU increase on expiration) | Only 59% of qCT parameters associated with BOS+ BOS prediction model combining qCT and PFT parameters outperforms model based on PFTs alone in the unilateral LTx group |
Dettmer [17] | 2018 | Prospective study 51 patients (17 BOS+) | Last CT within 1 year before BOS diagnostic First CT within 1 year after BOS diagnostic | Mevis Pulmo | Density-based quantification of air trapping (E/I-MLD ratio, E/I Volumes, density percentiles) | Significant increase in E/I-Volumes and decrease in E/I-MLD in BOS+ Changes more pronounced in the lower lobes Highest AUC for 10th percentile on expiration (0.903) and E/I-MLD ratio (AUC: 0.886) |
Horie [18] | 2018 | Retrospective study 74 patients (23 RAS, 51 BOS) | CT performed ±4 months from CLAD and/or RAS/BOS onset. | Lung segmentation on Vitrea workstation | Lung volume and MLD on inspiration Quantitative density metrics (QDM) defined as ratios of the right and left quantile weights of the density histogram on inspiratory CT | Significant difference of Lung volume and MLD in BOS and RAS patients Hazard ratio for death 3.2 times higher at the 75th percentile of QDM1compared to the 25th percentile |
Saito [20] | 2016 | Retrospective study 63 patients (19 RAS, 44 BOS) | CT performed at baseline and time of CLAD onset | Lung segmentation on Vitrea workstation | Lung volume on inspiration | Decrease in CT lung volume in RAS patients CT volumetry < 90% baseline had an accuracy of 0.937 for differentiating RAS from BOS |
Saito [21] | 2018 | Retrospective study 58 patients, 14 CLAD | CT performed 3, 6, and 12 months after LTx and once yearly thereafter | Lung segmentation on Synapse Vincent workstation | Lung volume on inspiration and expiration Evaluation of Δlung volume over time (difference between inspiration and expiration) | Δlung volume onset/baseline significantly decreased in the CLAD group 0.80 cutoff had an AUC of 0.87 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Hoang-Thi, T.-N.; Chassagnon, G.; Hua-Huy, T.; Boussaud, V.; Dinh-Xuan, A.-T.; Revel, M.-P. Chronic Lung Allograft Dysfunction Post Lung Transplantation: A Review of Computed Tomography Quantitative Methods for Detection and Follow-Up. J. Clin. Med. 2021, 10, 1608. https://doi.org/10.3390/jcm10081608
Hoang-Thi T-N, Chassagnon G, Hua-Huy T, Boussaud V, Dinh-Xuan A-T, Revel M-P. Chronic Lung Allograft Dysfunction Post Lung Transplantation: A Review of Computed Tomography Quantitative Methods for Detection and Follow-Up. Journal of Clinical Medicine. 2021; 10(8):1608. https://doi.org/10.3390/jcm10081608
Chicago/Turabian StyleHoang-Thi, Trieu-Nghi, Guillaume Chassagnon, Thong Hua-Huy, Veronique Boussaud, Anh-Tuan Dinh-Xuan, and Marie-Pierre Revel. 2021. "Chronic Lung Allograft Dysfunction Post Lung Transplantation: A Review of Computed Tomography Quantitative Methods for Detection and Follow-Up" Journal of Clinical Medicine 10, no. 8: 1608. https://doi.org/10.3390/jcm10081608
APA StyleHoang-Thi, T.-N., Chassagnon, G., Hua-Huy, T., Boussaud, V., Dinh-Xuan, A.-T., & Revel, M.-P. (2021). Chronic Lung Allograft Dysfunction Post Lung Transplantation: A Review of Computed Tomography Quantitative Methods for Detection and Follow-Up. Journal of Clinical Medicine, 10(8), 1608. https://doi.org/10.3390/jcm10081608