Experimental and quantitative imaging techniques in interstitial lung disease
Open accessAbstract
Interstitial lung diseases (ILDs) are a heterogeneous group of conditions, with a wide and complex variety of imaging features. Difficulty in monitoring, treating and exploring novel therapies for these conditions is in part due to the lack of robust, readily available biomarkers. Radiological studies are vital in the assessment and follow-up of ILD, but currently CT analysis in clinical practice is qualitative and therefore somewhat subjective. In this article, we report on the role of novel and quantitative imaging techniques across a range of imaging modalities in ILD and consider how they may be applied in the assessment and understanding of ILD. We critically appraised evidence found from searches of Ovid online, PubMed and the TRIP database for novel and quantitative imaging studies in ILD. Recent studies have explored the capability of texture-based lung parenchymal analysis in accurately quantifying several ILD features. Newer techniques are helping to overcome the challenges inherent to such approaches, in particular distinguishing peripheral reticulation of lung parenchyma from pleura and accurately identifying the complex density patterns that accompany honeycombing. Robust and validated texture-based analysis may remove the subjectivity that is inherent to qualitative reporting and allow greater objective measurements of change over time. In addition to lung parenchymal feature quantification, pulmonary vessel volume analysis on CT has demonstrated prognostic value in two retrospective analyses and may be a sign of vascular changes in ILD which, to date, have been difficult to quantify in the absence of overt pulmonary hypertension. Novel applications of existing imaging techniques, such as hyperpolarised gas MRI and positron emission tomography (PET), show promise in combining structural and functional information. Although structural imaging of lung tissue is inherently challenging in terms of conventional proton MRI techniques, inroads are being made with ultrashort echo time, and dynamic contrast-enhanced MRI may be used for lung perfusion assessment. In addition, inhaled hyperpolarised 129 Xenon gas MRI may provide multifunctional imaging metrics, including assessment of ventilation, intra-acinar gas diffusion and alveolar-capillary diffusion. PET has demonstrated high standard uptake values (SUVs) of 18 F-fluorodeoxyglucose in fibrosed lung tissue, challenging the assumption that these are ‘burned out’ and metabolically inactive regions. Regions that appear structurally normal also appear to have higher SUV, warranting further exploration with future longitudinal studies to assess if this precedes future regions of macroscopic structural change. Given the subtleties involved in diagnosing, assessing and predicting future deterioration in many forms of ILD, multimodal quantitative lung structure-function imaging may provide the means of identifying novel, sensitive and clinically applicable imaging markers of disease. Such imaging metrics may provide mechanistic and phenotypic information that can help direct appropriate personalised therapy, can be used to predict outcomes and could potentially be more sensitive and specific than global pulmonary function testing. Quantitative assessment may objectively assess subtle change in character or extent of disease that can assist in efficacy of antifibrotic therapy or detecting early changes of potentially pneumotoxic drugs involved in early intervention studies.
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HP 129Xe MRI has shown potential for evaluating global and regional gas exchange, and when inhaled it dissolves into lung tissue and binds to red blood cells, allowing functional assessment of alveolar-capillary diffusion, ventilation and intra-acinar gas diffusion.
“Hyperpolarized (HP) xenon 129 ( 129 Xe) magnetic resonance imaging (MRI) has shown potential for evaluating global and regional gas exchange. When inhaled, HP 129 Xe has the property to dissolve into the lung tissue and bind to red blood cells (RBC). It can therefore be used as a probe for functional assessment of alveolar-capillary diffusion, ventilation and intra-acinar gas”
From Pulmonary fibrosis: Emerging diagnostic and therapeutic strategies (2023) · cites this paper as Weatherley et al., 2019
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Travis, W. D., Costabel, U., Hansell, D. M., King, T. E., Lynch, D. A., Nicholson, A. G., Ryerson, C. J., Ryu, J. H., Selman, M., Wells, A. U., et al. (2013) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.201308-1483st · Cited by 4,320 · see citing works
Interstitial Lung Disease: A Quantitative Study Using the Adaptive Multiple Feature Method
Uppaluri, R., Hoffman, E. A., Milan Sonka, A., Hunninghake, G. W., & McLennan, G. (1999) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/ajrccm.159.2.9707145 · Cited by 562 · see citing works
Theory of spin-exchange optical pumping of 3 He and 129 Xe
Appelt, S., Baranga, A. B.-A., Erickson, C., Romalis, M. V., Young, A. R., & Happer, W. (1998) · Physical Review A · doi:10.1103/physreva.58.1412 · Cited by 443 · see citing works
Interobserver Variability in the CT Assessment of Honeycombing in the Lungs
Watadani, T., Sakai, F., Johkoh, T., Noma, S., Akira, M., Fujimoto, K., Bankier, A. A., Lee, K. S., Müller, N. L., Song, J., et al. (2012) · Radiology · doi:10.1148/radiol.12112516 · Cited by 412 · see citing works
Computed Tomography Findings in Pathological Usual Interstitial Pneumonia
Sumikawa, H., Johkoh, T., Colby, T. V., Ichikado, K., Suga, M., Taniguchi, H., Kondoh, Y., Ogura, T., Arakawa, H., Fujimoto, K., et al. (2007) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.200611-1696oc · Cited by 354 · see citing works
Optimized 3D ultrashort echo time pulmonary MRI
Johnson, K. M., Fain, S. B., Schiebler, M. L., & Nagle, S. K. (2012) · Magnetic Resonance in Medicine · doi:10.1002/mrm.24570 · Cited by 322 · see citing works
Best, A. C., Meng, J., Lynch, A. M., Bozic, C. M., Miller, D. E., Grunwald, G. K., & Lynch, D. A. (2008) · Radiology · doi:10.1148/radiol.2463062200 · Cited by 322 · see citing works
Jacob, J., Bartholmai, B. J., Rajagopalan, S., Kokosi, M., Nair, A., Karwoski, R., Walsh, S. L. F., Wells, A. U., & Hansell, D. M. (2017) · European Respiratory Journal · doi:10.1183/13993003.01011-2016 · Cited by 309 · see citing works
Self-citationAutomated quantification of radiological patterns predicts survival in idiopathic pulmonary fibrosis
Maldonado, F., Moua, T., Rajagopalan, S., Karwoski, R. A., Raghunath, S. M., Decker, P. A., Hartman, T. E., Bartholmai, B. J., Robb, R. A., & Ryu, J. H. (2013) · European Respiratory Journal · doi:10.1183/09031936.00071812 · Cited by 264 · see citing works
Self-citationPredicting outcomes in idiopathic pulmonary fibrosis using automated computed tomographic analysis
Jacob, J., Bartholmai, B. J., Rajagopalan, S., van Moorsel, C. H. M., van Es, H. W., van Beek, F. T., Struik, M. H. L., Kokosi, M., Egashira, R., Brun, A. L., et al. (2018) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.201711-2174oc · Cited by 198 · see citing works
Self-citationHumphries, S. M., Yagihashi, K., Huckleberry, J., Rho, B.-H., Schroeder, J., Strand, M. J., Schwarz, M. I., Flaherty, K. R., Kazerooni, E. A., van Beek, E. J. R., et al. (2017) · Radiology · doi:10.1148/radiol.2017161177 · Cited by 190 · see citing works
Woodhouse, N., Wild, J. M., Paley, M. N. J., Fichele, S., Said, Z., Swift, A. J., & van Beek, E. J. R. (2005) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.20290 · Cited by 181 · see citing works
Self-citationQuantitative Computed Tomography Imaging of Interstitial Lung Diseases
Bartholmai, B. J., Raghunath, S. M., Karwoski, R. A., Moua, T., Rajagopalan, S., Maldonado, F., Decker, P. A., & Robb, R. A. (2013) · Journal of Thoracic Imaging · doi:10.1097/rti.0b013e3182a21969 · Cited by 179 · see citing works
Self-citationQuantitative CT Indexes in Idiopathic Pulmonary Fibrosis: Relationship with Physiologic Impairment
Best, A. C., Lynch, A. M., Bozic, C. M., et al. (2003) · Radiology · doi:10.1148/radiol.2282020274 · Cited by 179 · see citing works
CT-quantified emphysema in male heavy smokers: association with lung function decline
Hoesein, F. A. A. M., de Hoop, B., Zanen, P., Gietema, H. A., Kruitwagen, C. L. J. J., van Ginneken, B., Išgum, I., Mol, C. P., van Klaveren, R. J., Dijkstra, A., et al. (2011) · Thorax · doi:10.1136/thx.2010.145995 · Cited by 176 · see citing works
Désogère, P. A., Tapias, L. F., Hariri, L. P., Rotile, N. J., Rietz, T. A., Probst, C. K., Blasi, F., Day, H., Mino-Kenudson, M. A., Weinreb, P., et al. (2017) · Science Translational Medicine · doi:10.1126/scitranslmed.aaf4696 · Cited by 175 · see citing works
Using hyperpolarized 129 Xe MRI to quantify regional gas transfer in idiopathic pulmonary fibrosis
Wang, J. M., Robertson, S. H., Wang, Z., et al. (2017) · Thorax · doi:10.1136/thoraxjnl-2017-210070 · Cited by 162 · see citing works
Ohno, Y., Koyama, H., Yoshikawa, T., et al. (2015) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.25008 · Cited by 159 · see citing works
Groves, A. M., Win, T., Screaton, N. J., Berovic, M., Endozo, R., Booth, H., Kayani, I. A., Menezes, L., Dickson, J. C., & Ell, P. J. (2009) · Journal of Nuclear Medicine · doi:10.2967/jnumed.108.057901 · Cited by 158 · see citing works
Xu, Y., van Beek, E. J. R., Yu, H., et al. (2006) · Academic Radiology · doi:10.1016/j.acra.2006.04.017 · Cited by 153 · see citing works
High-resolution CT-derived measures of lung density are valid indexes of interstitial lung disease
Hartley, P. G., Galvin, J. R., Hunninghake, G. W., et al. (1994) · Journal of Applied Physiology · doi:10.1152/jappl.1994.76.1.271 · Cited by 151 · see citing works
Salisbury, M. L., Lynch, D. A., van Beek, E. J. R., Kazerooni, E. A., Guo, J., Xia, M., Murray, S., Anstrom, K. J., Yow, E. S., Martínez, F. J., et al. (2016) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.201607-1385oc · Cited by 137 · see citing works
Kim, H. J., Brown, M. S., Chong, D. Y., Gjertson, D. W., Lu, P., Kim, H. J., Coy, H., & Goldin, J. G. (2014) · Academic Radiology · doi:10.1016/j.acra.2014.08.004 · Cited by 129 · see citing works
Kaushik, S. S., Robertson, S. H., Freeman, M. S., et al. (2015) · Magnetic Resonance in Medicine · doi:10.1002/mrm.25675 · Cited by 128 · see citing works
Automated segmentation of lungs with severe interstitial lung disease in CT
Wang, J., Li, F., & Li, Q. (2009) · Medical Physics · doi:10.1118/1.3222872 · Cited by 125 · see citing works
Severity of pulmonary involvement and 18F-FDG PET activity in sarcoidosis
Mostard, R. L. M., Verschakelen, J., van Kroonenburgh, M. J. P. G., et al. (2012) · Respiratory Medicine · doi:10.1016/j.rmed.2012.11.011 · Cited by 117 · see citing works
Progression parameters for emphysema: A clinical investigation
Stolk, J. N., Putter, H., Bakker, E. M., et al. (2007) · Respiratory Medicine · doi:10.1016/j.rmed.2007.04.016 · Cited by 116 · see citing works
Non-invasive Imaging of Idiopathic Pulmonary Fibrosis Using Cathepsin Protease Probes
Withana, N. P., Ma, X., McGuire, H. M., Verdoes, M., van der Linden, W. A., Ofori, L. O., Zhang, R., Li, H., Sanman, L. E., Wei, K., et al. (2016) · Scientific Reports · doi:10.1038/srep19755 · Cited by 113 · see citing works
Molecular Magnetic Resonance Imaging of Pulmonary Fibrosis in Mice
Caravan, P. D., Yang, Y., Zachariah, R., Schmitt, A. D., Mino-Kenudson, M. A., Chen, H. H., Sosnovik, D. E., Dai, G., Fuchs, B. C., & Lanuti, M. D. (2013) · American Journal of Respiratory Cell and Molecular Biology · doi:10.1165/rcmb.2013-0039oc · Cited by 110 · see citing works
Ash, S. Y., Harmouche, R., Lassala, D., et al. (2017) · Respiratory Research · doi:10.1186/s12931-017-0527-8 · Cited by 110 · see citing works
Idiopathic Pulmonary Fibrosis: CT and Risk of Death
Ley, B., Elicker, B. M., Hartman, T. E., Ryerson, C. J., Vittinghoff, E., Ryu, J. H., Lee, J. S., Jones, K. D., Richeldi, L., King, T. E., et al. (2014) · Radiology · doi:10.1148/radiol.14130216 · Cited by 107 · see citing works
Kaushik, S. S., Freeman, M. S., Yoon, P., Liljeroth, M., Stiles, J. V., Roos, J. E., Foster, W. S. M., Rackley, C. R., McAdams, H. P., & Driehuys, B. (2014) · Journal of Applied Physiology · doi:10.1152/japplphysiol.00326.2014 · Cited by 105 · see citing works
Stewart, N. J., Leung, G., Norquay, G., Marshall, H., Parra‐Robles, J., Murphy, P. S., Schulte, R. F., Elliot, C. A., Condliffe, R., Griffiths, P. D., et al. (2014) · Magnetic Resonance in Medicine · doi:10.1002/mrm.25400 · Cited by 95 · see citing works
Self-citationJacob, J., Bartholmai, B. J., Rajagopalan, S., Brun, A., Egashira, R., Karwoski, R. A., Kokosi, M. A., Wells, A. U., & Hansell, D. M. (2016) · BMC Medicine · doi:10.1186/s12916-016-0739-7 · Cited by 94 · see citing works
Self-citationQuantitative analysis of hyperpolarized 129 Xe gas transfer MRI
Wang, Z., Robertson, S. H., Wang, J. P., et al. (2017) · Medical Physics · doi:10.1002/mp.12264 · Cited by 94 · see citing works
Areas of normal pulmonary parenchyma on HRCT exhibit increased FDG PET signal in IPF patients
Win, T., Thomas, B. A., Lambrou, T., Hutton, B. F., Screaton, N. J., Porter, J. C., Maher, T. M., Endozo, R., Shortman, R. I., Afaq, A., et al. (2013) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-013-2514-8 · Cited by 93 · see citing works
Pulmonary Diffusing Capacity: Assessment with Oxygen-enhanced Lung MR Imaging—Preliminary Findings
Müller, C., Schwaiblmair, M., Scheidler, J. E., Deimling, M., Weber, J., Löffler, R., & Reiser, M. F. (2002) · Radiology · doi:10.1148/radiol.2222000869 · Cited by 91 · see citing works
Yi, C. A., Lee, K. S., Han, J., et al. (2008) · American Journal of Roentgenology · doi:10.2214/ajr.07.2833 · Cited by 86 · see citing works
CT Evaluation of Diffuse Infiltrative Lung Disease
Mayo, J. R. (2009) · Journal of Thoracic Imaging · doi:10.1097/rti.0b013e3181c227b2 · Cited by 82 · see citing works
Korfiatis, P. D., Karahaliou, A. N., Kazantzi, A., et al. (2009) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2009.2036166 · Cited by 82 · see citing works
CT analysis of the effect of pirfenidone in patients with idiopathic pulmonary fibrosis
Iwasawa, T., Ogura, T., Sakai, F., Kanauchi, T., Komagata, T., Baba, T., Gotoh, T., Morita, S., Yazawa, T., & Inoue, T. (2012) · European Journal of Radiology · doi:10.1016/j.ejrad.2012.02.014 · Cited by 82 · see citing works
Win, T., Screaton, N. J., Porter, J. C., et al. (2018) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-017-3917-8 · Cited by 79 · see citing works
Hyperpolarized 129Xe Magnetic Resonance Imaging
Shukla, Y., Wheatley, A., Kirby, M., et al. (2012) · Academic Radiology · doi:10.1016/j.acra.2012.03.018 · Cited by 77 · see citing works
Rosas, I. O., Yao, J., Avila, N. A., Chow, C. K., Gahl, W. A., & Gochuico, B. R. (2011) · CHEST Journal · doi:10.1378/chest.10-2545 · Cited by 73 · see citing works
Marinelli, J. P., Levin, D. L., Vassallo, R., Carter, R. E., Hubmayr, R. D., Ehman, R. L., & McGee, K. P. (2017) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.25579 · Cited by 68 · see citing works
Delorme, S., Keller-Reichenbecher, M.-A., ZUNA, I., et al. (1997) · Investigative Radiology · doi:10.1097/00004424-199709000-00009 · Cited by 66 · see citing works
Weatherley, N., Stewart, N. J., Chan, H., Austin, M., Smith, L., Collier, G. J., Rao, M. R., Marshall, H., Norquay, G., Renshaw, S. A., et al. (2018) · Thorax · doi:10.1136/thoraxjnl-2018-211851 · Cited by 65 · see citing works
Self-citationPrognostic Value of Dual-Time-Point 18 F-FDG PET for Idiopathic Pulmonary Fibrosis
Umeda, Y., Demura, Y., Morikawa, M., Anzai, M., Kadowaki, M., Ameshima, S., Tsuchida, T., Tsujikawa, T., Kiyono, Y., Okazawa, H., et al. (2015) · Journal of Nuclear Medicine · doi:10.2967/jnumed.115.163360 · Cited by 62 · see citing works
Umeda, Y., Demura, Y., Ishizaki, T., et al. (2009) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-009-1069-1 · Cited by 61 · see citing works
Stadler, A., Jakob, P. M., Griswold, M. A., Stiebellehner, L., Barth, M., & Bankier, A. A. (2007) · Magnetic Resonance in Medicine · doi:10.1002/mrm.21446 · Cited by 61 · see citing works
Win, T., Lambrou, T., Hutton, B. F., Kayani, I. A., Screaton, N. J., Porter, J. C., Maher, T. M., Endozo, R., Shortman, R. I., Lukey, P. T., et al. (2011) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-011-1986-7 · Cited by 57 · see citing works
Lung Morphology Assessment with Balanced Steady-State Free Precession MR Imaging Compared with CT
Rajaram, S., Swift, A. J., Capener, D. A., et al. (2012) · Radiology · doi:10.1148/radiol.12110990 · Cited by 57 · see citing works
Self-citationBarreto, M. M., Rafful, P. P., Rodrigues, R. S., et al. (2013) · European Journal of Radiology · doi:10.1016/j.ejrad.2013.04.037 · Cited by 55 · see citing works
Reproducibility of spirometrically controlled CT lung densitometry in a clinical setting
Lamers, R. J. S., Kemerink, G., Drent, M., et al. (1998) · European Respiratory Journal · doi:10.1183/09031936.98.11040942 · Cited by 55 · see citing works
Pinal‐Fernandez, I., Pineda, V., Pallisa-Nuñez, E., et al. (2016) · Clinical Rheumatology · doi:10.1007/s10067-016-3267-0 · Cited by 50 · see citing works
Colombi, D., Dinkel, J., Weinheimer, O., et al. (2015) · PLoS ONE · doi:10.1371/journal.pone.0130653 · Cited by 48 · see citing works
Chong, D. Y., Kim, H. J., Lo, P., Young, S., McNitt‐Gray, M. F., Abtin, F. G., Goldin, J. G., & Brown, M. S. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2015.2459064 · Cited by 48 · see citing works
Sverzellati, N., Calabrò, E., Chetta, A. A., et al. (2007) · La radiologia medica · doi:10.1007/s11547-007-0213-x · Cited by 47 · see citing works
SUV as an adjunct in evaluating disease activity in idiopathic pulmonary fibrosis – a pilot study
Lee, E. Y. P., Wong, C. S., Fung, S.-L., et al. (2014) · Nuclear Medicine Communications · doi:10.1097/mnm.0000000000000083 · Cited by 46 · see citing works
Tateishi, U., Hasegawa, T., Seki, K., et al. (2006) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-006-0073-y · Cited by 44 · see citing works
Sergiacomi, G., Bolacchi, F., Cadioli, M., et al. (2010) · Radiology · doi:10.1148/radiol.09081546 · Cited by 43 · see citing works
Measuring Extracellular pH in a Lung Fibrosis Model with acidoCEST MRI
Jones, K. M., Randtke, E. A., Howison, C. M., et al. (2014) · Molecular Imaging and Biology · doi:10.1007/s11307-014-0784-6 · Cited by 42 · see citing works
Boehm, H. F., Fink, C., Attenberger, U. I., et al. (2008) · European Radiology · doi:10.1007/s00330-008-1082-y · Cited by 42 · see citing works
Jacquelin, V., MEKINIAN, A., Brillet, P., et al. (2016) · European Journal of Radiology · doi:10.1016/j.ejrad.2016.10.001 · Cited by 42 · see citing works
T2 mapping of CT remodelling patterns in interstitial lung disease
Wetscherek, M. T., Eichinger, M., Kreuter, M., Kauczor, H., Herth, F. J. F., Warth, A., Pop, C. M., Heußel, C. P., & Dinkel, J. (2015) · European Radiology · doi:10.1007/s00330-015-3751-y · Cited by 41 · see citing works
Ohno, Y., Nishio, M., Koyama, H., Takenaka, D., Takahashi, M., Yoshikawa, T., Matsumoto, S., Obara, M., CAUTEREN, M. V., & Sugimura, K. (2013) · European Journal of Radiology · doi:10.1016/j.ejrad.2013.02.031 · Cited by 41 · see citing works
Gould, G. A., Redpath, A. T., Ryan, M. I., et al. (1993) · CHEST Journal · doi:10.1016/s0012-3692(16)47462-3 · Cited by 40 · see citing works
Gould, G., Redpath, A., Ryan, M., et al. (1993) · European Respiratory Journal · doi:10.1183/09031936.93.06050698 · Cited by 39 · see citing works
Molinari, F., Eichinger, M., Risse, F., et al. (2007) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.21043 · Cited by 37 · see citing works
Sverzellati, N., Zompatori, M., Luca, G. D., Chetta, A. A., Bnà, C., Ormitti, F., & Cobelli, R. (2005) · European Journal of Radiology · doi:10.1016/j.ejrad.2005.05.012 · Cited by 37 · see citing works
Tanizawa, K., Handa, T., Nagai, S., et al. (2015) · BMC Pulmonary Medicine · doi:10.1186/s12890-015-0069-0 · Cited by 37 · see citing works
Ohno, Y., Nishio, M., Koyama, H., Yoshikawa, T., Matsumoto, S., Seki, S., Tsubakimoto, M., & Sugimura, K. (2013) · European Journal of Radiology · doi:10.1016/j.ejrad.2013.11.001 · Cited by 35 · see citing works
Uehara, T., Takeno, M., Hama, M., et al. (2015) · Modern Rheumatology · doi:10.3109/14397595.2015.1054099 · Cited by 34 · see citing works
Lavelle, L. P., Brady, D., McEvoy, S. H., et al. (2017) · Diagnostic and Interventional Radiology · doi:10.5152/dir.2016.15331 · Cited by 33 · see citing works
Marinelli, J. P., Levin, D. L., Vassallo, R., Carter, R. E., Hubmayr, R. D., Ehman, R. L., & McGee, K. P. (2017) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.25805 · Cited by 33 · see citing works
Kanski, M., Arheden, H., Wuttge, D. M., et al. (2013) · Journal of Cardiovascular Magnetic Resonance · doi:10.1186/1532-429x-15-86 · Cited by 32 · see citing works
Quantitative Stratification of Diffuse Parenchymal Lung Diseases
Raghunath, S. M., Rajagopalan, S., Karwoski, R. A., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0093229 · Cited by 30 · see citing works
Nonspecific Interstitial Pneumonia Versus Usual Interstitial Pneumonia
Do, K., Lee, J. S., Colby, T. V., et al. (2005) · Journal of Computer Assisted Tomography · doi:10.1097/01.rct.0000164255.43859.96 · Cited by 27 · see citing works
Win, T., Screaton, N. J., Porter, J. C., et al. (2012) · Molecular Imaging · doi:10.2310/7290.2011.00030 · Cited by 27 · see citing works
Koyama, H., Ohno, Y., Yamazaki, Y., Nogami, M., Kusaka, A., Murase, K., & Sugimura, K. (2009) · European Journal of Radiology · doi:10.1016/j.ejrad.2009.02.006 · Cited by 27 · see citing works
Bartholmai, B. J., Karwoski, R. A., Zavaletta, V. A., Robb, R. A., & Holmes, D. (2006) · The Insight Journal · doi:10.54294/hzdcno · Cited by 27 · see citing works
Self-citationBink, A., Hanisch, G., Karg, A., et al. (2007) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.20933 · Cited by 26 · see citing works
Mirsadraee, S., Tse, M., Kershaw, L. E., Semple, S. I. K., Schembri, N., Chin, C., Murchison, J. T., Hirani, N., & van Beek, E. J. R. (2016) · PubMed Central · doi:10.3978/j.issn.2223-4292.2016.02.02 · Cited by 26 · see citing works
Iwasawa, T., Kanauchi, T., Hoshi, T., et al. (2015) · Japanese Journal of Radiology · doi:10.1007/s11604-015-0496-0 · Cited by 24 · see citing works
Dahhan, T., Kaushik, S. S., He, M., et al. (2016) · Pulmonary Circulation · doi:10.1086/685110 · Cited by 24 · see citing works
Wetscherek, M. T., Wetscherek, A., Heußel, C. P., et al. (2017) · PLoS ONE · doi:10.1371/journal.pone.0177689 · Cited by 15 · see citing works
Bondue, B., Sherer, F., Simaeys, G. V., et al. (2015) · PubMed · doi:10.2967/jnumed.114.147421 · Cited by 6 · see citing works
Tsuchiya, N., 鮎川, 雄., & Murayama, S. (2012) · Japanese Journal of Radiology · doi:10.1007/s11604-012-0171-7 · Cited by 5 · see citing works
Matin, T., Xu, X., Doel, T., et al. (2014) · Thorax · doi:10.1136/thoraxjnl-2014-206260.25 · Cited by 1 · see citing works
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- [25]Asakura AGT, Iwasawa T Classification system of the CT images with nonspecific interstitial pneumonia. Journal of the Institute of Image Electronics Engineers of Japan 2004; 33: 180– 8.
- [30]Maldonado F, Raghunath S, Aubry MC, et al Validation of CALIPER (Computer-aided lung informatics for pathology evaluation and rating) for the non-invasive assessment of pulmonary nodules of the adenocarcinoma spectrum. European Respiratory Journal 2012; 40.
- [59]Appelt S, Ben-Amar Baranga A, Erickson CJ, et al Theory of spin-exchange optical pumping of 3He and 129Xe. Physical Review A 1998; 58: 1412– 39.
Cited by
Sorted by citations
Pulmonary fibrosis: Emerging diagnostic and therapeutic strategies
Selvarajah, B., Platé, M., & Chambers, R. C. (2023) · Molecular Aspects of Medicine · doi:10.1016/j.mam.2023.101227 · Cited by 57 · see citing works
Tibiletti, M., Eaden, J. A., Naish, J. H., Hughes, P. J. C., Waterton, J. C., Heaton, M. J., Chaudhuri, N., Skeoch, S., Bruce, I. N., Bianchi, S., et al. (2023) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2022.10.005 · Cited by 13 · see citing works
Self-citation
- Authors
- Weatherley, N. D., Eaden, J. A., Stewart, N. J., Bartholmai, B. J., Swift, A. J., Bianchi, S. M., & Wild, J. M.
- Affiliations
- University of Sheffield (GB)
- Mayo Clinic (US)
- Sheffield Teaching Hospitals NHS Foundation Trust (GB)
- Corresponding author
- Wild, Jim M
- ORCID
- Venue
- Thorax
- Publisher
- BMJ
- Published
- 2019-03-18
- Volume
- 74
- Issue
- 6
- Pages
- 611-619
- ISSN
- 0040-6376 (ISSN-L)
- 1468-3296
- PMC
- PMC6585263
- OpenAlex
- W2922500394
- Cited by
- 87
- References
- 89
- Citations by year
- 2026: 7 · 2025: 8 · 2024: 10 · 2023: 13 · 2022: 13 · 2021: 23 · 2020: 10 · 2019: 2
- FWCI
- 2.02
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Thorax (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (acceptedVersion)
- HighWire Press Open Archive (submittedVersion)
- Funders
- European Union Horizon 2020 Research and Innovation Programme
- Topics
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Interstitial Lung Diseases and Idiopathic Pulmonary Fibrosis
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Medical Imaging and Pathology Studies
- Keywords
- interstitial lung disease, quantitative imaging, texture analysis, lung parenchyma, honeycombing, pulmonary blood flow, 18F-FDG PET, pulmonary fibrosis, biomarkers, pulmonary hypertension, alveolar-capillary diffusion
- MeSH terms
- Contrast Media, Diagnosis, Differential, Humans, Lung Diseases, Interstitial
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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