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Review
. 2023 Apr 28:10:1174443.
doi: 10.3389/fmed.2023.1174443. eCollection 2023.

Novel diagnostic techniques in interstitial lung disease

Laura M Glenn  1   2   3 , Lauren K Troy  1   2   3 , Tamera J Corte  1   2   3
Affiliations
Review

Novel diagnostic techniques in interstitial lung disease

Laura M Glenn et al. Front Med (Lausanne). .

Abstract

Research into novel diagnostic techniques and targeted therapeutics in interstitial lung disease (ILD) is moving the field toward increased precision and improved patient outcomes. An array of molecular techniques, machine learning approaches and other innovative methods including electronic nose technology and endobronchial optical coherence tomography are promising tools with potential to increase diagnostic accuracy. This review provides a comprehensive overview of the current evidence regarding evolving diagnostic methods in ILD and to consider their future role in routine clinical care.

Keywords: biomarker; diagnostic techniques; electronic nose; genomics; interstitial lung disease; machine learning; optical coherence tomography.

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Conflict of interest statement

LG has received travel and conference support from Boehringer Ingelheim. LT has provided paid consultancy for Erbe Elektromedezin and Boehringer Ingelheim. TC has received grant support, consultancy fees, and speaking honoraria from Boehringer Ingelheim and Hoffman-La Roche, consultancy fees from Bristol Myers Squibb; grant support from Biogen, and provides consultancy for DevPro and Ad Alta.

Figures

Figure 1
Figure 1
Molecular mechanisms underlying fibrotic lung injury and potential serum biomarkers of ILD. Abbreviations: KL-6 Krebs von lungen 6; SP-A/SP-D surfactant protein A/D; cCK18 cleaved cytokeratin 18; CC16 Clara cell protein 16; MMP matrix metalloproteinase; OPN osteopontin, HSP47 heat-shock protein 47; sLOXL2 serum lysyl oxidase-like 2; ADAM a disintegrin and metalloprotease; CCL18 chemokine ligand 18; ICAM intercellular adhesion molecule; VCAM vascular cell adhesion molecule; CXCL13 C-X-C motif chemokine 13; CCL2 CC chemokine 2; IL-6 interleukin-6. Reprinted from Pharmacology and Therapeutics, 202, Jee et al. (56), with permission from Elsevier.
Figure 2
Figure 2
Four slice montage generated from a patient with an assigned HRCT diagnosis of probable UIP based on two expert thoracic radiologists, accompanied by a saliency map depicting parts of the lungs most influential in SOFIA-based decision-making. SOFIA probabilities for this montage were UIP: 0.224, probable UIP: 0.764, indeterminate for UIP 0.012, alternative diagnosis: 0.000. The SOFIA deep-learning algorithm generates up to 500 four axial slice montages from an HRCT scan by segmenting the lungs into quarters (excluding the apical 10%) and then randomly selecting slices from each quarter. The algorithm generates a set of numbers from 0 to 1, totalling 1.0, representing the probability of each of the UIP diagnosis categories (definite UIP, probable UIP, indeterminate, alternative diagnosis); which is the average probability for each diagnostic category across all the montages generated for each HRCT (85). Sourced and reproduced with permission from Simon L. F. Walsh, M.D., F.F.R.R.C.S.I., National Heart and Lung Institute, Imperical College London, London, United Kingdom.
See this image and copyright information in PMC

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