Quantitative computed tomography predicts outcomes in idiopathic pulmonary fibrosis

Correlation between CT-based phenotypes and serum biomarker in interstitial lung diseases

BACKGROUND

The quantitative analysis of computed tomography (CT) and Krebs von den Lungen-6 (KL-6) serum level has gained importance in the diagnosis, monitoring, and prognostication of interstitial lung disease (ILD). However, the associations between quantitative analysis of CT and serum KL-6 level remain poorly understood.

METHODS

In this retrospective observational study conducted at tertiary hospital between June 2020 and March 2022, quantitative analysis of CT was performed using the deep learning-based method including reticulation, ground glass opacity (GGO), honeycombing, and consolidation. We investigated the associations between CT-based phenotypes and serum KL-6 measured within three months of the CT scan. Furthermore, we evaluated the performance of the combined CT-based phenotypes and KL-6 levels in predicting hospitalizations due to respiratory reasons of ILD patients.

RESULTS

A total of 131 ILD patients (104 males) with a median age of 67 years were included in this study. Reticulation, GGO, honeycombing, and consolidation extents showed a positive correlation with KL-6 levels. [Reticulation, correlation coefficient (r) = 0.567, p < 0.001; GGO, r = 0.355, p < 0.001; honeycombing, r = 0.174, p = 0.046; and consolidation, r = 0.446, p < 0.001]. Additionally, the area under the ROC of the combined reticulation and KL-6 for hospitalizations due to respiratory reasons was 0.810 (p < 0.001).

CONCLUSIONS

Quantitative analysis of CT features and serum KL-6 levels ascertained a positive correlation between the two. In addition, the combination of reticulation and KL-6 shows potential for predicting hospitalizations of ILD patients due to respiratory causes. The combination of reticulation, focusing on phenotypic change in lung parenchyma, and KL-6, as an indicator of lung injury extent, could be helpful for monitoring and predicting the prognosis of various types of ILD.

Progress in the radiologic diagnosis of idiopathic pulmonary fibrosis

PURPOSE OF REVIEW

To discuss the most recent applications of radiological imaging, from conventional to quantitative, in the setting of idiopathic pulmonary fibrosis (IPF) diagnosis.

RECENT FINDINGS

In this article, current concepts on radiological diagnosis of IPF, from high-resolution computed tomography (CT) to other imaging modalities, are reviewed. In a separate section, advances in quantitative CT and development of novel imaging biomarkers, as well as current limitations and future research trends, are described.

SUMMARY

Radiological imaging in IPF, particularly quantitative CT, is an evolving field which holds promise in the future to allow for an increasingly accurate disease assessment and prognostication of IPF patients. However, further standardization and validation studies of alternative imaging applications and quantitative biomarkers are needed.

From pixels to prognosis: unlocking the potential of deep learning in fibrotic lung disease imaging analysis

The licensing of antifibrotic therapy for fibrotic lung diseases, including idiopathic pulmonary fibrosis (IPF), has created an urgent need for reliable biomarkers to predict disease progression and treatment response. Some patients experience stable disease trajectories, while others deteriorate rapidly, making treatment decisions challenging. High-resolution chest CT has become crucial for diagnosis, but visual assessments by radiologists suffer from low reproducibility and high interobserver variability. To address these issues, computer-based image analysis, called quantitative CT, has emerged. However, many quantitative CT methods rely on human input for training, therefore potentially incorporating human error into computer training. Rapid advances in artificial intelligence, specifically deep learning, aim to overcome this limitation by enabling autonomous quantitative analysis. While promising, deep learning also presents challenges including the need to minimize algorithm biases, ensuring explainability, and addressing accessibility and ethical concerns. This review explores the development and application of deep learning in improving the imaging process for fibrotic lung disease.

Advancements in imaging in ChILD

Interstitial and diffuse lung diseases in children constitute a range of congenital and acquired disorders. These disorders present with signs and symptoms of respiratory disease accompanied by diffuse radiographic changes. In many cases, radiographic findings are nonspecific, while in other disorders, chest computed tomography (CT) is diagnostic in the appropriate context. Regardless, chest imaging remains central in the evaluation of the patient with suspected childhood interstitial lung disease (chILD). Several newly described chILD entities, spanning both genetic and acquired etiologies, have imaging that aid in their diagnoses. Advances in CT scanning technology and CT analysis techniques continue to improve scan quality as well as expand use of chest CT as a research tool. Finally, ongoing research is expanding use of imaging modalities without ionizing radiation. Magnetic resonance imaging is being applied to investigate pulmonary structure and function, and ultrasound of the lung and pleura is a novel technique with an emerging role in chILD disorders. This review describes the current state of imaging in chILD including recently described diagnoses, advances in conventional imaging techniques and applications, and evolving new imaging modalities that expand the clinical and research roles for imaging in these disorders.

CT Quantification of Interstitial Lung Abnormality and Interstitial Lung Disease: From Technical Challenges to Future Directions

ABSTRACT

Interstitial lung disease (ILD) encompasses a variety of lung disorders with varying degrees of inflammation or fibrosis, requiring a combination of clinical, imaging, and pathologic data for evaluation. Imaging is essential for the noninvasive diagnosis of the disease, as well as for assessing disease severity, monitoring its progression, and evaluating treatment response. However, traditional visual assessments of ILD with computed tomography (CT) suffer from reader variability. Automated quantitative CT offers a more objective approach by using computer-based analysis to consistently evaluate and measure ILD. Advancements in technology have significantly improved the accuracy and reliability of these measurements. Recently, interstitial lung abnormalities (ILAs), which represent potential preclinical ILD incidentally found on CT scans and are characterized by abnormalities in over 5% of any lung zone, have gained attention and clinical importance. The challenge lies in the accurate and consistent identification of ILA, given that its definition relies on a subjective threshold, making quantitative tools crucial for precise ILA evaluation. This review highlights the state of CT quantification of ILD and ILA, addressing clinical and research disparities while emphasizing how machine learning or deep learning in quantitative imaging can improve diagnosis and management by providing more accurate assessments, and finally, suggests the future directions of quantitative CT in this area.

Deep Learning Classification of Usual Interstitial Pneumonia Predicts Outcomes

Rationale: Computed tomography (CT) enables noninvasive diagnosis of usual interstitial pneumonia (UIP), but enhanced image analyses are needed to overcome the limitations of visual assessment. Objectives: Apply multiple instance learning (MIL) to develop an explainable deep learning algorithm for prediction of UIP from CT and validate its performance in independent cohorts. Methods: We trained an MIL algorithm using a pooled dataset (n = 2,143) and tested it in three independent populations: data from a prior publication (n = 127), a single-institution clinical cohort (n = 239), and a national registry of patients with pulmonary fibrosis (n = 979). We tested UIP classification performance using receiver operating characteristic analysis, with histologic UIP as ground truth. Cox proportional hazards and linear mixed-effects models were used to examine associations between MIL predictions and survival or longitudinal FVC. Measurements and Main Results: In two cohorts with biopsy data, MIL improved accuracy for histologic UIP (area under the curve, 0.77 [n = 127] and 0.79 [n = 239]) compared with visual assessment (area under the curve, 0.65 and 0.71). In cohorts with survival data, MIL-UIP classifications were significant for mortality (n = 239, mortality to April 2021: unadjusted hazard ratio, 3.1; 95% confidence interval [CI], 1.96-4.91; P < 0.001; and n = 979, mortality to July 2022: unadjusted hazard ratio, 3.64; 95% CI, 2.66-4.97; P < 0.001). Individuals classified as UIP positive by the algorithm had a significantly greater annual decline in FVC than those classified as UIP negative (-88 ml/yr vs. -45 ml/yr; n = 979; P < 0.01), adjusting for extent of lung fibrosis. Conclusions: Computerized assessment using MIL identifies clinically significant features of UIP on CT. Such a method could improve confidence in radiologic assessment of patients with interstitial lung disease, potentially enabling earlier and more precise diagnosis.

Proteomic Biomarkers of Survival in Idiopathic Pulmonary Fibrosis

Rationale: Idiopathic pulmonary fibrosis (IPF) causes progressive lung scarring and high mortality. Reliable and accurate prognostic biomarkers are urgently needed. Objectives: To identify and validate circulating protein biomarkers of IPF survival. Methods: High-throughput proteomic data were generated using prospectively collected plasma samples from patients with IPF from the Pulmonary Fibrosis Foundation Patient Registry (discovery cohort) and the Universities of California, Davis; Chicago; and Virginia (validation cohort). Proteins associated with three-year transplant-free survival (TFS) were identified using multivariable Cox proportional hazards regression. Those associated with TFS after adjustment for false discovery in the discovery cohort were advanced for testing in the validation cohort, with proteins maintaining TFS association with consistent effect direction considered validated. After combining cohorts, functional analyses were performed, and machine learning was used to derive a proteomic signature of TFS. Measurements and Main Results: Of 2,921 proteins tested in the discovery cohort (n = 871), 231 were associated with differential TFS. Of these, 140 maintained TFS association with consistent effect direction in the validation cohort (n = 355). After cohorts were combined, the validated proteins with the strongest TFS association were latent-transforming growth factor β-binding protein 2 (hazard ratio [HR], 2.43; 95% confidence interval [CI] = 2.09-2.82), collagen α-1(XXIV) chain (HR, 2.21; 95% CI = 1.86-2.39), and keratin 19 (HR, 1.60; 95% CI = 1.47-1.74). In decision curve analysis, a proteomic signature of TFS outperformed a similarly derived clinical prediction model. Conclusions: In the largest proteomic investigation of IPF outcomes performed to date, we identified and validated 140 protein biomarkers of TFS. These results shed important light on potential drivers of IPF progression.

Progressive Pulmonary Fibrosis and Interstitial Lung Abnormalities: AJR Expert Panel Narrative Review

Progressive pulmonary fibrosis (PPF) and interstitial lung abnormalities (ILA) are relatively new concepts in interstitial lung disease (ILD) imaging and clinical management. Recognition of signs of PPF, as well as identification and classification of ILA, are important tasks during chest high-resolution CT interpretation, to optimize management of patients with ILD and those at risk of developing ILD. However, following professional society guidance, the role of imaging surveillance remains unclear in stable patients with ILD, asymptomatic patients with ILA who are at risk of progression, and asymptomatic patients at risk of developing ILD without imaging abnormalities. In this AJR Expert Panel Narrative Review, we summarize the current knowledge regarding PPF and ILA and describe the range of clinical practice with respect to imaging patients with ILD, those with ILA, and those at risk of developing ILD. In addition, we offer suggestions to help guide surveillance imaging in areas with an absence of published guidelines, where such decisions are currently driven primarily by local pulmonologists' preference.

Meaningful Endpoints for Idiopathic Pulmonary Fibrosis (IPF) Clinical Trials: Emphasis on 'Feels, Functions, Survives'. Report of a Collaborative Discussion in a Symposium with Direct Engagement from Representatives of Patients, Investigators, the National Institutes of Health, a Patient Advocacy Organization, and a Regulatory Agency

Background: Idiopathic pulmonary fibrosis (IPF) carries significant mortality and unpredictable progression, with limited therapeutic options. Designing trials with patient-meaningful endpoints, enhancing the reliability and interpretability of results, and streamlining the regulatory approval process are of critical importance to advancing clinical care in IPF. Methods: A landmark in-person symposium in June 2023 assembled 43 participants from the US and internationally, including patients with IPF, investigators, and regulatory representatives, to discuss the immediate future of IPF clinical trial endpoints. Patient advocates were central to discussions, which evaluated endpoints according to regulatory standards and the FDA's 'feels, functions, survives' criteria. Results: Three themes emerged: 1) consensus on endpoints mirroring the lived experiences of patients with IPF; 2) consideration of replacing forced vital capacity (FVC) as the primary endpoint, potentially by composite endpoints that include 'feels, functions, survives' measures or FVC as components; 3) support for simplified, user-friendly patient-reported outcomes (PROs) as either components of primary composite endpoints or key secondary endpoints, supplemented by functional tests as secondary endpoints and novel biomarkers as supportive measures (FDA Guidance for Industry (Multiple Endpoints in Clinical Trials) available at: https://www.fda.gov/media/162416/download). Conclusions: This report, detailing the proceedings of this pivotal symposium, suggests a potential turning point in designing future IPF clinical trials more attuned to outcomes meaningful to patients, and documents the collective agreement across multidisciplinary stakeholders on the importance of anchoring IPF trial endpoints on real patient experiences-namely, how they feel, function, and survive. There is considerable optimism that clinical care in IPF will progress through trials focused on patient-centric insights, ultimately guiding transformative treatment strategies to enhance patients' quality of life and survival.

Radiological usual interstitial pneumonia pattern is associated with two-year mortality in patients with idiopathic pulmonary fibrosis

Introduction

The new diagnostic guidelines for idiopathic pulmonary fibrosis (IPF) did not rule out the possibility of combining the radiological patterns of usual interstitial pneumonia (UIP) and probable UIP, given the similar management and diagnostic capacity. However, the prognostic implications of these patterns have not been fully elucidated, with different studies showing heterogeneous results. We applied the new criteria to a retrospective series of patients with IPF, assessing survival based on radiological patterns, findings, and their extension.

Methods

Two thoracic radiologists reviewed high-resolution computed tomography images taken at diagnosis in 146 patients with IPF, describing the radiological findings and patterns. The association of each radiological finding and radiological patterns with two-year mortality was analysed.

Results

The two-year mortality rate was 40.2% in IPF patients with an UIP radiological pattern versus 7.1% in those with probable UIP. Compared to the UIP pattern, probable UIP was protective against mortality, even after adjusting for age, sex, pulmonary function, and extent of fibrosis (hazard ratio (HR) 0.23, 95% confidence interval (CI) 0.06-0.99). Receiving antifibrotic treatment was also a protective factor (HR 0.51, 95%CI 0.27-0.98). Honeycombing (HR 3.62, 95%CI 1.27-10.32), an acute exacerbation pattern (HR 4.07, 95%CI 1.84-8.96), and the overall extent of fibrosis (HR 1.04, 95%CI 1.02-1.06) were predictors of mortality.

Conclusions

In our series, two-year mortality was higher in patients with IPF who presented a radiological pattern of UIP versus probable UIP on the initial scan. Honeycombing, an acute exacerbation pattern, and a greater overall extent of fibrosis were also predictors of increased mortality. The prognostic differences between the radiological pattern of UIP and probable UIP in our series would support maintaining them as two differentiated patterns.

Deep Learning-based Fibrosis Extent on Computed Tomography Predicts Outcome of Fibrosing Interstitial Lung Disease Independent of Visually Assessed Computed Tomography Pattern

Rationale: Radiologic pattern has been shown to predict survival in patients with fibrosing interstitial lung disease. The additional prognostic value of fibrosis extent by quantitative computed tomography (CT) is unknown. Objectives: We hypothesized that fibrosis extent provides information beyond visually assessed CT pattern that is useful for outcome prediction. Methods: We performed a retrospective analysis of chest CT, demographics, longitudinal pulmonary function, and transplantation-free survival among participants in the Pulmonary Fibrosis Foundation Patient Registry. CT pattern was classified visually according to the 2018 usual interstitial pneumonia criteria. Extent of fibrosis was objectively quantified using data-driven textural analysis. We used Kaplan-Meier plots and Cox proportional hazards and linear mixed-effects models to evaluate the relationships between CT-derived metrics and outcomes. Results: Visual assessment and quantitative analysis were performed on 979 enrollment CT scans. Linear mixed-effect modeling showed that greater baseline fibrosis extent was significantly associated with the annual rate of decline in forced vital capacity. In multivariable models that included CT pattern and fibrosis extent, quantitative fibrosis extent was strongly associated with transplantation-free survival independent of CT pattern (hazard ratio, 1.04; 95% confidence interval, 1.04-1.05; P < 0.001; C statistic = 0.73). Conclusions: The extent of lung fibrosis by quantitative CT is a strong predictor of physiologic progression and survival, independent of visually assessed CT pattern.

Thin-Section CT in the Categorization and Management of Pulmonary Fibrosis including Recently Defined Progressive Pulmonary Fibrosis

While idiopathic pulmonary fibrosis (IPF) is the most common type of fibrotic lung disease, there are numerous other causes of pulmonary fibrosis that are often characterized by lung injury and inflammation. Although often gradually progressive and responsive to immune modulation, some cases may progress rapidly with reduced survival rates (similar to IPF) and with imaging features that overlap with IPF, including usual interstitial pneumonia (UIP)-pattern disease characterized by peripheral and basilar predominant reticulation, honeycombing, and traction bronchiectasis or bronchiolectasis. Recently, the term progressive pulmonary fibrosis has been used to describe non-IPF lung disease that over the course of a year demonstrates clinical, physiologic, and/or radiologic progression and may be treated with antifibrotic therapy. As such, appropriate categorization of the patient with fibrosis has implications for therapy and prognosis and may be facilitated by considering the following categories: (a) radiologic UIP pattern and IPF diagnosis, (b) radiologic UIP pattern and non-IPF diagnosis, and (c) radiologic non-UIP pattern and non-IPF diagnosis. By noting increasing fibrosis, the radiologist contributes to the selection of patients in which therapy with antifibrotics can improve survival. As the radiologist may be first to identify developing fibrosis and overall progression, this article reviews imaging features of pulmonary fibrosis and their significance in non-IPF-pattern fibrosis, progressive pulmonary fibrosis, and implications for therapy. Keywords: Idiopathic Pulmonary Fibrosis, Progressive Pulmonary Fibrosis, Thin-Section CT, Usual Interstitial Pneumonia © RSNA, 2024.

The applications of CT with artificial intelligence in the prognostic model of idiopathic pulmonary fibrosis

TAKE HOME MESSAGE

The review summarizes the applications of CT and AI algorithms for prognostic models in IPF and procedures of model construction. It reveals the current limitations and prospects of AI-aid models, and helps clinicians to recognize the AI algorithms and apply them to more clinical work.

Quantitative Analysis for Lung Disease on Thin-Section CT

Thin-section computed tomography (CT) is widely employed not only for assessing morphology but also for evaluating respiratory function. Three-dimensional images obtained from thin-section CT provide precise measurements of lung, airway, and vessel volumes. These volumetric indices are correlated with traditional pulmonary function tests (PFT). CT also generates lung histograms. The volume ratio of areas with low and high attenuation correlates with PFT results. These quantitative image analyses have been utilized to investigate the early stages and disease progression of diffuse lung diseases, leading to the development of novel concepts such as pre-chronic obstructive pulmonary disease (pre-COPD) and interstitial lung abnormalities. Quantitative analysis proved particularly valuable during the COVID-19 pandemic when clinical evaluations were limited. In this review, we introduce CT analysis methods and explore their clinical applications in the context of various lung diseases. We also highlight technological advances, including images with matrices of 1024 × 1024 and slice thicknesses of 0.25 mm, which enhance the accuracy of these analyses.

Role of the internet of medical things in care for patients with interstitial lung disease

PURPOSE OF REVIEW

Online technologies play an increasing role in facilitating care for patients with interstitial lung disease (ILD). In this review, we will give an overview of different applications of the internet of medical things (IoMT) for patients with ILD.

RECENT FINDINGS

Various applications of the IoMT, including teleconsultations, virtual MDTs, digital information, and online peer support, are now used in daily care of patients with ILD. Several studies showed that other IoMT applications, such as online home monitoring and telerehabilitation, seem feasible and reliable, but widespread implementation in clinical practice is lacking. The use of artificial intelligence algorithms and online data clouds in ILD is still in its infancy, but has the potential to improve remote, outpatient clinic, and in-hospital care processes. Further studies in large real-world cohorts to confirm and clinically validate results from previous studies are needed.

SUMMARY

We believe that in the near future innovative technologies, facilitated by the IoMT, will further enhance individually targeted treatment for patients with ILD by interlinking and combining data from various sources.

Exploring computer-based imaging analysis in interstitial lung disease: opportunities and challenges

The advent of quantitative computed tomography (QCT) and artificial intelligence (AI) using high-resolution computed tomography data has revolutionised the way interstitial diseases are studied. These quantitative methods provide more accurate and precise results compared to prior semiquantitative methods, which were limited by human error such as interobserver disagreement or low reproducibility. The integration of QCT and AI and the development of digital biomarkers has facilitated not only diagnosis but also prognostication and prediction of disease behaviour, not just in idiopathic pulmonary fibrosis in which they were initially studied, but also in other fibrotic lung diseases. These tools provide reproducible, objective prognostic information which may facilitate clinical decision-making. However, despite the benefits of QCT and AI, there are still obstacles that need to be addressed. Important issues include optimal data management, data sharing and maintenance of data privacy. In addition, the development of explainable AI will be essential to develop trust within the medical community and facilitate implementation in routine clinical practice.

Contemporary Concise Review 2022: Interstitial lung disease

Novel genetic associations for idiopathic pulmonary fibrosis (IPF) risk have been identified. Common genetic variants associated with IPF are also associated with chronic hypersensitivity pneumonitis. The characterization of underlying mechanisms, such as pathways involved in myofibroblast differentiation, may reveal targets for future treatments. Newly identified circulating biomarkers are associated with disease progression and mortality. Deep learning and machine learning may increase accuracy in the interpretation of CT scans. Novel treatments have shown benefit in phase 2 clinical trials. Hospitalization with COVID-19 is associated with residual lung abnormalities in a substantial number of patients. Inequalities exist in delivering and accessing interstitial lung disease specialist care.

Idiopathic pulmonary fibrosis disease progression: a dynamic quantitative chest computed tomography follow-up analysis

Background

To clarify whether dynamic quantification of variables derived from chest high-resolution computed tomography (HRCT) can assess the progression of idiopathic pulmonary fibrosis (IPF).

Methods

Patients with IPF who underwent serial computed tomography (CT) imaging were retrospectively enrolled. Several structural abnormalities seen on HRCT in IPF were segmented and quantified. Patients were divided into 2 groups according to their pulmonary function test (PFT) results: those with disease stabilization and those with disease progression, and differences between the groups were analyzed.

Results

There were no statistically significant differences between the 2 patient groups for the following parameters: baseline PFTs, total lesion extent, lesion extent at different sites in the lungs, and pulmonary vessel-related parameters (with P values ranging from 0.057 to 0.894). Median changes in total lung volume, total lesion volume, and total lesion ratio were significantly higher in patients with worsening disease compared with those with stable disease (P<0.001). There was a significant increase in total lesion volume of 214.73 mL [interquartile range (IQR), 68.26 to 501.46 mL] compared with 3.67 mL (IQR, -71.70 to 85.33 mL) in the disease progression group compared with the disease stability group (P=0.001). The decline in pulmonary vessel volume and number of pulmonary vessel branches was more pronounced in the group with functional worsening compared with the group with functional stability. Moreover, changes in lesion volume ratio were negatively correlated with changes in diffusing capacity of the lungs for carbon monoxide (DLco) during follow-up (R=-0.57, P<0.001), and changes in pulmonary vessel-related parameters demonstrated positive correlation with DLco (with R ranging from 0.27 to 0.53, P<0.001) and forced vital capacity (FVC) (with R ranging from 0.44 to 0.61, P<0.001).

Conclusions

Changes in CT-related parameters during follow-up may have better predictive performance compared with baseline imaging parameters and PFTs for disease progression in IPF.

Early Diagnosis and Treatment of Idiopathic Pulmonary Fibrosis: A Narrative Review

Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive fibrosing interstitial lung disease of unknown aetiology. Patients typically present with symptoms of chronic dyspnoea and cough over a period of months to years. IPF has a poor prognosis, with an average life expectancy of 3-5 years from diagnosis if left untreated. Two anti-fibrotic medications (nintedanib and pirfenidone) have been approved for the treatment of IPF. These drugs slow disease progression by reducing decline in lung function. Early diagnosis is crucial to ensure timely treatment selection and improve outcomes. High-resolution computed tomography (HRCT) plays a major role in the diagnosis of IPF. In this narrative review, we discuss the importance of early diagnosis, awareness among primary care physicians, lung cancer screening programmes and early IPF detection, and barriers to accessing anti-fibrotic medications.

Novel diagnostic techniques in interstitial lung disease

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.

Diagnosing interstitial lung disease by multidisciplinary discussion: A review

The multidisciplinary meeting (MDM) has been endorsed in current international consensus guidelines as the gold standard method for diagnosis of interstitial lung disease (ILD). In the absence of an accurate and reliable diagnostic test, the agreement between multidisciplinary meetings has been used as a surrogate marker for diagnostic accuracy. Although the ILD MDM has been shown to improve inter-clinician agreement on ILD diagnosis, result in a change in diagnosis in a significant proportion of patients and reduce unclassifiable diagnoses, the ideal form for an ILD MDM remains unclear, with constitution and processes of ILD MDMs varying greatly around the world. It is likely that this variation of practice contributes to the lack of agreement seen between MDMs, as well as suboptimal diagnostic accuracy. A recent Delphi study has confirmed the essential components required for the operation of an ILD MDM. The ILD MDM is a changing entity, as it incorporates new diagnostic tests and genetic markers, while also adapting in its form in response to the obstacles of the COVID-19 pandemic. The aim of this review was to evaluate the current evidence regarding ILD MDM and their role in the diagnosis of ILD, the practice of ILD MDM around the world, approaches to ILD MDM standardization and future directions to improve diagnostic accuracy in ILD.