Towards the adoption of quantitative computed tomography in the management of interstitial lung disease

Impact of quantitative radiological features of interstitial lung disease on immunomodulatory treatment response in three autoimmune interstitial lung disease cohorts

BACKGROUND

The defining radiological features of autoimmune interstitial lung disease (ILD) are ground glass opacification (GGO) and fibrosis. The associations between these features and physiological response to immunomodulation remain unclear.

METHODS

This study leveraged three autoimmune ILD cohorts: two with systemic sclerosis (SSc) and one with rheumatoid arthritis (RA) which were selected for inherent differences in fibrotic extents/patterns. Linear regression models examined associations between baseline quantitative GGO, fibrosis, their ratio and forced vital capacity (FVC)%-predicted changes after 12 months of immunomodulatory therapy.

RESULTS

Patients with SSc-ILD (N=262) exhibited a higher GGO-to-fibrosis ratio compared with patients with RA-ILD (N=130) (mean ratio 3.0 vs 0.25). Increased GGO-to-fibrosis was not associated with improved FVC%-predicted in any cohort. Conversely, in patients with SSc-ILD treated with cyclophosphamide (CYC), increased fibrosis (estimate 0.17 (95% CI 0.003, 0.33); p=0.04) and increased GGO (estimate 0.15 (95% CI 0.004, 0.30); p=0.044) were both significantly associated with FVC% improvement. Given the negative direction of the estimate for GGO-to-fibrosis ratio (estimate -0.33 (95% CI -0.61, -0.06); p=0.016), CYC was associated with greater FVC% improvement in patients with a higher degree of fibrosis relative to GGO. No significant correlation was seen in patients with SSc-ILD treated with mycophenolate (N=56) or in patients with RA-ILD treated with immunomodulation (N=130).

DISCUSSION

Increased quantitative GGO relative to fibrosis was not significantly associated with improved response to immunomodulation in patients with RA-ILD and SSc-ILD. However, increased quantitative fibrosis and GGO extent were associated with improved response to CYC in SSc-ILD. More research is needed to understand how to use radiological features to guide treatment selection in ILD.

A systematic review of the role of quantitative CT in the prognostication and disease monitoring of interstitial lung disease

BACKGROUND

The unpredictable trajectory and heterogeneity of interstitial lung disease (ILDs) make prognostication challenging. Current prognostic indices and outcome measures have several limitations. Quantitative computed tomography (qCT) provides automated numerical assessment of CT imaging and has shown promise when applied to the prognostication and disease monitoring of ILD. This systematic review aims to highlight the current evidence underpinning the prognostic value of qCT in predicting outcomes in ILD.

METHODS

A comprehensive search of four databases (Medline, EMCare, Embase and CINAHL (Cumulative Index to Nursing and Allied Health Literature)) was conducted for studies published up to and including 22 November 2024. A modified CHARMS (CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies) checklist was used for data extraction. The risk of bias was assessed using a Quality in Prognostic Studies template.

RESULTS

The search identified 1134 unique studies, of which 185 studies met inclusion and exclusion criteria. Commonly studied ILD subtypes included idiopathic pulmonary fibrosis (41%, n=75), mixed subtypes (26%, n=48) and systemic sclerosis ILD (16%, n=30). Numerous studies showed significant prognostic signals, even when adjusted for common covariates and/or significant correlation between serial qCT biomarkers and conventional outcome measures. Heterogenous and nonstandardised reporting methods meant that direct comparison or meta-analysis of studies was not possible. Studies were limited by the use of retrospective methodology without prospective validation and significant study attrition.

DISCUSSION

qCT has shown efficacy in the prognostication and disease monitoring of a range of ILDs. Hurdles exist to widespread adoption including governance concerns, appropriate algorithm anchoring and standardisation of image acquisition. International collaboration is underway to address these hurdles, paving the way for regulatory approval and ultimately patient benefit.

Extent of lung fibrosis is of greater prognostic importance than HRCT pattern in patients with progressive pulmonary fibrosis: data from the ILD-PRO registry

BACKGROUND

The prognostic value of patterns and quantitative measures of lung fibrosis on high-resolution computed tomography (HRCT) in patients identified as having progressive pulmonary fibrosis (PPF) has not been established. We investigated whether HRCT patterns and quantitative scores were associated with risk of progression in patients with PPF.

METHODS

Patients enrolled in the ILD-PRO Registry had an interstitial lung disease (ILD) other than idiopathic pulmonary fibrosis, reticular abnormality and traction bronchiectasis, and met criteria for ILD progression. HRCT images taken between 24 months prior to enrollment and 90 days after enrollment were analyzed using a machine learning algorithm to derive quantitative scores. Associations were assessed between HRCT pattern (usual interstitial pneumonia [UIP]-like versus other patterns) and tertiles of quantitative scores and measures of disease severity at enrollment, and between these patterns/tertiles at enrollment and ILD progression (relative decline in forced vital capacity [FVC] % predicted ≥ 10%, lung transplant, or death) over a median follow-up of 17.3 months.

RESULTS

Among 395 patients, 178 (45.1%) had a UIP-like pattern on HRCT. A UIP-like pattern did not associate with worse disease severity at enrollment or an increased risk of ILD progression (HR 1.01 [95% CI: 0.71, 1.44]). The highest quantitative lung fibrosis (QLF) score tertile (≥ 20.5%) was associated with worse disease severity. In unadjusted analyses, patients with QLF scores in the highest tertile had a significantly increased risk of ILD progression versus the middle tertile (HR [95% CI] 1.63 [1.07, 2.49] and a numerically increased risk versus the lowest tertile (HR 1.46 [0.97, 2.18]); however, after adjustment for sex, age, FVC % predicted and oxygen use at enrollment, there were no significant differences. There were no significant associations between tertiles of quantitative ILD score, quantitative ground glass score, or quantitative honeycomb cysts score and risk of ILD progression in unadjusted or adjusted analyses.

CONCLUSIONS

In a real-world cohort of patients with PPF, QLF score associated with subsequent risk of ILD progression, while HRCT pattern did not. The QLF score did not provide additional prognostic information beyond clinical variables.

TRIAL REGISTRATION

ClinicalTrials.gov; No: NCT01915511; registered August 5, 2013; URL: www.

CLINICALTRIALS

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From images to clinical insights: an educational review on radiomics in lung diseases

Radiological imaging is a cornerstone in the clinical workup of lung diseases. Radiomics represents a significant advancement in clinical lung imaging, offering a powerful tool to complement traditional qualitative image analysis. Radiomic features are quantitative and computationally describe shape, intensity, texture and wavelet characteristics from medical images that can uncover detailed and often subtle information that goes beyond the visual capabilities of radiological examiners. By extracting this quantitative information, radiomics can provide deep insights into the pathophysiology of lung diseases and support clinical decision-making as well as personalised medicine approaches. In this educational review, we provide a step-by-step guide to radiomics-based medical image analysis, discussing the technical challenges and pitfalls, and outline the potential clinical applications of radiomics in diagnosing, prognosticating and evaluating treatment responses in respiratory medicine.

Identification of progressive pulmonary fibrosis: consensus findings from a modified Delphi study

BACKGROUND

We sought consensus among practising respiratory physicians on the prediction, identification and monitoring of progression in patients with fibrosing interstitial lung disease (ILD) using a modified Delphi process.

METHODS

Following a literature review, statements on the prediction, identification and monitoring of progression of ILD were developed by a panel of physicians with specialist expertise. Practising respiratory physicians were sent a survey asking them to indicate their level of agreement with these statements on a binary scale or 7-point Likert scale (- 3 to 3), or to select answers from a list. Consensus was considered to be achieved if ≥ 70% of respondents selected the same answer, or, for responses on a Likert scale, the median score was ≤ -2 (disagree/not important) or ≥ 2 (agree/important) with an interquartile range ≤ 1. There were three rounds of the survey.

RESULTS

Surveys 1, 2 and 3 were completed by 207, 131 and 94 physicians, respectively, between March 2022 and July 2023. Decline in forced vital capacity (FVC), decline in diffusing capacity of the lungs for carbon monoxide, and increased fibrosis on high-resolution computed tomography (HRCT) were ranked as the most important endpoints for determining progression. Consensus was reached that progression on HRCT or a decline in FVC ≥ 10% from baseline is sufficient to determine progression, and that small declines in multiple endpoints indicates progression. Consensus was reached that a histological pattern of usual interstitial pneumonia (UIP) is a risk factor for progression of ILD, but that a biopsy to look for a UIP pattern should not be performed solely for prognostic reasons. Consensus was not reached on the time period over which progression should be defined. There was consensus that appropriate management of ILD depends on the type of ILD, and that 'despite adequate management' or 'despite usual management' should be included in the definition of progression.

CONCLUSIONS

This modified Delphi process provided consensus statements on the identification of ILD progression that were supported by a broad group of clinicians and may help to inform clinical practice until robust evidence-based guidelines are available.

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.

[Using Artificial Intelligence Software for Diagnosing Emphysema and Interstitial Lung Disease]

Researchers have developed various algorithms utilizing artificial intelligence (AI) to automatically and objectively diagnose patterns and extent of pulmonary emphysema or interstitial lung diseases on chest CT scans. Studies show that AI-based quantification of emphysema on chest CT scans reveals a connection between an increase in the relative percentage of emphysema and a decline in lung function. Notably, quantifying centrilobular emphysema has proven helpful in predicting clinical symptoms or mortality rates of chronic obstructive pulmonary disease. In the context of interstitial lung diseases, AI can classify the usual interstitial pneumonia pattern on CT scans into categories like normal, ground-glass opacity, reticular opacity, honeycombing, emphysema, and consolidation. This classification accuracy is comparable to chest radiologists (70%-80%). However, the results generated by AI are influenced by factors such as scan parameters, reconstruction algorithms, radiation doses, and the training data used to develop the AI. These limitations currently restrict the widespread adoption of AI for quantifying pulmonary emphysema and interstitial lung diseases in daily clinical practice. This paper will showcase the authors' experience using AI for diagnosing and quantifying emphysema and interstitial lung diseases through case studies. We will primarily focus on the advantages and limitations of AI for these two diseases.

Interstitial Lung Disease Associated with Systemic Sclerosis

Systemic sclerosis (SSc) is a rare autoimmune disease characterized by a tripod combining vasculopathy, fibrosis, and immune-mediated inflammatory processes. The prevalence of interstitial lung disease (ILD) in SSc varies according to the methods used to detect it, ranging from 25 to 95%. The fibrotic and vascular pulmonary manifestations of SSc, particularly ILD, are the main causes of morbidity and mortality, contributing to 35% of deaths. Although early trials were conducted with cyclophosphamide, more recent randomized controlled trials have been performed to assess the efficacy and tolerability of several medications, mostly mycophenolate, rituximab, tocilizumab, and nintedanib. Although many uncertainties remain, expert consensus is emerging to optimize the therapeutic management and to provide clinicians with evidence-based clinical practice guidelines for patients with SSc-ILD. This article provides an overview, in the light of the latest advances, of the available evidence for the diagnosis and management of SSc-ILD.