Vessel-related structures predict UIP pathology in those with a non-IPF pattern on CT

Diagnostic Approach to Interstitial Lung Diseases Associated with Connective Tissue Diseases

Interstitial lung disorders are a group of respiratory diseases characterized by interstitial compartment infiltration, varying degrees of infiltration, and fibrosis, with or without small airway involvement. Although some are idiopathic (e.g., idiopathic pulmonary fibrosis, idiopathic interstitial pneumonias, and sarcoidosis), the great majority have an underlying etiology, such as systemic autoimmune rheumatic disease (SARD, also called Connective Tissue Diseases or CTD), inhalational exposure to organic matter, medications, and rarely, genetic disorders. This review focuses on diagnostic approaches in interstitial lung diseases associated with SARDs. To make an accurate diagnosis, a multidisciplinary, personalized approach is required, with input from various specialties, including pulmonary, rheumatology, radiology, and pathology, to reach a consensus. In a minority of patients, a definitive diagnosis cannot be established. Their clinical presentations and prognosis can be variable even within subsets of SARDs.

The usual Interstitial pneumonia pattern in autoimmune rheumatic diseases

Usual Interstitial Pneumonia (UIP) is characterized by progression of lung parenchyma that may be observed in various autoimmune rheumatic diseases (ARDs), including rheumatoid arthritis and connective tissue diseases. From a diagnostic point of view, a UIP pattern related to ARDs may display imaging and pathological features able to distinguish it from that related to IPF, such as the "straight-edge" sign at HRCT and lymphoplasmacytic infiltrates at histologic specimens. Multidisciplinary approach (MDD), involving at least pulmonologist, rheumatologist and radiologist, is fundamental in the differential diagnosis process, but MDD is also required in the evaluation of severity, progression and response to treatment, that is based on the combination of changes in symptoms, pulmonary function trends, and, in selected patients, serial CT evaluation. Differently from IPF, in patients with ARDs both functional evaluation and patient-reported outcomes may be affected by systemic involvement and comorbidities, including musculoskeletal manifestations of disease. Finally, in regards to pharmacological treatment, immunosuppressants have been considered the cornerstone of therapy, despite the lack of solid evidence in most cases; recently, antifibrotic drugs were also proposed for the treatment of progressive fibrosing ILDs other than IPF. In ARD-ILD, the therapeutic choice should balance the need for the control of systemic and lung involvements with the risk of adverse events from multi-morbidities and -therapies. Purpose of this review is to summarize the definition, the radiological and morphological features of the UIP pattern in ARDs, together with risk factors, diagnostic criteria, prognostic evaluation, monitoring and management approaches of the UIP-ARDs.

Analysis of tissue lipidomics and computed tomography pulmonary fat attenuation volume (CTPFAV ) in idiopathic pulmonary fibrosis

BACKGROUND AND OBJECTIVE

There is increasing interest in the role of lipids in processes that modulate lung fibrosis with evidence of lipid deposition in idiopathic pulmonary fibrosis (IPF) histological specimens. The aim of this study was to identify measurable markers of pulmonary lipid that may have utility as IPF biomarkers.

STUDY DESIGN AND METHODS

IPF and control lung biopsy specimens were analysed using a unbiased lipidomic approach. Pulmonary fat attenuation volume (PFAV) was assessed on chest CT images (CTPFAV ) with 3D semi-automated lung density software. Aerated lung was semi-automatically segmented and CTPFAV calculated using a Hounsfield-unit (-40 to -200HU) threshold range expressed as a percentage of total lung volume. CTPFAV was compared to pulmonary function, serum lipids and qualitative CT fibrosis scores.

RESULTS

There was a significant increase in total lipid content on histological analysis of IPF lung tissue (23.16 nmol/mg) compared to controls (18.66 mol/mg, p = 0.0317). The median CTPFAV in IPF was higher than controls (1.34% vs. 0.72%, p < 0.001) and CTPFAV correlated significantly with DLCO% predicted (R2  = 0.356, p < 0.0001) and FVC% predicted (R2  = 0.407, p < 0.0001) in patients with IPF. CTPFAV correlated with CT features of fibrosis; higher CTPFAV was associated with >10% reticulation (1.6% vs. 0.94%, p = 0.0017) and >10% honeycombing (1.87% vs. 1.12%, p = 0.0003). CTPFAV showed no correlation with serum lipids.

CONCLUSION

CTPFAV is an easily quantifiable non-invasive measure of pulmonary lipids. In this pilot study, CTPFAV correlates with pulmonary function and radiological features of IPF and could function as a potential biomarker for IPF disease severity assessment.

Recent Advancements in Computed Tomography Assessment of Fibrotic Interstitial Lung Diseases

Interstitial lung disease (ILD) is a heterogeneous group of disorders with complex and varied imaging manifestations and prognosis. High-resolution computed tomography (HRCT) is the current standard-of-care imaging tool for ILD assessment. However, visual evaluation of HRCT is limited by interobserver variation and poor sensitivity for subtle changes. Such challenges have led to tremendous recent research interest in objective and reproducible methods to examine ILDs. Computer-aided CT analysis to include texture analysis and machine learning methods have recently been shown to be viable supplements to traditional visual assessment through improved characterization and quantification of ILDs. These quantitative tools have not only been shown to correlate well with pulmonary function tests and patient outcomes but are also useful in disease diagnosis, surveillance and management. In this review, we provide an overview of recent computer-aided tools in diagnosis, prognosis, and longitudinal evaluation of fibrotic ILDs, while outlining some of the pitfalls and challenges that have precluded further advancement of these tools as well as potential solutions and further endeavors.

Neutrophil levels correlate with quantitative extent and progression of fibrosis in IPF: results of a single-centre cohort study

BACKGROUND

Idiopathic pulmonary fibrosis (IPF) is a progressive fibrotic lung disease with poor prognosis. Clinical studies have demonstrated association between different blood leucocytes and mortality and forced vital capacity (FVC) decline. Here, we question which blood leucocyte levels are specifically associated with progression of fibrosis, measured by accumulation of fibrosis on CT scan using a standardised automated method.

METHODS

Using the Computer-Aided Lung Informatics for Pathology Evaluation and Rating CT algorithm, we determined the correlation between different blood leucocytes (<4 months from CT) and total lung fibrosis (TLF) scores, pulmonary vessel volume (PVV), FVC% and transfer factor of lung for carbon monoxide% at baseline (n=171) and with progression of fibrosis (n=71), the latter using multivariate Cox regression.

RESULTS

Neutrophils (but not monocyte or lymphocytes) correlated with extent of lung fibrosis (TLF/litre) (r=0.208, p=0.007), PVV (r=0.259, p=0.001), FVC% (r=-0.127, p=0.029) at baseline. For the 71 cases with repeat CT; median interval between CTs was 25.9 (16.8-39.9) months. Neutrophil but not monocyte levels are associated with increase in TLF/litre (HR 2.66, 95% CI 1.35 to 5.25, p=0.005).

CONCLUSION

Our study shows that neutrophil rather than monocyte levels correlated with quantifiable increase in fibrosis on imaging of the lungs in IPF, suggesting its relative greater contribution to progression of fibrosis in IPF.

Lung shrinking assessment on HRCT with elastic registration technique for monitoring idiopathic pulmonary fibrosis

OBJECTIVES

Evaluation and follow-up of idiopathic pulmonary fibrosis (IPF) mainly rely on high-resolution computed tomography (HRCT) and pulmonary function tests (PFTs). The elastic registration technique can quantitatively assess lung shrinkage. We aimed to investigate the correlation between lung shrinkage and morphological and functional deterioration in IPF.

METHODS

Patients with IPF who underwent at least two HRCT scans and PFTs were retrospectively included. Elastic registration was performed on the baseline and follow-up HRCTs to obtain deformation maps of the whole lung. Jacobian determinants were calculated from the deformation fields and after logarithm transformation, log_jac values were represented on color maps to describe morphological deterioration, and to assess the correlation between log_jac values and PFTs.

RESULTS

A total of 69 patients with IPF (male 66) were included. Jacobian maps demonstrated constriction of the lung parenchyma marked at the lung base in patients who were deteriorated on visual and PFT assessment. The log_jac values were significantly reduced in the deteriorated patients compared to the stable patients. Mean log_jac values showed positive correlation with baseline percentage of predicted vital capacity (VC%) (r = 0.394, p < 0.05) and percentage of predicted forced vital capacity (FVC%) (r = 0.395, p < 0.05). Additionally, the mean log_jac values were positively correlated with pulmonary vascular volume (r = 0.438, p < 0.01) and the number of pulmonary vascular branches (r = 0.326, p < 0.01).

CONCLUSIONS

Elastic registration between baseline and follow-up HRCT was helpful to quantitatively assess the morphological deterioration of lung shrinkage in IPF, and the quantitative indicator log_jac values were significantly correlated with PFTs.

KEY POINTS

• The elastic registration on HRCT was helpful to quantitatively assess the deterioration of IPF. • Jacobian logarithm was significantly reduced in deteriorated patients and mean log_jac values were correlated with PFTs. • The mean log_jac values were related to the changes of pulmonary vascular volume and the number of vascular branches.

The current status and future of FDA-approved artificial intelligence tools in chest radiology in the United States

Artificial intelligence (AI) is becoming more widespread within radiology. Capabilities that AI algorithms currently provide include detection, segmentation, classification, and quantification of pathological findings. Artificial intelligence software have created challenges for the traditional United States Food and Drug Administration (FDA) approval process for medical devices given their abilities to evolve over time with incremental data input. Currently, there are 190 FDA-approved radiology AI-based software devices, 42 of which pertain specifically to thoracic radiology. The majority of these algorithms are approved for the detection and/or analysis of pulmonary nodules, for monitoring placement of endotracheal tubes and indwelling catheters, for detection of emergent findings, and for assessment of pulmonary parenchyma; however, as technology evolves, there are many other potential applications that can be explored. For example, evaluation of non-idiopathic pulmonary fibrosis interstitial lung diseases, synthesis of imaging, clinical and/or laboratory data to yield comprehensive diagnoses, and survival or prognosis prediction of certain pathologies. With increasing physician and developer engagement, transparency and frequent communication between developers and regulatory agencies, such as the FDA, AI medical devices will be able to provide a critical supplement to patient management and ultimately enhance physicians' ability to improve patient care.