Computed tomography lung parenchymal descriptions in routine radiological reporting have diagnostic and prognostic utility in patients with idiopathic pulmonary arterial hypertension and pulmonary hypertension associated with lung disease

Pulmonary hypertension associated with lung diseases

Pulmonary hypertension (PH) associated with chronic lung disease (CLD) is both common and underrecognised. The presence of PH in the setting of lung disease has been consistently shown to be associated with worse outcomes. Recent epidemiological studies have advanced understanding of the heterogeneity of this patient population and shown that defining both the specific type of CLD as well as the severity of PH (i.e. deeper phenotyping) is necessary to inform natural history and prognosis. A systematic diagnostic approach to screening and confirmation of suspected PH in CLD is recommended. Numerous uncontrolled studies and one phase 3 randomised, controlled trial have suggested a benefit in treating PH in some patients with CLD, specifically those with fibrotic interstitial lung disease (ILD). However, other studies in diseases such as COPD-PH showed adverse outcomes with some therapies. Given the expanding list of approved pharmacological treatments for pulmonary arterial hypertension, developing a treatment algorithm for specific phenotypes of CLD-PH is required. This article will summarise existing data in COPD, ILD and other chronic lung diseases, and provide recommendations for classification of CLD-PH and approach to the diagnosis and management of these challenging patients.

Impact of an expert-derived, quick hands-on tool on classifying pulmonary hypertension in chest computed tomography: a study on inexperienced readers using RAPID-CT-PH

PURPOSE

To test the inter-reader agreement in classifying pulmonary hypertension (PH) on chest contrast-enhanced computed tomography (CECT) between a consensus of two cardio-pulmonary-devoted radiologists (CRc) and inexperienced readers (radiology residents, RRs) when using a CECT-based quick hands-on tool built upon PH imaging literature, i.e., the "Rapid Access and Practical Information Digest on Computed Tomography for PH-RAPID-CT-PH".

MATERIAL AND METHODS

The observational study retrospectively included 60 PH patients who underwent CECT between 2015 and 2022. Four RRs independently reviewed all CECTs and classified each case into one of the five PH groups per the 2022 ESC/ERS guidelines. While RR3 and RR4 (RAPID-CT-PH group) used RAPID-CT-PH, RR1 and RR2 (control group) did not. RAPID-CT-PH and control groups' reports were compared with CRc using unweighted Cohen's Kappa (k) statistics. RRs' report completeness and reporting time were also compared using the Wilcoxon-Mann-Whitney test.

RESULTS

The inter-reader agreement in classifying PH between the RAPID-CT-PH group and CRc was substantial (k = 0.75 for RR3 and k = 0.65 for RR4); while, it was only moderate for the control group (k = 0.57 for RR1 and k = 0.49 for RR2). Using RAPID-CT-PH resulted in significantly higher report completeness (all p < 0.0001) and significantly lower reporting time (p < 0.0001) compared to the control group.

CONCLUSION

RRs using RAPID-CT-PH showed a substantial agreement with CRc on CECT-based PH classification. RAPID-CT-PH improved report completeness and reduced reporting time. A quick hands-on tool for classifying PH on chest CECT may help inexperienced radiologists effectively contribute to the PH multidisciplinary team.

External validation, radiological evaluation, and development of deep learning automatic lung segmentation in contrast-enhanced chest CT

OBJECTIVES

There is a need for CT pulmonary angiography (CTPA) lung segmentation models. Clinical translation requires radiological evaluation of model outputs, understanding of limitations, and identification of failure points. This multicentre study aims to develop an accurate CTPA lung segmentation model, with evaluation of outputs in two diverse patient cohorts with pulmonary hypertension (PH) and interstitial lung disease (ILD).

METHODS

This retrospective study develops an nnU-Net-based segmentation model using data from two specialist centres (UK and USA). Model was trained (n = 37), tested (n = 12), and clinically evaluated (n = 176) on a diverse 'real-world' cohort of 225 PH patients with volumetric CTPAs. Dice score coefficient (DSC) and normalised surface distance (NSD) were used for testing. Clinical evaluation of outputs was performed by two radiologists who assessed clinical significance of errors. External validation was performed on heterogenous contrast and non-contrast scans from 28 ILD patients.

RESULTS

A total of 225 PH and 28 ILD patients with diverse demographic and clinical characteristics were evaluated. Mean accuracy, DSC, and NSD scores were 0.998 (95% CI 0.9976, 0.9989), 0.990 (0.9840, 0.9962), and 0.983 (0.9686, 0.9972) respectively. There were no segmentation failures. On radiological review, 82% and 71% of internal and external cases respectively had no errors. Eighteen percent and 25% respectively had clinically insignificant errors. Peripheral atelectasis and consolidation were common causes for suboptimal segmentation. One external case (0.5%) with patulous oesophagus had a clinically significant error.

CONCLUSION

State-of-the-art CTPA lung segmentation model provides accurate outputs with minimal clinical errors on evaluation across two diverse cohorts with PH and ILD.

CLINICAL RELEVANCE

Clinical translation of artificial intelligence models requires radiological review and understanding of model limitations. This study develops an externally validated state-of-the-art model with robust radiological review. Intended clinical use is in techniques such as lung volume or parenchymal disease quantification.

KEY POINTS

• Accurate, externally validated CT pulmonary angiography (CTPA) lung segmentation model tested in two large heterogeneous clinical cohorts (pulmonary hypertension and interstitial lung disease). • No segmentation failures and robust review of model outputs by radiologists found 1 (0.5%) clinically significant segmentation error. • Intended clinical use of this model is a necessary step in techniques such as lung volume, parenchymal disease quantification, or pulmonary vessel analysis.

Improving Prognostication in Pulmonary Hypertension Using AI-quantified Fibrosis and Radiologic Severity Scoring at Baseline CT

Background There is clinical need to better quantify lung disease severity in pulmonary hypertension (PH), particularly in idiopathic pulmonary arterial hypertension (IPAH) and PH associated with lung disease (PH-LD). Purpose To quantify fibrosis on CT pulmonary angiograms using an artificial intelligence (AI) model and to assess whether this approach can be used in combination with radiologic scoring to predict survival. Materials and Methods This retrospective multicenter study included adult patients with IPAH or PH-LD who underwent incidental CT imaging between February 2007 and January 2019. Patients were divided into training and test cohorts based on the institution of imaging. The test cohort included imaging examinations performed in 37 external hospitals. Fibrosis was quantified using an established AI model and radiologically scored by radiologists. Multivariable Cox regression adjusted for age, sex, World Health Organization functional class, pulmonary vascular resistance, and diffusing capacity of the lungs for carbon monoxide was performed. The performance of predictive models with or without AI-quantified fibrosis was assessed using the concordance index (C index). Results The training and test cohorts included 275 (median age, 68 years [IQR, 60-75 years]; 128 women) and 246 (median age, 65 years [IQR, 51-72 years]; 142 women) patients, respectively. Multivariable analysis showed that AI-quantified percentage of fibrosis was associated with an increased risk of patient mortality in the training cohort (hazard ratio, 1.01 [95% CI: 1.00, 1.02]; P = .04). This finding was validated in the external test cohort (C index, 0.76). The model combining AI-quantified fibrosis and radiologic scoring showed improved performance for predicting patient mortality compared with a model including radiologic scoring alone (C index, 0.67 vs 0.61; P < .001). Conclusion Percentage of lung fibrosis quantified on CT pulmonary angiograms by an AI model was associated with increased risk of mortality and showed improved performance for predicting patient survival when used in combination with radiologic severity scoring compared with radiologic scoring alone. © RSNA, 2024 Supplemental material is available for this article.

Pulmonary hypertension associated with lung disease: new insights into pathomechanisms, diagnosis, and management

Patients with chronic lung diseases, particularly interstitial lung disease and chronic obstructive pulmonary disease, frequently develop pulmonary hypertension, which results in clinical deterioration, worsening of oxygen uptake, and an increased mortality risk. Pulmonary hypertension can develop and progress independently from the underlying lung disease. The pulmonary vasculopathy is distinct from that of other forms of pulmonary hypertension, with vascular ablation due to loss of small pulmonary vessels being a key feature. Long-term tobacco exposure might contribute to this type of pulmonary vascular remodelling. The distinct pathomechanisms together with the underlying lung disease might explain why treatment options for this condition remain scarce. Most drugs approved for pulmonary arterial hypertension have shown no or sometimes harmful effects in pulmonary hypertension associated with lung disease. An exception is inhaled treprostinil, which improves exercise capacity in patients with interstitial lung disease and pulmonary hypertension. There is a pressing need for safe, effective treatment options and for reliable, non-invasive diagnostic tools to detect and characterise pulmonary hypertension in patients with chronic lung disease.

An increased risk of pulmonary hypertension in patients with combined pulmonary fibrosis and emphysema: a meta-analysis

BACKGROUND AND AIM

Pulmonary hypertension (PH) is a common complication of combined pulmonary fibrosis and emphysema (CPFE). Whether the incidence of PH is increased in CPFE compared with pure pulmonary fibrosis or emphysema remains unclear. This meta-analysis aimed to evaluate the risk of PH in patients with CPFE compared to those with IPF or COPD/emphysema.

METHODS

We searched the PubMed, Embase, Cochrane Library, and CNKI databases for relevant studies focusing on the incidence of PH in patients with CPFE and IPF or emphysema. Pooled odds ratios (ORs) and standard mean differences (SMD) with 95% confidence intervals (95% CIs) were used to evaluate the differences in the clinical characteristics presence and severity of PH between patients with CPFE, IPF, or emphysema. The survival impact of PH in patients with CPFE was assessed using hazard ratios (HRs).

RESULTS

A total of 13 eligible studies were included in the meta-analysis, involving 560, 720, and 316 patients with CPFE, IPF, and emphysema, respectively. Patients with CPFE had an increased PH risk with a higher frequency of pulmonary hypertension and higher estimated systolic pulmonary artery pressure (esPAP), compared with those with IPF (OR: 2.66; 95% CI: 1.55-4.57; P < 0.01; SMD: 0.86; 95% CI: 0.52-1.19; P < 0.01) or emphysema (OR: 3.19; 95% CI: 1.42-7.14; P < 0.01; SMD: 0.73; 95% CI: 0.50-0.96; P < 0.01). In addition, the patients with CPFE combined with PH had a poor prognosis than patients with CPFE without PH (HR: 6.16; 95% CI: 2.53-15.03; P < 0.01).

CONCLUSIONS

Our meta-analysis showed that patients with CPFE were associated with a significantly higher risk of PH compared with those with IPF or emphysema alone. The presence of PH was a poor predictor of mortality.

Pulmonary Hypertension in Chronic Lung Diseases: What Role Do Radiologists Play?

Pulmonary hypertension (PH) is a pathophysiological disorder, defined by a mean pulmonary arterial pressure (mPAP) > 20 mmHg at rest, as assessed by right heart catheterization (RHC). PH is not a specific disease, as it may be observed in multiple clinical conditions and may complicate a variety of thoracic diseases. Conditions associated with the risk of developing PH are categorized into five different groups, according to similar clinical presentations, pathological findings, hemodynamic characteristics, and treatment strategy. Most chronic lung diseases that may be complicated by PH belong to group 3 (interstitial lung diseases, chronic obstructive pulmonary disease, combined pulmonary fibrosis, and emphysema) and are associated with the lowest overall survival among all groups. However, some of the chronic pulmonary diseases may develop PH with unclear/multifactorial mechanisms and are included in group 5 PH (sarcoidosis, pulmonary Langerhans' cell histiocytosis, and neurofibromatosis type 1). This paper focuses on PH associated with chronic lung diseases, in which radiological imaging-particularly computed tomography (CT)-plays a crucial role in diagnosis and classification. Radiologists should become familiar with the hemodynamical, physiological, and radiological aspects of PH and chronic lung diseases in patients at risk of developing PH, whose prognosis and treatment depend on the underlying disease.

A transparent artificial intelligence framework to assess lung disease in pulmonary hypertension

Recent studies have recognized the importance of characterizing the extent of lung disease in pulmonary hypertension patients by using Computed Tomography. The trustworthiness of an artificial intelligence system is linked with the depth of the evaluation in functional, operational, usability, safety and validation dimensions. The safety and validation of an artificial tool is linked to the uncertainty estimation of the model's prediction. On the other hand, the functionality, operation and usability can be achieved by explainable deep learning approaches which can verify the learning patterns and use of the network from a generalized point of view. We developed an artificial intelligence framework to map the 3D anatomical models of patients with lung disease in pulmonary hypertension. To verify the trustworthiness of the framework we studied the uncertainty estimation of the network's prediction, and we explained the learning patterns of the network. Therefore, a new generalized technique combining local explainable and interpretable dimensionality reduction approaches (PCA-GradCam, PCA-Shape) was developed. Our open-source software framework was evaluated in unbiased validation datasets achieving accurate, robust and generalized results.

Phenotyping of idiopathic pulmonary arterial hypertension: a registry analysis

BACKGROUND

Among patients meeting diagnostic criteria for idiopathic pulmonary arterial hypertension (IPAH), there is an emerging lung phenotype characterised by a low diffusion capacity for carbon monoxide (DLCO) and a smoking history. The present study aimed at a detailed characterisation of these patients.

METHODS

We analysed data from two European pulmonary hypertension registries, COMPERA (launched in 2007) and ASPIRE (from 2001 onwards), to identify patients diagnosed with IPAH and a lung phenotype defined by a DLCO of less than 45% predicted and a smoking history. We compared patient characteristics, response to therapy, and survival of these patients to patients with classical IPAH (defined by the absence of cardiopulmonary comorbidities and a DLCO of 45% or more predicted) and patients with pulmonary hypertension due to lung disease (group 3 pulmonary hypertension).

FINDINGS

The analysis included 128 (COMPERA) and 185 (ASPIRE) patients with classical IPAH, 268 (COMPERA) and 139 (ASPIRE) patients with IPAH and a lung phenotype, and 910 (COMPERA) and 375 (ASPIRE) patients with pulmonary hypertension due to lung disease. Most patients with IPAH and a lung phenotype had normal or near normal spirometry, a severe reduction in DLCO, with the majority having no or a mild degree of parenchymal lung involvement on chest computed tomography. Patients with IPAH and a lung phenotype (median age, 72 years [IQR 65-78] in COMPERA and 71 years [65-76] in ASPIRE) and patients with group 3 pulmonary hypertension (median age 71 years [65-77] in COMPERA and 69 years [63-74] in ASPIRE) were older than those with classical IPAH (median age, 45 years [32-60] in COMPERA and 52 years [38-64] in ASPIRE; p<0·0001 for IPAH with a lung phenotype vs classical IPAH in both registries). While 99 (77%) patients in COMPERA and 133 (72%) patients in ASPIRE with classical IPAH were female, there was a lower proportion of female patients in the IPAH and a lung phenotype cohort (95 [35%] COMPERA; 75 [54%] ASPIRE), which was similar to group 3 pulmonary hypertension (336 [37%] COMPERA; 148 [39%] ASPIRE]). Response to pulmonary arterial hypertension therapies at first follow-up was available from COMPERA. Improvements in WHO functional class were observed in 54% of patients with classical IPAH, 26% of patients with IPAH with a lung phenotype, and 22% of patients with group 3 pulmonary hypertension (p<0·0001 for classical IPAH vs IPAH and a lung phenotype, and p=0·194 for IPAH and a lung phenotype vs group 3 pulmonary hypertension); median improvements in 6 min walking distance were 63 m, 25 m, and 23 m for these cohorts respectively (p=0·0015 for classical IPAH vs IPAH and a lung phenotype, and p=0·64 for IPAH and a lung phenotype vs group 3 pulmonary hypertension), and median reductions in N-terminal-pro-brain-natriuretic-peptide were 58%, 27%, and 16% respectively (p=0·0043 for classical IPAH vs IPAH and a lung phenotype, and p=0·14 for IPAH and a lung phenotype vs group 3 pulmonary hypertension). In both registries, survival of patients with IPAH and a lung phenotype (1 year, 89% in COMPERA and 79% in ASPIRE; 5 years, 31% in COMPERA and 21% in ASPIRE) and group 3 pulmonary hypertension (1 year, 78% in COMPERA and 64% in ASPIRE; 5 years, 26% in COMPERA and 18% in ASPIRE) was worse than survival of patients with classical IPAH (1 year, 95% in COMPERA and 98% in ASPIRE; 5 years, 84% in COMPERA and 80% in ASPIRE; p<0·0001 for IPAH with a lung phenotype vs classical IPAH in both registries).

INTERPRETATION

A cohort of patients meeting diagnostic criteria for IPAH with a distinct, presumably smoking-related form of pulmonary hypertension accompanied by a low DLCO, resemble patients with pulmonary hypertension due to lung disease rather than classical IPAH. These observations have pathogenetic, diagnostic, and therapeutic implications, which require further exploration.

FUNDING

COMPERA is funded by unrestricted grants from Acceleron, Bayer, GlaxoSmithKline, Janssen, and OMT. The ASPIRE Registry is supported by Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK.