Automated quantification of radiological patterns predicts survival in idiopathic pulmonary fibrosis

Correlations between blood vessel distribution, lung function and structural change in idiopathic pulmonary fibrosis

BACKGROUND AND OBJECTIVE

Correlations between the image analysis of CT scan, lung function and quality of life in patients with idiopathic pulmonary fibrosis (IPF) remain unclear. This study aimed to investigate the impacts of pulmonary blood-vessel distribution and the extent of fibrosis on the lung function and quality of life of patients with IPF.

METHODS

Patients were enrolled in an IPF registry and had completed pulmonary function tests, chest HRCT, St. George Respiratory Questionnaire (SGRQ) and echocardiography. Pulmonary blood-vessel distribution, specific image-derived airway volume (siVaw) and fibrosis extent (siVfib) were quantitatively calculated by functional respiratory imaging on HRCT.

RESULTS

The study subjects were categorized into DLco <40% pred. (n = 40) and DLco ≥40% pred. (n = 19) groups. Patients with DLco <40% pred. had significantly higher scores of SGRQ, composite physiologic index (CPI), exercise oxygen desaturation (∆SpO2), siVaw, lower FVC% pred. and 6-minute walking distance% pred. The proportion of small blood vessels in the upper lobes (BV5PR-UL) was significantly correlated with CPI, DLco % Pred., FVC% pred., SGRQ and ∆SpO2. Only BV5PR-UL had a significant impact on all indices but not BV5PR in the lower lobes (BV5PR-LL). siVfib was significantly negatively correlated with BV5PR-UL, DLco% pred. and FVC% pred., as well as positively correlated with CPI, ∆SpO2 and siVaw.

CONCLUSION

BV5PR-UL and siVfib had significant correlations with lung function and may become important indicators to assess the severity of IPF and the impact on quality of life.

A quantitative analysis of progressive fibrosing interstitial lung disease on computed tomography for the assessment of decreased vital capacity

BACKGROUND

The results of a quantitative analysis of computed tomography (CT) of interstitial lung disease (ILD) using a computer-aided detection (CAD) technique were correlated with the results of pulmonary function tests.

PURPOSE

To evaluate the correlation between a quantitative analysis of CT of progressive fibrosing interstitial lung disease (PF-ILD) including idiopathic pulmonary fibrosis (IPF) and non-IPF, which can manifest progressive pulmonary fibrosis and the vital capacity (VC), and to identify indicators for the assessment of a decreased VC.

MATERIAL AND METHODS

A total of 73 patients (46 patients with IPF and 27 patients with non-IPF) were included in this study. Associations between the quantitative analysis of CT and the %VC using a CAD software program were investigated using Spearman's rank correlation and a logistic regression analysis. The appropriate cutoff vale for predicting a decreased VC was determined (%VC <80) and the area under the curve (AUC) was calculated.

RESULTS

A multiple logistic regression analysis showed that the total extent of interstitial pneumonia on CT was a significant indicator of a decreased VC (P = 0.0001; odds ratio [OR]=1.15; 95% confidence interval [CI]=1.06-1.27 in IPF and P = 0.0025; OR=1.16; 95% CI=1.03-1.30 in non-IPF). The cutoff values of the total extent of interstitial pneumonia in IPF and non-IPF for predicting a decreased VC were determined to be 23.3% and 21.5%, and the AUCs were 0.83 and 0.91, respectively.

CONCLUSION

A quantitative analysis of CT of PF-ILD using a CAD software program could be useful for predicting a decreased VC.

Clustering analysis of HRCT parameters measured using a texture-based automated system: relationship with clinical outcomes of IPF

PURPOSE

The extent of honeycombing and reticulation predict the clinical prognosis of IPF. Emphysema, consolidation, and ground glass opacity are visible in HRCT scans. To date, there have been few comprehensive studies that have used these parameters. We conducted automated quantitative analysis to identify predictive parameters for clinical outcomes and then grouped the subjects accordingly.

METHODS

CT images were obtained while patients held their breath at full inspiration. Parameters were analyzed using an automated lung texture quantification system. Cluster analysis was conducted on 159 IPF patients and clinical profiles were compared between clusters in terms of survival.

RESULTS

Kaplan-Meier analysis revealed that survival rates declined as fibrosis, reticulation, honeycombing, consolidation, and emphysema scores increased. Cox regression analysis revealed that reticulation had the most significant impact on survival rate, followed by honeycombing, consolidation, and emphysema scores. Hierarchical and K-means cluster analyses revealed 3 clusters. Cluster 1 (n = 126) with the lowest values for all parameters had the longest survival duration, and relatively-well preserved FVC and DLCO. Cluster 2 (n = 15) with high reticulation and consolidation scores had the lowest FVC and DLCO values with a predominance of female, while cluster 3 (n = 18) with high honeycombing and emphysema scores predominantly consisted of male smokers. Kaplan-Meier analysis revealed that cluster 2 had the lowest survival rate, followed by cluster 3 and cluster 1.

CONCLUSION

Automated quantitative CT analysis provides valuable information for predicting clinical outcomes, and clustering based on these parameters may help identify the high-risk group for management.

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.

Radiology of fibrosis. Part I: Thoracic organs

Sustained injury from factors such as hypoxia, infection, or physical damage may provoke improper tissue repair and the anomalous deposition of connective tissue that causes fibrosis. This phenomenon may take place in any organ, ultimately leading to their dysfunction and eventual failure. Tissue fibrosis has also been found to be central in both the process of carcinogenesis and cancer progression. Thus, its prompt diagnosis and regular monitoring is necessary for implementing effective disease-modifying interventions aiming to reduce mortality and improve overall quality of life. While significant research has been conducted on these subjects, a comprehensive understanding of how their relationship manifests through modern imaging techniques remains to be established. This work intends to provide a comprehensive overview of imaging technologies relevant to the detection of fibrosis affecting thoracic organs as well as to explore potential future advancements in this field.

Computed tomography machine learning classifier correlates with mortality in interstitial lung disease

BACKGROUND

A machine learning classifier system, Fibresolve, was designed and validated as an adjunct to non-invasive diagnosis in idiopathic pulmonary fibrosis (IPF). The system uses a deep learning algorithm to analyze chest computed tomography (CT) imaging. We hypothesized that Fibresolve is a useful predictor of mortality in interstitial lung diseases (ILD).

METHODS

Fibresolve was previously validated in a multi-site >500-patient dataset. In this analysis, we assessed the usefulness of Fibresolve to predict mortality in a subset of 228 patients with IPF and other ILDs in whom follow up data was available. We applied Cox regression analysis adjusting for the Gender, Age, and Physiology (GAP) score and for other known predictors of mortality in IPF. We also analyzed the role of Fibresolve as tertiles adjusting for GAP stages.

RESULTS

During a median follow-up of 2.8 years (range 5 to 3434 days), 89 patients died. After adjusting for GAP score and other mortality risk factors, the Fibresolve score significantly predicted the risk of death (HR: 7.14; 95% CI: 1.31-38.85; p = 0.02) during the follow-up period, as did forced vital capacity and history of lung cancer. After adjusting for GAP stages and other variables, Fibresolve score split into tertiles significantly predicted the risk of death (p = 0.027 for the model; HR 1.37 for 2nd tertile; 95% CI: 0.77-2.42. HR 2.19 for 3rd tertile; 95% CI: 1.22-3.93).

CONCLUSIONS

The machine learning classifier Fibresolve demonstrated to be an independent predictor of mortality in ILDs, with prognostic performance equivalent to GAP based solely on CT images.

Usefulness of CT Quantification-Based Assessment in Defining Progressive Pulmonary Fibrosis

RATIONALE AND OBJECTIVES

To establish a quantitative CT threshold for radiological disease progression of progressive pulmonary fibrosis (PPF) and evaluate its feasibility in patients with connective tissue disease-related interstitial lung disease (CTD-ILD).

MATERIALS AND METHODS

Between April 2007 and October 2022, patients diagnosed with CTD-ILD retrospectively evaluated. CT quantification was conducted using a commercial software by summing the percentages of ground-glass opacity, consolidation, reticular opacity, and honeycombing. The quantitative threshold for radiological progression was determined based on the highest discrimination on overall survival (OS). Two thoracic radiologists independently evaluated visual radiological progression, and the senior radiologist's assessment was used as the final result. Cox regression was used to assess prognosis of PPF based on the visual assessment and quantitative threshold.

RESULTS

97 patients were included and followed up for a median of 30.3 months (range, 4.7-198.1 months). For defining radiological disease progression, the optimal quantitative CT threshold was 4%. Using this threshold, 12 patients were diagnosed with PPF, while 14 patients were diagnosed with PPF based on the visual assessment, with an agreement rate of 97.9% (95/97). Worsening respiratory symptoms (hazard ratio [HR], 12.73; P < .001), PPF based on the visual assessment (HR, 8.86; P = .002) and based on the quantitative threshold (HR, 6.72; P = .009) were independent risk factors for poor OS.

CONCLUSION

The quantitative CT threshold for radiological disease progression (4%) was feasible in defining PPF in terms of its agreement with PPF grouping and prognostic performance when compared to visual assessment.

Machine learning classifier is associated with mortality in interstitial lung disease: a retrospective validation study leveraging registry data

BACKGROUND

Mortality prediction in interstitial lung disease (ILD) poses a significant challenge to clinicians due to heterogeneity across disease subtypes. Currently, forced vital capacity (FVC) and Gender, Age, and Physiology (GAP) score are the two most utilized metrics in prognostication. Recently, a machine learning classifier system, Fibresolve, designed to identify a variety of computed tomography (CT) patterns associated with idiopathic pulmonary fibrosis (IPF), was demonstrated to have a significant association with mortality across multiple subtypes of ILD. The purpose of this follow-up study was to retrospectively validate these findings in a large, external cohort of patients with ILD.

METHODS

In this multi-center validation study, Fibresolve was applied to chest CT scans of patients with confirmed ILD that had available follow-up data. Fibresolve scores categorized by tertile were analyzed using Cox regression analysis adjusted for tobacco use and modified GAP (mGAP) score.

RESULTS

Of 643 patients included, 446 (69.3%) died over a median follow-up time of 144 [1-821] weeks. The median [range] mGAP score was 5 [3-7]. In multivariable analysis, Fibresolve score categorized by tertile was significantly associated with mortality (Tertile 2 HR 1.47, 95% CI 0.82-2.37, p = 0.11; Tertile 3 HR 3.12, 95% CI 1.98-4.90, p < 0.001). Subgroup analyses revealed significant associations amongst those with non-IPF ILDs (Tertile 2 HR 1.95, 95% CI 1.28-2.97, Tertile 3 HR 4.66, 95% CI 2.94-7.38) and severe disease, defined by a FVC ≤ 75% (Tertile 2 HR 2.29, 95% CI 1.43-3.67, Tertile 3 HR 4.80, 95% CI 2.93-7.86).

CONCLUSIONS

Fibresolve is independently associated with mortality in ILD, particularly amongst patients with non-IPF ILDs and in those with severe disease.

Longitudinal assessment of interstitial lung abnormalities on CT in patients with COPD using artificial intelligence-based segmentation: a prospective observational study

BACKGROUND

Interstitial lung abnormalities (ILAs) on CT may affect the clinical outcomes in patients with chronic obstructive pulmonary disease (COPD), but their quantification remains unestablished. This study examined whether artificial intelligence (AI)-based segmentation could be applied to identify ILAs using two COPD cohorts.

METHODS

ILAs were diagnosed visually based on the Fleischner Society definition. Using an AI-based method, ground-glass opacities, reticulations, and honeycombing were segmented, and their volumes were summed to obtain the percentage ratio of interstitial lung disease-associated volume to total lung volume (ILDvol%). The optimal ILDvol% threshold for ILA detection was determined in cross-sectional data of the discovery and validation cohorts. The 5-year longitudinal changes in ILDvol% were calculated in discovery cohort patients who underwent baseline and follow-up CT scans.

RESULTS

ILAs were found in 32 (14%) and 15 (10%) patients with COPD in the discovery (n = 234) and validation (n = 153) cohorts, respectively. ILDvol% was higher in patients with ILAs than in those without ILA in both cohorts. The optimal ILDvol% threshold in the discovery cohort was 1.203%, and good sensitivity and specificity (93.3% and 76.3%) were confirmed in the validation cohort. 124 patients took follow-up CT scan during 5 ± 1 years. 8 out of 124 patients (7%) developed ILAs. In a multivariable model, an increase in ILDvol% was associated with ILA development after adjusting for age, sex, BMI, and smoking exposure.

CONCLUSION

AI-based CT quantification of ILDvol% may be a reproducible method for identifying and monitoring ILAs in patients with COPD.

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.

Quantitative CT Texture Analysis of COVID-19 Hospitalized Patients during 3-24-Month Follow-Up and Correlation with Functional Parameters

BACKGROUND

To quantitatively evaluate CT lung abnormalities in COVID-19 survivors from the acute phase to 24-month follow-up. Quantitative CT features as predictors of abnormalities' persistence were investigated.

METHODS

Patients who survived COVID-19 were retrospectively enrolled and underwent a chest CT at baseline (T0) and 3 months (T3) after discharge, with pulmonary function tests (PFTs). Patients with residual CT abnormalities repeated the CT at 12 (T12) and 24 (T24) months after discharge. A machine-learning-based software, CALIPER, calculated the CT percentage of the whole lung of normal parenchyma, ground glass (GG), reticulation (Ret), and vascular-related structures (VRSs). Differences (Δ) were calculated between time points. Receiver operating characteristic (ROC) curve analyses were performed to test the baseline parameters as predictors of functional impairment at T3 and of the persistence of CT abnormalities at T12.

RESULTS

The cohort included 128 patients at T0, 133 at T3, 61 at T12, and 34 at T24. The GG medians were 8.44%, 0.14%, 0.13% and 0.12% at T0, T3, T12 and T24. The Ret medians were 2.79% at T0 and 0.14% at the following time points. All Δ significantly differed from 0, except between T12 and T24. The GG and VRSs at T0 achieved AUCs of 0.73 as predictors of functional impairment, and area under the curves (AUCs) of 0.71 and 0.72 for the persistence of CT abnormalities at T12.

CONCLUSIONS

CALIPER accurately quantified the CT changes up to the 24-month follow-up. Resolution mostly occurred at T3, and Ret persisting at T12 was almost unchanged at T24. The baseline parameters were good predictors of functional impairment at T3 and of abnormalities' persistence at T12.

Detection of the usual interstitial pneumonia pattern in chest CT: effect of computer-aided diagnosis on radiologist diagnostic performance

BACKGROUND

Anti-fibrotic drugs for interstitial pulmonary fibrosis (IPF) have been developed. Physicians are becoming increasingly aware of the need for better diagnosis of IPF.

PURPOSE

To evaluate whether a computer-aided system can improve the diagnostic performance of general radiologists in detecting the usual interstitial pneumonia (UIP) pattern on computed tomography (CT).

MATERIAL AND METHODS

We included 60 CT datasets from 30 patients with IPF and 30 with idiopathic fibrosing non-specific interstitial pneumonia (fNSIP), all diagnosed by a multidisciplinary diagnosis (MDD) procedure that included surgical biopsy. We analyzed the CT data using a computer-aided system (Gaussian histogram normalized correlation: GHNC). Five general radiologists with <6 years of experience each interpreted these CT scans with and without the GHNC results. We compared the likelihoods of a UIP-pattern diagnosis with the likelihood of the same diagnosis by MDD using the average area under the curve (AUC) of the receiver operating characteristics (ROC). We also evaluated the association between the radiologists' diagnosis and survival using the Kaplan-Meier method.

RESULTS

In the ROC analysis, the AUC increased significantly from 0.731 without GHNC to 0.829 with GHNC (P = 0.0396). The diagnosis without GHNC was not significantly associated with survival for any radiologist, but the UIP diagnosis with GHNC was significantly associated with a worse prognosis for four out of five radiologists.

CONCLUSION

The computer-aided system could increase the confidence level of UIP-pattern diagnosis by non-expert radiologists.

External validation of Fibresolve, a machine-learning algorithm, to non-invasively diagnose idiopathic pulmonary fibrosis

BACKGROUND

Previous work has shown the ability of Fibresolve, a machine learning system, to non-invasively classify idiopathic pulmonary fibrosis (IPF) with a pre-invasive sensitivity of 53% and specificity of 86% versus other types of interstitial lung disease. Further external validation for the use of Fibresolve to classify IPF in patients with non-definite usual interstitial pneumonia (UIP) is needed. The aim of this study is to assess the sensitivity for Fibresolve to positively classify IPF in an external cohort of patients with a non-definite UIP radiographic pattern.

METHODS

This is a retrospective analysis of patients (n = 193) enrolled in two prospective phase two clinical trials that enrolled patients with IPF. We retrospectively identified patients with non-definite UIP on HRCT (n = 51), 47 of whom required surgical lung biopsy for diagnosis. Fibresolve was used to analyze the HRCT chest imaging which was obtained prior to invasive biopsy and sensitivity for final diagnosis of IPF was calculated.

RESULTS

The sensitivity of Fibresolve for the non-invasive classification of IPF in patients with a non-definite UIP radiographic pattern by HRCT was 76.5% (95% CI 66.5-83.7). For the subgroup of 47 patients who required surgical biopsy to aid in final diagnosis of IPF, Fibresolve had a sensitivity of 74.5% (95% CI 60.5-84.7).

CONCLUSION

In patients with suspected IPF with non-definite UIP on HRCT, Fibresolve can positively identify cases of IPF with high sensitivity. These results suggest that in combination with standard clinical assessment, Fibresolve has the potential to serve as an adjunct in the non-invasive diagnosis of IPF.

Severe Lung Dysfunction and Pulmonary Blood Flow during Extracorporeal Membrane Oxygenation

BACKGROUND

Extracorporeal membrane oxygenation (ECMO) is indicated for patients with severe respiratory and/or circulatory failure. The standard technique to visualize the extent of pulmonary damage during ECMO is computed tomography (CT).

PURPOSE

This single-center, retrospective study investigated whether pulmonary blood flow (PBF) measured with echocardiography can assist in assessing the extent of pulmonary damage and whether echocardiography and CT findings are associated with patient outcomes.

METHODS

All patients (>15 years) commenced on ECMO between 2011 and 2017 with septic shock of pulmonary origin and a treatment time >28 days were screened. Of 277 eligible patients, 9 were identified where both CT and echocardiography had been consecutively performed.

RESULTS

CT failed to indicate any differences in viable lung parenchyma within or between survivors and non-survivors at any time during ECMO treatment. Upon initiation of ECMO, the survivors (n = 5) and non-survivors (n = 4) had similar PBF. During a full course of ECMO support, survivors showed no change in PBF (3.8 ± 2.1 at ECMO start vs. 7.9 ± 4.3 L/min, p = 0.12), whereas non-survivors significantly deteriorated in PBF from 3.5 ± 1.0 to 1.0 ± 1.1 L/min (p = 0.029). Tidal volumes were significantly lower over time among the non-survivors, p = 0.047.

CONCLUSIONS

In prolonged ECMO for pulmonary septic shock, CT was not found to be effective for the evaluation of pulmonary viability or recovery. This hypothesis-generating investigation supports echocardiography as a tool to predict pulmonary recovery via the assessment of PBF at the early to later stages of ECMO support.

Deep Learning-Based CT Reconstruction Kernel Conversion in the Quantification of Interstitial Lung Disease: Effect on Reproducibility

RATIONALE AND OBJECTIVES

The effect of different computed tomography (CT) reconstruction kernels on the quantification of interstitial lung disease (ILD) has not been clearly demonstrated. The study aimed to investigate the effect of reconstruction kernels on the quantification of ILD on CT and determine whether deep learning-based kernel conversion can reduce the variability of automated quantification results between different CT kernels.

MATERIALS AND METHODS

Patients with ILD or interstitial lung abnormality who underwent noncontrast high-resolution CT between June 2022 and September 2022 were retrospectively included. Images were reconstructed with three different kernels: B30f, B50f, and B60f. B60f was regarded as the reference standard for quantification, and B30f and B50f images were converted to B60f images using a deep learning-based algorithm. Each disease pattern of ILD and the fibrotic score were quantified using commercial software. The effect of kernel conversion on measurement variability was estimated using intraclass correlation coefficient (ICC) and Bland-Altman method.

RESULTS

A total of 194 patients were included in the study. Application of different kernels induced differences in the quantified extent of each pattern. Reticular opacity and honeycombing were underestimated on B30f images and overestimated on B50f images. After kernel conversion, measurement variability was reduced (mean difference, from -2.0 to 3.9 to -0.3 to 0.4%, and 95% limits of agreement [LOA], from [-5.0, 12.7] to [-2.7, 2.1]). The fibrotic score for converted B60f from B50f images was almost equivalent to the original B60f (ICC, 1.000; mean difference, 0.0; and 95% LOA [-0.4, 0.4]).

CONCLUSION

Quantitative CT analysis of ILD was affected by the application of different kernels, but deep learning-based kernel conversion effectively reduced measurement variability, improving the reproducibility of quantification.

Diagnosis of interstitial lung disease (ILD) secondary to systemic sclerosis (SSc) and rheumatoid arthritis (RA) and identification of 'progressive pulmonary fibrosis' using chest CT: a narrative review

Interstitial lung disease (ILD) is a frequent manifestation of connective tissue diseases (CTDs), with incidence and prevalence variously assessed in the literature but reported in up to 30% of patients, with higher frequency in rheumatoid arthritis (RA) and systemic sclerosis (SSc). Recent years have seen a growing interest in the pulmonary manifestations of ILD-CTDs, mainly due to the widening of the use of anti-fibrotic drugs initially introduced exclusively for IPF, and radiologists play a key role because the lung biopsy is very rarely used in these patients where the morphological assessment is essentially left to imaging and especially HRCT. In this narrative review we will discuss, from the radiologist's point of view, the most recent findings in the field of ILD secondary to SSc and RA, with a special focus about the progression of disease and in particular about the 'progressive pulmonary fibrosis' (PPF) phenotype, and we will try to address two main issues: How to predict a possible evolution and therefore a worse prognosis when diagnosing a new case of ILD-CTDs and how to assess the progression of an already diagnosed ILD-CTDs.

The potential role of artificial intelligence in the clinical practice of interstitial lung disease

Artificial intelligence (AI) is being widely applied in the field of medicine, in areas such as drug discovery, diagnostic support, and assistance with medical practice. Among these, medical imaging is an area where AI is expected to make a significant contribution. In Japan, as of November 2022, 23 AI medical devices have received regulatory approval; all these devices are related to image analysis. In interstitial lung diseases, technologies have been developed that use AI to analyze high-resolution computed tomography and pathological images, and gene expression patterns in tissue taken from transbronchial lung biopsies to assist in the diagnosis of idiopathic pulmonary fibrosis. Some of these technologies are already being used in clinical practice in the United States. AI is expected to reduce the burden on physicians, improve reproducibility, and advance personalized medicine. Obtaining sufficient data for diseases with a small number of patients is difficult. Additionally, certain issues must be addressed in order for AI to be applied in healthcare. These issues include taking responsibility for the AI results output, updating software after the launch of technology, and adapting to new imaging technologies. Establishing research infrastructures such as large-scale databases and common platforms is important for the development of AI technology: their use requires an understanding of the characteristics and limitations of the systems. CLINICAL TRIAL REGISTRATION: Not applicable.

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.

Computed Tomography Radiomics-based Prediction Model for Gender-Age-Physiology Staging of Connective Tissue Disease-associated Interstitial Lung Disease

PURPOSE

To analyze the feasibility of predicting gender-age-physiology (GAP) staging in patients with connective tissue disease-associated interstitial lung disease (CTD-ILD) by radiomics based on computed tomography (CT) of the chest.

MATERIALS AND METHODS

Chest CT images of 184 patients with CTD-ILD were retrospectively analyzed. GAP staging was performed on the basis of gender, age, and pulmonary function test results. GAP I, II, and III have 137, 36, and 11 cases, respectively. The cases in GAP Ⅱ and Ⅲ were then combined into one group, and the two groups of patients were randomly divided into the training and testing groups with a 7:3 ratio. The radiomics features were extracted using AK software. Multivariate logistic regression analysis was then conducted to establish a radiomics model. A nomogram model was established on the basis of Rad-score and clinical factors (age and gender).

RESULTS

For the radiomics model, four significant radiomics features were selected to construct the model and showed excellent ability to differentiate GAP I from GAP Ⅱ and Ⅲ in both the training group (the area under the curve [AUC] = 0.803, 95% confidence interval [CI]: 0.724-0.874) and testing group (AUC = 0.801, 95% CI:0.663-0.912). The nomogram model that combined clinical factors and radiomics features improved higher accuracy of both training (88.4% vs. 82.1%) and testing (83.3% vs. 79.2%).

CONCLUSION

The disease severity assessment of patients with CTD-ILD can be evaluated by applying the radiomics method based on CT images. The nomogram model demonstrates better performance for predicting the GAP staging.

Radiographic grading system for usual interstitial pneumonia correlates with mortality and may serve as a surrogate endpoint in clinical trials

PURPOSE

Usual interstitial pneumonia (UIP)/ idiopathic pulmonary fibrosis (IFP) is a relentlessly progressive lung disease with outcomes similar to cancer. We have previously established a radiologic grading system for UIP and demonstrated that it correlates with pulmonary function tests; here we test the hypothesis that it correlates with mortality. Validating a correlation with mortality will demonstrate the utility of this system for monitoring progression over time clinically and in trials of anti-fibrotic agents.

METHODS

We searched the radiology record system "Catalyst" to identify cases and reviewed each case to confirm the diagnosis. 94 patients met the inclusion criteria for further assessment. Chest CT grade was determined by consensus of two cardiothoracic radiologists. Data was analyzed to identify the interval between chest CT and death. This interval was correlated with CT grade using Spearman correlation analysis.

RESULTS

For all cases, chest CT grade and mortality demonstrated a positive correlation of rs = 0.37732, 2-tailed p = 0.00018. We also employed subgroup analysis; for the subgroup with intervals less than or equal to 100 days, there was a positive correlation, rs = 0.48339, 2-tailed p = 0.03602; for the subgroup with an interval greater than 100 days between imaging and death there was a positive correlation, rs = 0.302, 2-tailed p = 0.00846.

CONCLUSION

These data support use of this system for monitoring clinical progression and as a surrogate endpoint for clinical trials. Future work building upon the data presented here will evaluate its utility in clinical trials and develop automated grading.

The effect of CALIPER-derived parameters for idiopathic pulmonary fibrosis in predicting prognosis, progression, and mortality: a systematic review

BACKGROUND

High-resolution computed tomography (HRCT), as the main tool for monitoring idiopathic pulmonary fibrosis (IPF), is characterized by subjective variability among radiologists and insensitivity to subtle changes. Recently, a few studies have aimed to decrease subjective bias by assessing the severity of IPF using computer software, i.e., Computer-Aided Lung Informatics for Pathology Evaluation and Rating (CALIPER). However, these studies had diverse research directions. In this review, we systematically assess the effect of CALIPER in the management of IPF.

METHODS

A systematic review was conducted through a search of published studies in PubMed, Web of Science, Cochrane, Embase, Scopus, and CNKI databases from database inception through February 28, 2022. The methodological quality would be evaluated by using Methodological Index for Non-Randomized Studies (MINORS). Narrative synthesis summarized findings by participant characteristics, study design, and associations with outcomes.

RESULTS

Ten studies were included. They evaluated the relationship between CALIPER-derived parameters and pulmonary function test (PFT) and mortality. CALIPER-derived parameters showed a significant correlation with PFT and mortality. Two studies reported that CALIPER could be used to stratify outcomes.

CONCLUSION

CALIPER-derived parameters can be used to evaluate prognosis and mortality. CALIPER-derived parameters combined with composite physiologic index (CPI) or Gender-Age-Physiology (GAP) could help clinicians implement targeted management by refining prognostic stratification. However, research has been constrained by small number of retrospective investigations and sample sizes. Therefore, it is essential to design prospective controlled studies and establish the staging system by CALIPER-derived parameters and combining them with CPI, FVC, or GAP.

CLINICAL RELEVANCE STATEMENT

It is beneficial for clinic to provide objective, sensitive, and accurate indicators of disease progression. It also helps the clinic to develop individualized treatment plans based on the stage of disease progression and provides evaluation of efficacy in drug trials.

KEY POINTS

• Computer-Aided Lung Informatics for Pathology Evaluation and Rating (CALIPER) is a quantitative CT analysis software that can be used to evaluate the progression of disease on CT. • The CALIPER-derived vessel-related structure shows great performance in the management of idiopathic pulmonary fibrosis. • CALIPER-derived parameters combined with composite physiologic index or Gender-Age-Physiology can be used to refine prognostic stratification.

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.

A quantitative analysis of long-term follow-up computed tomography of idiopathic pulmonary fibrosis: the correlation with the progression and prognosis

BACKGROUND

Quantitative analyses of computed tomography (CT) images using computer-aided detection (CAD) are correlated with visual assessments and pulmonary function test findings and might be useful for predicting the prognosis of patients with idiopathic pulmonary fibrosis (IPF).

PURPOSE

To evaluate the association between the quantitative analysis of long-term follow-up CT of IPF and the progression and prognosis.

MATERIAL AND METHODS

A total of 48 patients with IPF who received over one year of follow-up CT were included in this study. The results of quantitative analyses (emphysema, ground-glass attenuation [GGA], consolidation, reticulation, and honeycombing) using a CAD software program of initial and follow-up CT findings were evaluated, and the association with the progression of the total lesion of IPF and prognosis using Spearman's rank correlation and Cox regression analyses was considered.

RESULTS

Results of quantitative analyses of consolidation, reticulation, honeycombing, and the total lesion on initial CT were correlated with progressive changes in the total lesion of IPF per year (r = 0.4375, 0.4128, 0.4649, and 0.4095, respectively). The results of quantitative analyses of honeycombing (hazard ratio [HR] = 1.40, 95% confidence interval [CI] = 1.03-1.89, P = 0.0314) and GGA (HR = 0.85, 95% CI = 0.72-0.99, P = 0.0384) at initial CT were prognostic factors according to a multivariate Cox regression analysis.

CONCLUSION

The quantitative analysis of honeycombing using a CAD software program of CT findings may be useful for predicting the progression and prognosis of patients with IPF.

Generative Adversarial Network-Based Image Conversion Among Different Computed Tomography Protocols and Vendors: Effects on Accuracy and Variability in Quantifying Regional Disease Patterns of Interstitial Lung Disease

OBJECTIVE

To assess whether computed tomography (CT) conversion across different scan parameters and manufacturers using a routable generative adversarial network (RouteGAN) can improve the accuracy and variability in quantifying interstitial lung disease (ILD) using a deep learning-based automated software.

MATERIALS AND METHODS

This study included patients with ILD who underwent thin-section CT. Unmatched CT images obtained using scanners from four manufacturers (vendors A-D), standard- or low-radiation doses, and sharp or medium kernels were classified into groups 1-7 according to acquisition conditions. CT images in groups 2-7 were converted into the target CT style (Group 1: vendor A, standard dose, and sharp kernel) using a RouteGAN. ILD was quantified on original and converted CT images using a deep learning-based software (Aview, Coreline Soft). The accuracy of quantification was analyzed using the dice similarity coefficient (DSC) and pixel-wise overlap accuracy metrics against manual quantification by a radiologist. Five radiologists evaluated quantification accuracy using a 10-point visual scoring system.

RESULTS

Three hundred and fifty CT slices from 150 patients (mean age: 67.6 ± 10.7 years; 56 females) were included. The overlap accuracies for quantifying total abnormalities in groups 2-7 improved after CT conversion (original vs. converted: 0.63 vs. 0.68 for DSC, 0.66 vs. 0.70 for pixel-wise recall, and 0.68 vs. 0.73 for pixel-wise precision; P < 0.002 for all). The DSCs of fibrosis score, honeycombing, and reticulation significantly increased after CT conversion (0.32 vs. 0.64, 0.19 vs. 0.47, and 0.23 vs. 0.54, P < 0.002 for all), whereas those of ground-glass opacity, consolidation, and emphysema did not change significantly or decreased slightly. The radiologists' scores were significantly higher (P < 0.001) and less variable on converted CT.

CONCLUSION

CT conversion using a RouteGAN can improve the accuracy and variability of CT images obtained using different scan parameters and manufacturers in deep learning-based quantification of ILD.

Beyond Visual Interpretation: Quantitative Analysis and Artificial Intelligence in Interstitial Lung Disease Diagnosis "Expanding Horizons in Radiology"

Diffuse lung disorders (DLDs) and interstitial lung diseases (ILDs) are pathological conditions affecting the lung parenchyma and interstitial network. There are approximately 200 different entities within this category. Radiologists play an increasingly important role in diagnosing and monitoring ILDs, as they can provide non-invasive, rapid, and repeatable assessments using high-resolution computed tomography (HRCT). HRCT offers a detailed view of the lung parenchyma, resembling a low-magnification anatomical preparation from a histological perspective. The intrinsic contrast provided by air in HRCT enables the identification of even the subtlest morphological changes in the lung tissue. By interpreting the findings observed on HRCT, radiologists can make a differential diagnosis and provide a pattern diagnosis in collaboration with the clinical and functional data. The use of quantitative software and artificial intelligence (AI) further enhances the analysis of ILDs, providing an objective and comprehensive evaluation. The integration of "meta-data" such as demographics, laboratory, genomic, metabolomic, and proteomic data through AI could lead to a more comprehensive clinical and instrumental profiling beyond the human eye's capabilities.

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.

CT predictors of outcomes in patients with connective tissue disease and progressive lung fibrosis

PURPOSE

Progressive fibrosing interstitial lung disease (PF-ILD) is a phenotype defined by rapid clinical progression towards respiratory failure. While idiopathic pulmonary fibrosis is the archetype of PF-ILD, connective tissue disease associated interstitial lung disease (CTD-ILD) can also manifest as PF-ILD. Few studies have described the value of serial computed tomography (CT) in predicting clinical progression of ILD. We explore which single and serial clinical and radiographic variables, in particular serial CT variables and a novel variable, the right lower lobe anterior bronchial angle (RLL-ABA), best predict mortality, oxygen requirement, hospital admissions, and lung transplant in CTD-ILD.

METHODS

This is a single-center retrospective study of 84 patients with a history of CTD-ILD. Cox survival analysis was used to predict two endpoints, all-cause mortality and composite negative outcomes (CNO): new oxygen requirement, respiratory admission, lung transplant, and death.

RESULTS

On serial CT, change in pulmonary artery (PA) size and RLL-ABA were predictive of mortality and CNO, and change in fibrosis was predictive of mortality alone. On single CT, the extent of fibrosis, PA size, and PA to aorta ratio were predictive of mortality and CNO. Among clinical variables, oxygen requirement, forced vital capacity (FVC), change in FVC, and worsening shortness of breath were predictive of mortality and CNO, and diffusing capacity for carbon monoxide was predictive of mortality alone.

CONCLUSIONS

In addition to clinical and single CT variables, serial CT measurements such as change in extent of fibrosis, PA size, PA to aorta ratio, and RLL-ABA were predictive of mortality and CNO.

Research Progress of Respiratory Disease and Idiopathic Pulmonary Fibrosis Based on Artificial Intelligence

Machine Learning (ML) is an algorithm based on big data, which learns patterns from the previously observed data through classifying, predicting, and optimizing to accomplish specific tasks. In recent years, there has been rapid development in the field of ML in medicine, including lung imaging analysis, intensive medical monitoring, mechanical ventilation, and there is need for intubation etiology prediction evaluation, pulmonary function evaluation and prediction, obstructive sleep apnea, such as biological information monitoring and so on. ML can have good performance and is a great potential tool, especially in the imaging diagnosis of interstitial lung disease. Idiopathic pulmonary fibrosis (IPF) is a major problem in the treatment of respiratory diseases, due to the abnormal proliferation of fibroblasts, leading to lung tissue destruction. The diagnosis mainly depends on the early detection of imaging and early treatment, which can effectively prolong the life of patients. If the computer can be used to assist the examination results related to the effects of fibrosis, a timely diagnosis of such diseases will be of great value to both doctors and patients. We also previously proposed a machine learning algorithm model that can play a good clinical guiding role in early imaging prediction of idiopathic pulmonary fibrosis. At present, AI and machine learning have great potential and ability to transform many aspects of respiratory medicine and are the focus and hotspot of research. AI needs to become an invisible, seamless, and impartial auxiliary tool to help patients and doctors make better decisions in an efficient, effective, and acceptable way. The purpose of this paper is to review the current application of machine learning in various aspects of respiratory diseases, with the hope to provide some help and guidance for clinicians when applying algorithm models.

Post-COVID-19 interstitial lung disease: Insights from a machine learning radiographic model

Introduction

Post-acute sequelae of COVID-19 seem to be an emerging global crisis. Machine learning radiographic models have great potential for meticulous evaluation of post-COVID-19 interstitial lung disease (ILD).

Methods

In this multicenter, retrospective study, we included consecutive patients that had been evaluated 3 months following severe acute respiratory syndrome coronavirus 2 infection between 01/02/2021 and 12/5/2022. High-resolution computed tomography was evaluated through Imbio Lung Texture Analysis 2.1.

Results

Two hundred thirty-two (n = 232) patients were analyzed. FVC% predicted was ≥80, between 60 and 79 and <60 in 74.2% (n = 172), 21.1% (n = 49), and 4.7% (n = 11) of the cohort, respectively. DLCO% predicted was ≥80, between 60 and 79 and <60 in 69.4% (n = 161), 15.5% (n = 36), and 15.1% (n = 35), respectively. Extent of ground glass opacities was ≥30% in 4.3% of patients (n = 10), between 5 and 29% in 48.7% of patients (n = 113) and <5% in 47.0% of patients (n = 109). The extent of reticulation was ≥30%, 5-29% and <5% in 1.3% (n = 3), 24.1% (n = 56), and 74.6% (n = 173) of the cohort, respectively. Patients (n = 13, 5.6%) with fibrotic lung disease and persistent functional impairment at the 6-month follow-up received antifibrotics and presented with an absolute change of +10.3 (p = 0.01) and +14.6 (p = 0.01) in FVC% predicted at 3 and 6 months after the initiation of antifibrotic.

Conclusion

Post-COVID-19-ILD represents an emerging entity. A substantial minority of patients presents with fibrotic lung disease and might experience benefit from antifibrotic initiation at the time point that fibrotic-like changes are "immature." Machine learning radiographic models could be of major significance for accurate radiographic evaluation and subsequently for the guidance of therapeutic approaches.

Unsupervised machine learning identifies predictive progression markers of IPF

OBJECTIVES

To identify and evaluate predictive lung imaging markers and their pathways of change during progression of idiopathic pulmonary fibrosis (IPF) from sequential data of an IPF cohort. To test if these imaging markers predict outcome.

METHODS

We studied radiological disease progression in 76 patients with IPF, including overall 190 computed tomography (CT) examinations of the chest. An algorithm identified candidates for imaging patterns marking progression by computationally clustering visual CT features. A classification algorithm selected clusters associated with radiological disease progression by testing their value for recognizing the temporal sequence of examinations. This resulted in radiological disease progression signatures, and pathways of lung tissue change accompanying progression observed across the cohort. Finally, we tested if the dynamics of marker patterns predict outcome, and performed an external validation study on a cohort from a different center.

RESULTS

Progression marker patterns were identified and exhibited high stability in a repeatability experiment with 20 random sub-cohorts of the overall cohort. The 4 top-ranked progression markers were consistently selected as most informative for progression across all random sub-cohorts. After spatial image registration, local tracking of lung pattern transitions revealed a network of tissue transition pathways from healthy to a sequence of disease tissues. The progression markers were predictive for outcome, and the model achieved comparable results on a replication cohort.

CONCLUSIONS

Unsupervised learning can identify radiological disease progression markers that predict outcome. Local tracking of pattern transitions reveals pathways of radiological disease progression from healthy lung tissue through a sequence of diseased tissue types.

KEY POINTS

• Unsupervised learning can identify radiological disease progression markers that predict outcome in patients with idiopathic pulmonary fibrosis. • Local tracking of pattern transitions reveals pathways of radiological disease progression from healthy lung tissue through a sequence of diseased tissue types. • The progression markers achieved comparable results on a replication cohort.

Effects of Automatic Deep-Learning-Based Lung Analysis on Quantification of Interstitial Lung Disease: Correlation with Pulmonary Function Test Results and Prognosis

We investigated the feasibility of a new deep-learning (DL)-based lung analysis method for the evaluation of interstitial lung disease (ILD) by comparing it with evaluation using the traditional computer-aided diagnosis (CAD) system and patients’ clinical outcomes. We prospectively included 104 patients (84 with and 20 without ILD). An expert radiologist defined regions of interest in the typical areas of normal, ground-glass opacity, consolidation, consolidation with fibrosis (traction bronchiectasis), honeycombing, reticulation, traction bronchiectasis, and emphysema, and compared them with the CAD and DL-based analysis results. Next, we measured the extent of ILD lesions with the CAD and DL-based analysis and compared them. Finally, we compared the lesion extent on computed tomography (CT) images, as measured with the DL-based analysis, with pulmonary function tests results and patients’ overall survival. Pearson’s correlation analysis revealed a significant correlation between DL-based analysis and CAD results. Forced vital capacity was significantly correlated with DL-based analysis (r = 0.789, p < 0.001 for normal lung volume and r = −0.316, p = 0.001 for consolidation with fibrosis volume). Consolidation with fibrosis measured using DL-based analysis was independently associated with poor survival. The lesion extent measured using DL-based analysis showed a negative correlation with the pulmonary function test results and prognosis.

Hyperpolarized 129Xe MR Spectroscopy in the Lung Shows 1-year Reduced Function in Idiopathic Pulmonary Fibrosis

Background Idiopathic pulmonary fibrosis (IPF) is a temporally and spatially heterogeneous lung disease. Identifying whether IPF in a patient is progressive or stable is crucial for treatment regimens. Purpose To assess the role of hyperpolarized (HP) xenon 129 (129Xe) MRI measures of ventilation and gas transfer in IPF generally and as an early signature of future IPF progression. Materials and Methods In a prospective study, healthy volunteers and participants with IPF were consecutively recruited between December 2015 and August 2019 and underwent baseline HP 129Xe MRI and chest CT. Participants with IPF were followed up with forced vital capacity percent predicted (FVC%p), diffusing capacity of the lungs for carbon monoxide percent predicted (DLco%p), and clinical outcome at 1 year. IPF progression was defined as reduction in FVC%p by at least 10%, reduction in DLco%p by at least 15%, or admission to hospice care. CT and MRI were spatially coregistered and a measure of pulmonary gas transfer (red blood cell [RBC]-to-barrier ratio) and high-ventilation percentage of lung volume were compared across groups and across fibrotic versus normal-appearing regions at CT by using Wilcoxon signed rank tests. Results Sixteen healthy volunteers (mean age, 57 years ± 14 [SD]; 10 women) and 22 participants with IPF (mean age, 71 years ± 9; 15 men) were evaluated, as follows: nine IPF progressors (mean age, 72 years ± 7; five women) and 13 nonprogressors (mean age, 70 years ± 10; 11 men). Reduction of high-ventilation percent (13% ± 6.1 vs 8.2% ± 5.9; P = .03) and RBC-to-barrier ratio (0.26 ± 0.06 vs 0.20 ± 0.06; P = .03) at baseline were associated with progression of IPF. Participants with progressive disease had reduced RBC-to-barrier ratio in structurally normal-appearing lung at CT (0.21 ± 0.07 vs 0.28 ± 0.05; P = .01) but not in fibrotic regions of the lung (0.15 ± 0.09 vs 0.14 ± 0.04; P = .62) relative to the nonprogressive group. Conclusion In this preliminary study, functional measures of gas transfer and ventilation measured with xenon 129 MRI and the extent of fibrotic structure at CT were associated with idiopathic pulmonary fibrosis disease progression. Differences in gas transfer were found in regions of nonfibrotic lung. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Gleeson and Fraser in this issue.

Prediction model for patient prognosis in idiopathic pulmonary fibrosis using hybrid radiomics analysis

Objectives

To develop an imaging prognostic model for idiopathic pulmonary fibrosis (IPF) patients using hybrid auto-segmentation radiomics analysis, and compare the predictive ability between the radiomics analysis and conventional visual score methods.

Methods

Data from 72 IPF patients who had undergone CT were analyzed. In the radiomics analysis, quantitative CT analysis was performed using the semi-auto-segmentation method. In the visual method, the extent of radiologic abnormalities was evaluated and the overall percentage of lung involvement was calculated by averaging values for six lung zones. Using a training cohort of 50 cases, we generated a radiomics model and a visual score model. Subsequently, we investigated the predictive ability of these models in a testing cohort of 22 cases.

Results

Three significant prognostic factors such as contrast, Idn, and cluster shade were selected by LASSO Cox regression analysis. In the visual method, multivariate Cox regression analysis revealed that honeycombing and reticulation were significant prognostic factors. Subsequently, a predictive nomogram for prognosis in IPF patients was established using these factors. In the testing cohort, the c-index of the visual and radiomics nomograms were 0.68 and 0.74, respectively. When dividing the cohort into high-risk and low-risk groups using the median nomogram score, significant differences in overall survival (OS) in the visual and radiomics models were observed (P=0.000 and P=0.0003, respectively).

Conclusions

The prediction model with hybrid radiomics analysis had a better ability to predict OS in IPF patients than that of the visual method.

Differences in lung and lobe volumes between supine and upright computed tomography in patients with idiopathic lung fibrosis

No clinical study has compared lung or lobe volumes on computed tomography (CT) between the supine and standing positions in patients with idiopathic lung fibrosis (IPF). This study aimed to compare lung and lobe volumes between the supine and standing positions and evaluate the correlations between the supine/standing lung volumes on CT and pulmonary function in patients with IPF. Twenty-three patients with IPF underwent a pulmonary function test and both low-dose conventional (supine position) and upright CT (standing position) during inspiration breath-holds. The volumes of the total lungs and lobes were larger in the standing than in the supine position in patients with IPF (all p < 0.05). Spearman's correlation coefficients between total lung volumes on chest CT in supine/standing positions and vital capacity (VC) or forced VC (FVC) were 0.61/0.79 or 0.64/0.80, respectively. CT-based volumes on upright CT were better correlated with VC and FVC than those on supine CT. Lung and lobe volumes in the standing position may be useful biomarkers to assess disease severity or therapeutic effect in patients with IPF.

Advances and opportunities in nanoimaging agents for the diagnosis of inflammatory lung diseases

The development of rapid, noninvasive diagnostics to detect lung diseases is a great need after the COVID-2019 outbreak. The nanotechnology-based approach has improved imaging and facilitates the early diagnosis of inflammatory lung diseases. The multifunctional properties of nanoprobes enable better spatial-temporal resolution and a high signal-to-noise ratio in imaging. Targeted nanoimaging agents have been used to bind specific tissues in inflammatory lungs for early-stage diagnosis. However, nanobased imaging approaches for inflammatory lung diseases are still in their infancy. This review provides a solution-focused approach to exploring medical imaging technologies and nanoprobes for the detection of inflammatory lung diseases. Prospects for the development of contrast agents for lung disease detection are also discussed.

Evaluation of Interstitial Lung Disease in Idiopathic Inflammatory Myopathies Through Semiquantitative and Quantitative Analysis of Lung Computed Tomography

PURPOSE

To perform a semiquantitative and quantitative analysis of interstitial lung disease (ILD), through computed tomography (CT), in different serological subgroups of idiopathic inflammatory myopathies (IIM) patients, to find radiologic and clinical differences of disease related to serology.

MATERIALS AND METHODS

This was a prospective study, which included 98 IIM patients, divided into serological subgroups: anti-aminoacyl-transfer-RNA-synthetases (anti-ARS) positive and myositis-specific autoantibodies (MSA) negative.For each baseline CT the total semiquantitative score of Warrick (WS) and the automated software (Computer-Aided Lung Informatics for Pathology Evaluation and Rating) quantitative scores interstitial lung disease % (ILD%) and vascular-related structure % (VRS%) were calculated. Pulmonary function tests included total lung capacity % (TLC%), forced vital capacity % (FVC%), and diffusing capacity of the lung for carbon monoxide % (DLCO%).

RESULTS

Inverse correlations ( P <0.001) between the radiologic scores and the functional scores DLCO% and TLC% were found, the most relevant being between ILD% and DLCO% (ρ=-0.590), VRS% and DLCO% (ρ=-0.549), and WS and DLCO% (ρ=-0.471).Positive correlations between ILD% and VRS% (ρ=0.916; P <0.001), WS and ILD% (ρ=0.663; ρ<0.001), and WS and VRS% (ρ=0.637; P <0.001) were obtained.Statistically significant higher values of WS, ILD%, and VRS% were found in the anti-ARS group (WS=15; ILD%=11; VRS%=3.5) compared with the MSA negative one (WS=2.5; ILD%=0.84; VRS%=2.2).The nonspecific interstitial pneumonia pattern was dominant. No statistically significant differences emerged at pulmonary function tests.

CONCLUSIONS

In this study, ILD in anti-ARS-positive and MSA-negative groups was defined through semiquantitative and quantitative analysis of lung CT. The inverse correlations between the radiologic scores and TLC% and DLCO% ( P <0.001) confirm the role of lung CT in the evaluation of ILD in IIM.

Quantitative computed tomography and machine learning: recent data in fibrotic interstitial lung disease and potential role in pulmonary sarcoidosis

PURPOSE OF REVIEW

The aim of this study was to summarize quantitative computed tomography (CT) and machine learning data in fibrotic lung disease and to explore the potential application of these technologies in pulmonary sarcoidosis.

RECENT FINDINGS

Recent data in the use of quantitative CT in fibrotic interstitial lung disease (ILD) are covered. Machine learning includes deep learning, a branch of machine learning particularly suited to medical imaging analysis. Deep learning imaging biomarker research in ILD is currently undergoing accelerated development, driven by technological advances in image processing and analysis. Fundamental concepts and goals related to deep learning imaging research in ILD are discussed. Recent work highlighted in this review has been performed in patients with idiopathic pulmonary fibrosis (IPF). Quantitative CT and deep learning have not been applied to pulmonary sarcoidosis, although there are recent deep learning data in cardiac sarcoidosis.

SUMMARY

Pulmonary sarcoidosis presents unsolved problems for which quantitative CT and deep learning may provide unique solutions: in particular, the exploration of the long-standing question of whether sarcoidosis should be viewed as a single disease or as an umbrella term for disorders that might usefully be considered as separate diseases.

How Artificial Intelligence in Imaging Can Better Serve Patients with Bronchial and Parenchymal Lung Diseases?

With the rapid development of computing today, artificial intelligence has become an essential part of everyday life, with medicine and lung health being no exception. Big data-based scientific research does not mean simply gathering a large amount of data and letting the machines do the work by themselves. Instead, scientists need to identify problems whose solution will have a positive impact on patients’ care. In this review, we will discuss the role of artificial intelligence from both physiological and anatomical standpoints, starting with automatic quantitative assessment of anatomical structures using lung imaging and considering disease detection and prognosis estimation based on machine learning. The evaluation of current strengths and limitations will allow us to have a broader view for future developments.

Quantitative Computed Tomography: What Clinical Questions Can it Answer in Chronic Lung Disease?

Quantitative computed tomography (QCT) has recently gained an important role in the functional assessment of chronic lung disease. Its capacity in diagnostic, staging, and prognostic evaluation in this setting is similar to that of traditional pulmonary function testing. Furthermore, it can demonstrate lung injury before the alteration of pulmonary function test parameters, and it enables the classification of disease phenotypes, contributing to the customization of therapy and performance of comparative studies without the intra- and inter-observer variation that occurs with qualitative analysis. In this review, we address technical issues with QCT analysis and demonstrate the ability of this modality to answer clinical questions encountered in daily practice in the management of patients with chronic lung disease.

A comprehensible machine learning tool to differentially diagnose idiopathic pulmonary fibrosis from other chronic interstitial lung diseases

BACKGROUND AND OBJECTIVE

Idiopathic pulmonary fibrosis (IPF) has poor prognosis, and the multidisciplinary diagnostic agreement is low. Moreover, surgical lung biopsies pose comorbidity risks. Therefore, using data from non-invasive tests usually employed to assess interstitial lung diseases (ILDs), we aimed to develop an automated algorithm combining deep learning and machine learning that would be capable of detecting and differentiating IPF from other ILDs.

METHODS

We retrospectively analysed consecutive patients presenting with ILD between April 2007 and July 2017. Deep learning was used for semantic image segmentation of HRCT based on the corresponding labelled images. A diagnostic algorithm was then trained using the semantic results and non-invasive findings. Diagnostic accuracy was assessed using five-fold cross-validation.

RESULTS

In total, 646,800 HRCT images and the corresponding labelled images were acquired from 1068 patients with ILD, of whom 42.7% had IPF. The average segmentation accuracy was 96.1%. The machine learning algorithm had an average diagnostic accuracy of 83.6%, with high sensitivity, specificity and kappa coefficient values (80.7%, 85.8% and 0.665, respectively). Using Cox hazard analysis, IPF diagnosed using this algorithm was a significant prognostic factor (hazard ratio, 2.593; 95% CI, 2.069-3.250; p < 0.001). Diagnostic accuracy was good even in patients with usual interstitial pneumonia patterns on HRCT and those with surgical lung biopsies.

CONCLUSION

Using data from non-invasive examinations, the combined deep learning and machine learning algorithm accurately, easily and quickly diagnosed IPF in a population with various ILDs.

Filtration-histogram based texture analysis and CALIPER based pattern analysis as quantitative CT techniques in idiopathic pulmonary fibrosis: head-to-head comparison

OBJECTIVE

To assess the prognostic performance of two quantitative CT (qCT) techniques in idiopathic pulmonary fibrosis (IPF) compared to established clinical measures of disease severity (GAP index).

METHODS

Retrospective analysis of high-resolution CT scans for 59 patients (age 70.5 ± 8.8 years) with two qCT methods. Computer-aided lung informatics for pathology evaluation and ratings based analysis classified the lung parenchyma into six different patterns: normal, ground glass, reticulation, hyperlucent, honeycombing and pulmonary vessels. Filtration histogram-based texture analysis extracted texture features: mean intensity, standard deviation (SD), entropy, mean of positive pixels (MPPs), skewness and kurtosis at different spatial scale filters. Univariate Kaplan-Meier survival analysis assessed the different qCT parameters' performance to predict patient outcome and refine the standard GAP staging system. Multivariate cox regression analysis assessed the independence of the significant univariate predictors of patient outcome.

RESULTS

The predominant parenchymal lung pattern was reticulation (16.6% ± 13.9), with pulmonary vessel percentage being the most predictive of worse patient outcome (p = 0.009). Higher SD, entropy and MPP, in addition to lower skewness and kurtosis at fine texture scale (SSF2), were the most significant predictors of worse outcome (p < 0.001). Multivariate cox regression analysis demonstrated that SD (SSF2) was the only independent predictor of survival (p < 0.001). Better patient outcome prediction was achieved after adding total vessel percentage and SD (SSF2) to the GAP staging system (p = 0.006).

CONCLUSION

Filtration-histogram texture analysis can be an independent predictor of patient mortality in IPF patients.

ADVANCES IN KNOWLEDGE

qCT analysis can help in risk stratifying IPF patients in addition to clinical markers.

The Current and Future Role of Technology in Respiratory Care

Over the past few decades, technology and improvements in artificial intelligence have dramatically changed major sectors of our day-to-day lives, including the field of healthcare. E-health includes a wide range of subdomains, such as wearables, smart-inhalers, portable electronic spirometers, digital stethoscopes, and clinical decision support systems. E-health has been consistently shown to enhance the quality of care, improve adherence to therapy, and allow early detection of worsening in chronic pulmonary diseases. The present review addresses the current and potential future role of major e-health tools and approaches in respiratory medicine, with the aim of providing readers with trustful and updated evidence to increase their awareness of the topic, and to allow them to optimally benefit from the latest innovation technology. Collected literature evidence shows that the potential of technology tools in respiratory medicine mainly relies on three fundamental interactions: between clinicians, between clinician and patient, and between patient and health technology. However, it would be desirable to establish widely agreed and adopted standards for conducting trials and reporting results in this area, as well as to take into proper consideration potentially relevant pitfalls related to privacy protection and compliance with regulatory procedures.

Lung MRI with hyperpolarised gases: current & future clinical perspectives

The use of pulmonary MRI in a clinical setting has historically been limited. Whilst CT remains the gold-standard for structural lung imaging in many clinical indications, technical developments in ultrashort and zero echo time MRI techniques are beginning to help realise non-ionising structural imaging in certain lung disorders. In this invited review, we discuss a complementary technique - hyperpolarised (HP) gas MRI with inhaled ³He and ¹²⁹Xe - a method for functional and microstructural imaging of the lung that has great potential as a clinical tool for early detection and improved understanding of pathophysiology in many lung diseases. HP gas MRI now has the potential to make an impact on clinical management by enabling safe, sensitive monitoring of disease progression and response to therapy. With reference to the significant evidence base gathered over the last two decades, we review HP gas MRI studies in patients with a range of pulmonary disorders, including COPD/emphysema, asthma, cystic fibrosis, and interstitial lung disease. We provide several examples of our experience in Sheffield of using these techniques in a diagnostic clinical setting in challenging adult and paediatric lung diseases.

Serial decline in lung volume parameters on computed tomography (CT) predicts outcome in idiopathic pulmonary fibrosis (IPF)

OBJECTIVES

In patients with IPF, this study aimed (i) to examine the relationship between serial change in CT parameters of lung volume and lung function, (ii) to identify the prognostic value of serial change in CT parameters of lung volume, and (iii) to define a threshold for serial change in CT markers of lung volume that optimally captures disease progression.

METHODS

Serial CTs were analysed for progressive volume loss or fibrosis progression in 81 IPF patients (66 males, median age = 67 years) with concurrent forced vital capacity (FVC) (median follow-up 12 months, range 6-23 months). Serial CT measurements of volume loss comprised oblique fissure posterior retraction distance (OFPRD), aortosternal distance (ASD), lung height corrected for body habitus (LH), and automated CT-derived total lung volumes (ALV) (measured using commercially available software). Fibrosis progression was scored visually. Serial changes in CT markers and FVC were compared using regression analysis, and evaluated against mortality using Cox proportional hazards.

RESULTS

There were 58 deaths (72%, median survival = 17 months). Annual % change in ALV was most significantly related to annual % change in FVC (R2 = 0.26, p < 0.0001). On multivariate analysis, annual % change in ASD predicted mortality (HR = 0.97, p < 0.001), whereas change in FVC did not. A 25% decline in annual % change in ASD best predicted mortality, superior to 10% decline in FVC and fibrosis progression.

CONCLUSION

In IPF, serial decline in CT markers of lung volume and, specifically, annualised 25% reduction in aortosternal distance provides evidence of disease progression, not always identified by FVC trends or changes in fibrosis extent.

KEY POINTS

• Serial decline in automated and surrogate markers of lung volume on CT corresponds to changes in FVC. • Annualised reductions in the distance between ascending aorta and posterior border of the sternum on CT predict mortality beyond annualised percentage change in FVC.

The role of artificial intelligence in assessing the progression of fibrosing lung diseases

Introduction. The widespread use of artificial intelligence (AI) programs during the COVID-19 pandemic to assess the exact volume of lung tissue damage has allowed them to train a large number of radiologists. The simplicity of the program for determining the volume of the affected lung tissue in acute interstitial pneumonia, which has density indicators in the range from -200 HU to -730 HU, which includes the density indicators of "ground glass" and reticulation (the main radiation patterns in COVID-19) allows you to accurately determine the degree of prevalence process. The characteristics of chronic interstitial pneumonia, which are progressive in nature, fit into the same density framework. Аim. To аssess AI's ability to assess the progression of fibrosing lung disease using lung volume counting programs used for COVID-19 and chronic obstructive pulmonary disease. Results. Retrospective analysis of computed tomography data during follow-up of 75 patients with progressive fibrosing lung disease made it possible to assess the prevalence and growth of interstitial lesions. Conclusion. Using the experience of using AI programs to assess acute interstitial pneumonia in COVID-19 can be applied to chronic interstitial pneumonia.

Quantitative evaluation of disease severity in connective tissue disease-associated interstitial lung disease by dual-energy computed tomography

Background

High-resolution computed tomography (HRCT) is recommended diagnosing and monitoring connective tissue disease-associated interstitial lung disease (CTD-ILD). Quantitative computed tomography has the potential to precisely assess the radiological severity of CTD-ILD, but has still been under study.

Objective

To investigate whether dual-energy computed tomography (DECT), a novel quantitative technique, can be used for quantitative severity assessment in CTD-ILD.

Methods

This cross sectional study recruited adult CTD-ILD patients who underwent DECT scans from the ICE study between October 2019 and November 2021. DECT parameters, including effective atomic number (Z_eff), lung (lobe) volume, and monochromatic CT number (MCTN) of each lung lobe, were evaluated. CTD-ILD was classified into extensive CTD-ILD and limited CTD-ILD by staging algorithm using combined forced vital capacity (FVC)%predicted and total extent of ILD (TEI) on CT. Dyspnea, cough, and life quality were scored by Borg dyspnea score, Leicester cough questionnaire (LCQ), and short-form 36 health survey questionnaire (SF-36), respectively.

Results

There was a total of 147 patients with DECT scans enrolled. Higher Z_eff value (3.104 vs 2.256, p < 0.001), higher MCTN (− 722.87 HU vs − 802.20 HU, p < 0.001), and lower lung volume (2309.51cm³ vs 3475.21cm³, p < 0.001) were found in extensive CTD-ILD compared with limited CTD-ILD. DECT parameters had significant moderate correlations with FVC%predicted (|r|= 0.542–0.667, p < 0.01), DLCO%predicted (|r|= 0.371–0.427, p < 0.01), and TEI (|r|= 0.485–0.742, p < 0.01). Receiver operating characteristic (ROC) analysis indicated MCTN averaged over the whole lung had the best performance for extensive CTD-ILD discrimination (AUC = 0.901, cut-off: − 762.30 HU, p < 0.001), with a sensitivity of 82.1% and a specificity of 85.4%. The Z_eff value was the independent risk factor for dyspnea (OR = 3.644, 95% CI: 1.846–7.192, p < 0.001) and cough (OR = 3.101, 95% CI: 1.528–6.294, p = 0.002), and lung volume significantly contributed to the mental component summary (MCS) in SF-36 (standardized β = 0.198, p < 0.05).

Conclusions

DECT can be applied to evaluate the severity of CTD-ILD.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12931-022-01972-4.

A Novel and Automated Approach to Classify Radiation Induced Lung Tissue Damage on CT Scans

Radiation-induced lung damage (RILD) is a common side effect of radiotherapy (RT). The ability to automatically segment, classify, and quantify different types of lung parenchymal change is essential to uncover underlying patterns of RILD and their evolution over time. A RILD dedicated tissue classification system was developed to describe lung parenchymal tissue changes on a voxel-wise level. The classification system was automated for segmentation of five lung tissue classes on computed tomography (CT) scans that described incrementally increasing tissue density, ranging from normal lung (Class 1) to consolidation (Class 5). For ground truth data generation, we employed a two-stage data annotation approach, akin to active learning. Manual segmentation was used to train a stage one auto-segmentation method. These results were manually refined and used to train the stage two auto-segmentation algorithm. The stage two auto-segmentation algorithm was an ensemble of six 2D Unets using different loss functions and numbers of input channels. The development dataset used in this study consisted of 40 cases, each with a pre-radiotherapy, 3-, 6-, 12-, and 24-month follow-up CT scans (n = 200 CT scans). The method was assessed on a hold-out test dataset of 6 cases (n = 30 CT scans). The global Dice score coefficients (DSC) achieved for each tissue class were: Class (1) 99% and 98%, Class (2) 71% and 44%, Class (3) 56% and 26%, Class (4) 79% and 47%, and Class (5) 96% and 92%, for development and test subsets, respectively. The lowest values for the test subsets were caused by imaging artefacts or reflected subgroups that occurred infrequently and with smaller overall parenchymal volumes. We performed qualitative evaluation on the test dataset presenting manual and auto-segmentation to a blinded independent radiologist to rate them as 'acceptable', 'minor disagreement' or 'major disagreement'. The auto-segmentation ratings were similar to the manual segmentation, both having approximately 90% of cases rated as acceptable. The proposed framework for auto-segmentation of different lung tissue classes produces acceptable results in the majority of cases and has the potential to facilitate future large studies of RILD.