This Review provides an updated approach to the diagnosis of idiopathic pulmonary fibrosis (IPF), based on a systematic search of the medical literature and the expert opinion of members of the ...Fleischner Society. A checklist is provided for the clinical evaluation of patients with suspected usual interstitial pneumonia (UIP). The role of CT is expanded to permit diagnosis of IPF without surgical lung biopsy in select cases when CT shows a probable UIP pattern. Additional investigations, including surgical lung biopsy, should be considered in patients with either clinical or CT findings that are indeterminate for IPF. A multidisciplinary approach is particularly important when deciding to perform additional diagnostic assessments, integrating biopsy results with clinical and CT features, and establishing a working diagnosis of IPF if lung tissue is not available. A working diagnosis of IPF should be reviewed at regular intervals since the diagnosis might change. Criteria are presented to establish confident and working diagnoses of IPF.
CT is increasingly being used to stage and quantify the extent of diffuse lung diseases both in clinical practice and in treatment trials. The role of CT in the assessment of patients entering ...treatment trials has greatly expanded as clinical researchers and pharmaceutical companies have focused their efforts on developing safe and effective drugs for interstitial lung diseases, particularly for idiopathic pulmonary fibrosis. These efforts have culminated in the simultaneous approval by the US Food and Drug Administration of two new drugs for the treatment of idiopathic pulmonary fibrosis. CT features are a key part of the inclusion criteria in many drug trials and CT is now being used to refine the type of patients enrolled. Interest in the potential use of serial CT as an effectiveness endpoint is increasing. For chronic progressive diseases, mortality may not be a feasible endpoint and many surrogate markers have been explored, ranging from pulmonary function decline to biomarkers. However, these surrogate markers are not entirely reliable and combinations of endpoints, including change in disease extent on CT, are being investigated. Methods to assess disease severity with CT range from simple visual estimates to sophisticated quantification by use of software. In this Position Paper, which cannot be regarded as a comprehensive set of guidelines in view of present knowledge, we examine the uses of serial CT in clinical practice and in drug trials and draw attention to uncertainties and challenges for future research.
In April 2023, the first American Roentgen Ray Society (ARRS) Wellness Summit was held in Honolulu, Hawaii. The Summit was a communal call to action bringing together professionals from the field of ...radiology to critically review our current state of wellness and reimagine the role of radiology and radiologists to further wellbeing. The in-person and virtual Summit was available free-of-cost to all meeting registrants and included 12 sessions with 44 invited moderators and panelists. The Summit aimed to move beyond simply rehashing the repeated issues and offering theoretical solutions, and instead focus on intentional practice evolution, identifying implementable strategies so that we as a field can start to walk our wellness talk. Here, we first summarize the thematic discussions from the 2023 ARRS Wellness Summit, and second, share several strategic action items that emerged.
The Scleroderma Lung Study II (SLS II) demonstrated significant improvements in pulmonary function and dyspnea at 24 months compared with baseline when patients with symptomatic scleroderma-related ...interstitial lung disease (SSc-ILD) were treated with either cyclophosphamide for 1 year (followed for another year on placebo) or mycophenolate mofetil for 2 years in a randomized, double-blind clinical trial. Physiologic and clinical outcomes of SLS II have been published previously.
The aim of the study was to assess changes from baseline in the extent of SSc-ILD on high-resolution computed tomography (HRCT) measured in the SLS II participants using quantitative image analysis after 2 years and to determine whether these HRCT changes were correlated with the changes in physiologic and clinical measures over the same time interval.
Ninety-seven of the 142 randomized subjects (cyclophosphamide group, 47 subjects; mycophenolate mofetil group, 50 subjects) participating in SLS II underwent thoracic volumetric thin-section HRCT at both baseline and 24 months. Quantitative computer-aided diagnosis scores using volumetric HRCT scans were obtained using a previously developed computer-aided system. The scores were quantitative lung fibrosis, quantitative ground glass, quantitative honeycomb, and quantitative interstitial lung disease (QILD), the latter representing the sum of quantitative lung fibrosis, quantitative ground glass, and quantitative honeycomb. These scores were obtained both for the whole lung and for individual lobes. Paired t tests were used for the combined (pooled) cyclophosphamide and mycophenolate mofetil groups to compare 24-month changes from baseline in both the whole lung and the lobe of maximal involvement as determined at baseline (worst lobe).
At the end of the 24-month trial, QILD in the whole lung was significantly reduced by a mean of 2.51% in the pooled groups (adjusted 95% confidence interval, -4.00 to -1.03%; P = 0.001). There was no significant difference in the QILD score improvement between the cyclophosphamide (-2.66%) and mycophenolate (-2.38%) groups when assessed separately (P = 0.88). For the pooled group, the 24-month changes in QILD scores in the whole lung correlated significantly with other outcomes, including 24-month changes in forced vital capacity (ρ = -0.37), single-breath diffusing capacity of the lung for carbon monoxide (ρ = -0.22), and breathlessness as measured by the Transition Dyspnea Index (ρ = -0.26).
Treatment of SSc-ILD with either cyclophosphamide for 1 year, followed by placebo for a second year, or mycophenolate for 2 years was associated with a significant reduction (improvement) in the extent of HRCT SSc-ILD assessed by computer-aided diagnosis scores, which correlated well with one or more other measures of treatment response. These findings demonstrate that actual changes in lung structure accompany improvements in physiologic and/or symptomatic measures in SSc-ILD.
Median survival of patients with idiopathic pulmonary fibrosis (IPF) is 2-5 years. Sensitive imaging metrics can play a role in detecting early changes in therapeutic development. The aim of the ...present study was to compare known computed tomography (CT) histogram kurtosis and a classifier-based quantitative score to assess baseline severity and change over time in patients with IPF.
A total of 57 patients with at least baseline and paired follow-up scans were selected from an imaging database of standardized CT scans obtained from patients with IPF. CT histogram measurement of kurtosis and quantitative lung fibrosis (QLF) and quantitative interstitial lung disease (QILD) scores from a classification algorithm were calculated. Spearman rank correlations were used to assess associations between baseline severity and changes for all CT-derived measures compared to forced vital capacity (FVC) and carbon monoxide diffusion capacity (DLCO) (percent predicted).
At baseline, mean (±SD) of kurtosis was 2.43 (±1.83). Mean (±SD) values of QLF and QILD scores were 20.7% (±13.4) and 43.3% (±20.0), respectively. All baseline histogram indices and QLF and QILD scores were correlated well with baseline FVC and DLCO. When assessing associations with changes in FVC and DLCO over time, only QLF score was statistically significant (ρ = -0.57; P < .0001 for FVC and ρ = -0.34; P = .025 for DLCO), whereas kurtosis was not.
Classifier-model-derived scores (QLF and QILD), based on a set of texture features, are associated with baseline disease extent and are also a sensitive measure of change over time. A QLF score can be used for measuring the extent of disease severity and longitudinal changes.
Objective
To examine changes in the extent of specific patterns of interstitial lung disease (ILD) as they transition from one pattern to another in response to immunosuppressive therapy in systemic ...sclerosis–related ILD (SSc‐ILD).
Methods
We evaluated changes in the quantitative extent of specific lung patterns of ILD using volumetric high‐resolution computed tomography (HRCT) scans obtained at baseline and after 2 years of therapy in patients treated with either cyclophosphamide (CYC) for 1 year or mycophenolate mofetil (MMF) for 2 years in Scleroderma Lung Study II. ILD patterns included lung fibrosis, ground glass, honeycombing, and normal lung. Net change was calculated as the difference in the probability of change from one ILD pattern to another. Wilcoxon's signed rank test was used to compare the changes.
Results
Forty‐seven and 50 patients had baseline and follow‐up scans in the CYC and MMF groups, respectively. Mean net improvements reflecting favorable changes from one ILD pattern to another in the whole lung in the CYC and MMF groups, respectively, were as follows: from lung fibrosis to a normal lung pattern, 21% and 19%; from a ground‐glass pattern to a normal lung pattern, 30% and 28%; and from lung fibrosis to a ground‐glass pattern, 5% and 0.5%. The mean overall improvement in transitioning from a ground‐glass pattern or lung fibrosis to a normal lung pattern was significant for both treatments (all P < 0.001).
Conclusion
Significantly favorable transitions from both ground‐glass and lung fibrosis ILD patterns to a normal lung pattern were observed in patients undergoing immunosuppressive treatment for SSc‐ILD, suggesting the usefulness of examining these transitions for insights into the underlying pathobiology of treatment response.
Significant heterogeneity of clinical presentation and disease progression exists within chronic obstructive pulmonary disease (COPD). Although FEV(1) inadequately describes this heterogeneity, a ...clear alternative has not emerged. The goal of phenotyping is to identify patient groups with unique prognostic or therapeutic characteristics, but significant variation and confusion surrounds use of the term "phenotype" in COPD. Phenotype classically refers to any observable characteristic of an organism, and up until now, multiple disease characteristics have been termed COPD phenotypes. We, however, propose the following variation on this definition: "a single or combination of disease attributes that describe differences between individuals with COPD as they relate to clinically meaningful outcomes (symptoms, exacerbations, response to therapy, rate of disease progression, or death)." This more focused definition allows for classification of patients into distinct prognostic and therapeutic subgroups for both clinical and research purposes. Ideally, individuals sharing a unique phenotype would also ultimately be determined to have a similar underlying biologic or physiologic mechanism(s) to guide the development of therapy where possible. It follows that any proposed phenotype, whether defined by symptoms, radiography, physiology, or cellular or molecular fingerprint will require an iterative validation process in which "candidate" phenotypes are identified before their relevance to clinical outcome is determined. Although this schema represents an ideal construct, we acknowledge any phenotype may be etiologically heterogeneous and that any one individual may manifest multiple phenotypes. We have much yet to learn, but establishing a common language for future research will facilitate our understanding and management of the complexity implicit to this disease.
Purpose
Domain knowledge (DK) acquired from prior studies is important for medical diagnosis. This paper leverages the population‐level DK using an optimality design criterion to train a deep ...learning model in an end‐to‐end manner. In this study, the problem of interest is at the patient level to diagnose a subject with idiopathic pulmonary fibrosis (IPF) among subjects with interstitial lung disease (ILD) using a computed tomography (CT). IPF diagnosis is a complicated process with multidisciplinary discussion with experts and is subject to interobserver variability, even for experienced radiologists. To this end, we propose a new statistical method to construct a time/memory‐efficient IPF diagnosis model using axial chest CT and DK, along with an optimality design criterion via a DK‐enhanced loss function of deep learning.
Methods
Four state‐of‐the‐art two‐dimensional convolutional neural network (2D‐CNN) architectures (MobileNet, VGG16, ResNet‐50, and DenseNet‐121) and one baseline 2D‐CNN are implemented to automatically diagnose IPF among ILD patients. Axial lung CT images are retrospectively acquired from 389 IPF patients and 700 non‐IPF ILD patients in five multicenter clinical trials. To enrich the sample size and boost model performance, we sample 20 three‐slice samples (triplets) from each CT scan, where these three slices are randomly selected from the top, middle, and bottom of both lungs respectively. Model performance is evaluated using a fivefold cross‐validation, where each fold was stratified using a fixed proportion of IPF vs non‐IPF.
Results
Using DK‐enhanced loss function increases the model performance of the baseline CNN model from 0.77 to 0.89 in terms of study‐wise accuracy. Four other well‐developed models reach satisfactory model performance with an overall accuracy >0.95 but the benefits brought on by the DK‐enhanced loss function is not noticeable.
Conclusions
We believe this is the first attempt that (a) uses population‐level DK with an optimal design criterion to train deep learning‐based diagnostic models in an end‐to‐end manner and (b) focuses on patient‐level IPF diagnosis. Further evaluation of using population‐level DK on prospective studies is warranted and is underway.
To develop artificial intelligence (AI) system that assists in checking endotracheal tube (ETT) placement on chest X-rays (CXRs) and evaluate whether it can move into clinical validation as a quality ...improvement tool.
A retrospective data set including 2000 de-identified images from intensive care unit patients was split into 1488 for training and 512 for testing. AI was developed to automatically identify the ETT, trachea, and carina using semantically embedded neural networks that combine a declarative knowledge base with deep neural networks. To check the ETT tip placement, a “safe zone” was computed as the region inside the trachea and 3–7 cm above the carina. Two AI outputs were evaluated: (1) ETT overlay, (2) ETT misplacement alert messages. Clinically relevant performance metrics were compared against prespecified thresholds of >85% overlay accuracy and positive predictive value (PPV) > 30% and negative predictive value NPV > 95% for alerts to move into clinical validation.
An ETT was present in 285 of 512 test cases. The AI detected 95% (271/285) of ETTs, 233 (86%) of these with accurate tip localization. The system (correctly) did not generate an ETT overlay in 221/227 CXRs where the tube was absent for an overall overlay accuracy of 89% (454/512). The alert messages indicating that either the ETT was misplaced or not detected had a PPV of 83% (265/320) and NPV of 98% (188/192).
The chest X-ray AI met prespecified performance thresholds to move into clinical validation.
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