Artificial Intelligence for Interstitial Lung Disease Analysis on Chest Computed Tomography: A Systematic Review

Current challenges in the diagnosis and management of idiopathic pulmonary fibrosis in Japan

Idiopathic pulmonary fibrosis (IPF) is the archetypal interstitial lung disease. It is a chronic progressive condition that is challenging to manage as the clinical course of the disease is often difficult to predict. The prevalence of IPF is rising globally and in Japan, where it is estimated to affect 27 individuals per 100,000 of the population. Greater patient numbers and the poor prognosis associated with IPF diagnosis mean that there is a growing need for disease management approaches that can slow or even reverse disease progression and improve survival. Considerable progress has been made in recent years, with the approval of two antifibrotic therapies for IPF (pirfenidone and nintedanib), the availability of Japanese treatment guidelines, and the creation of global and Japanese disease registries. Despite this, significant unmet needs remain with respect to the diagnosis, treatment, and management of this complex disease. Each of these challenges will be discussed in this review, including making a timely and differential diagnosis of IPF, uptake and adherence to antifibrotic therapy, patient access to pulmonary rehabilitation, lung transplantation and palliative care, and optimal strategies for monitoring and staging disease progression, with a particular focus on the status in Japan. In addition, the review will reflect upon how ongoing research, clinical trials of novel therapies, and technologic advancements (including artificial intelligence, biomarkers, and genomic classification) may help address these challenges in the future.

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.

Analysis of Validation Performance of a Machine Learning Classifier in Interstitial Lung Disease Cases Without Definite or Probable Usual Interstitial Pneumonia Pattern on CT Using Clinical and Pathology-Supported Diagnostic Labels

We previously validated Fibresolve, a machine learning classifier system that non-invasively predicts idiopathic pulmonary fibrosis (IPF) diagnosis. The system incorporates an automated deep learning algorithm that analyzes chest computed tomography (CT) imaging to assess for features associated with idiopathic pulmonary fibrosis. Here, we assess performance in assessment of patterns beyond those that are characteristic features of usual interstitial pneumonia (UIP) pattern. The machine learning classifier was previously developed and validated using standard training, validation, and test sets, with clinical plus pathologically determined ground truth. The multi-site 295-patient validation dataset was used for focused subgroup analysis in this investigation to evaluate the classifier's performance range in cases with and without radiologic UIP and probable UIP designations. Radiologic assessment of specific features for UIP including the presence and distribution of reticulation, ground glass, bronchiectasis, and honeycombing was used for assignment of radiologic pattern. Output from the classifier was assessed within various UIP subgroups. The machine learning classifier was able to classify cases not meeting the criteria for UIP or probable UIP as IPF with estimated sensitivity of 56-65% and estimated specificity of 92-94%. Example cases demonstrated non-basilar-predominant as well as ground glass patterns that were indeterminate for UIP by subjective imaging criteria but for which the classifier system was able to correctly identify the case as IPF as confirmed by multidisciplinary discussion generally inclusive of histopathology. The machine learning classifier Fibresolve may be helpful in the diagnosis of IPF in cases without radiological UIP and probable UIP patterns.

[The contribution of artificial intelligence (AI) subsequent to the processing of thoracic imaging]

The contribution of artificial intelligence (AI) to medical imaging is currently the object of widespread experimentation. The development of deep learning (DL) methods, particularly convolution neural networks (CNNs), has led to performance gains often superior to those achieved by conventional methods such as machine learning. Radiomics is an approach aimed at extracting quantitative data not accessible to the human eye from images expressing a disease. The data subsequently feed machine learning models and produce diagnostic or prognostic probabilities. As for the multiple applications of AI methods in thoracic imaging, they are undergoing evaluation. Chest radiography is a practically ideal field for the development of DL algorithms able to automatically interpret X-rays. Current algorithms can detect up to 14 different abnormalities present either in isolation or in combination. Chest CT is another area offering numerous AI applications. Various algorithms have been specifically formed and validated for the detection and characterization of pulmonary nodules and pulmonary embolism, as well as segmentation and quantitative analysis of the extent of diffuse lung diseases (emphysema, infectious pneumonias, interstitial lung disease). In addition, the analysis of medical images can be associated with clinical, biological, and functional data (multi-omics analysis), the objective being to construct predictive approaches regarding disease prognosis and response to treatment.

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.

A formula for predicting emphysema extent in combined idiopathic pulmonary fibrosis and emphysema

BACKGROUND

No single pulmonary function test captures the functional effect of emphysema in idiopathic pulmonary fibrosis (IPF). Without experienced radiologists, other methods are needed to determine emphysema extent. Here, we report the development and validation of a formula to predict emphysema extent in patients with IPF and emphysema.

METHODS

The development cohort included 76 patients with combined IPF and emphysema at the Royal Brompton Hospital, London, United Kingdom. The formula was derived using stepwise regression to generate the weighted combination of pulmonary function data that fitted best with emphysema extent on high-resolution computed tomography. Test cohorts included patients from two clinical trials (n = 455 [n = 174 with emphysema]; NCT00047645, NCT00075998) and a real-world cohort from the Royal Brompton Hospital (n = 191 [n = 110 with emphysema]). The formula is only applicable for patients with IPF and concomitant emphysema and accordingly was not used to detect the presence or absence of emphysema.

RESULTS

The formula was: predicted emphysema extent = 12.67 + (0.92 x percent predicted forced vital capacity) - (0.65 x percent predicted forced expiratory volume in 1 second) - (0.52 x percent predicted carbon monoxide diffusing capacity). A significant relationship between the formula and observed emphysema extent was found in both cohorts (R2 = 0.25, P < 0.0001; R2 = 0.47, P < 0.0001, respectively). In both, the formula better predicted observed emphysema extent versus individual pulmonary function tests. A 15% emphysema extent threshold, calculated using the formula, identified a significant difference in absolute changes from baseline in forced vital capacity at Week 48 in patients with baseline-predicted emphysema extent < 15% versus ≥ 15% (P = 0.0105).

CONCLUSION

The formula, designed for use in patients with IPF and emphysema, demonstrated enhanced ability to predict emphysema extent versus individual pulmonary function tests.

TRIAL REGISTRATION

NCT00047645; NCT00075998.

A Novel Automatic Algorithm to Support Lung Ultrasound Non-Expert Physicians in Interstitial Pneumonia Evaluation: A Single-Center Study

INTRODUCTION

Lung ultrasound (LUS) is widely used in clinical practice for identifying interstitial lung diseases (ILDs) and assessing their progression. Although high-resolution computed tomography (HRCT) remains the gold standard for evaluating the severity of ILDs, LUS can be performed as a screening method or as a follow-up tool post-HRCT. Minimum training is needed to better identify typical lesions, and the integration of innovative artificial intelligence (AI) automatic algorithms may enhance diagnostic efficiency.

AIM

This study aims to assess the effectiveness of a novel AI algorithm in automatic ILD recognition and scoring in comparison to an expert LUS sonographer. The "SensUS Lung" device, equipped with an automatic algorithm, was employed for the automatic recognition of the typical ILD patterns and to calculate an index grading of the interstitial involvement.

METHODS

We selected 33 Caucasian patients in follow-up for ILDs exhibiting typical HRCT patterns (honeycombing, ground glass, fibrosis). An expert physician evaluated all patients with LUS on twelve segments (six per side). Next, blinded to the previous evaluation, an untrained operator, a non-expert in LUS, performed the exam with the SensUS device equipped with the automatic algorithm ("SensUS Lung") using the same protocol. Pulmonary functional tests (PFT) and DLCO were conducted for all patients, categorizing them as having reduced or preserved DLCO. The SensUS device indicated different grades of interstitial involvement named Lung Staging that were scored from 0 (absent) to 4 (peak), which was compared to the Lung Ultrasound Score (LUS score) by dividing it by the number of segments evaluated. Statistical analyses were done with Wilcoxon tests for paired values or Mann-Whitney for unpaired samples, and correlations were performed using Spearman analysis; p < 0.05 was considered significant.

RESULTS

Lung Staging was non-inferior to LUS score in identifying the risk of ILDs (median SensUS 1 [0-2] vs. LUS 0.67 [0.25-1.54]; p = 0.84). Furthermore, the grade of interstitial pulmonary involvement detected with the SensUS device is directly related to the LUS score (r = 0.607, p = 0.002). Lung Staging values were inversely correlated with forced expiratory volume at first second (FEV1%, r = -0.40, p = 0.027), forced vital capacity (FVC%, r = -0.39, p = 0.03) and forced expiratory flow (FEF) at 25th percentile (FEF25%, r = -0.39, p = 0.02) while results directly correlated with FEF25-75% (r = 0.45, p = 0.04) and FEF75% (r = 0.43, p = 0.01). Finally, in patients with reduced DLCO, the Lung Staging was significantly higher, overlapping the LUS (reduced median 1 [1-2] vs. preserved 0 [0-1], p = 0.001), and overlapping the LUS (reduced median 18 [4-20] vs. preserved 5.5 [2-9], p = 0.035).

CONCLUSIONS

Our data suggest that the considered AI automatic algorithm may assist non-expert physicians in LUS, resulting in non-inferior-to-expert LUS despite a tendency to overestimate ILD lesions. Therefore, the AI algorithm has the potential to support physicians, particularly non-expert LUS sonographers, in daily clinical practice to monitor patients with ILDs. The adopted device is user-friendly, offering a fully automatic real-time analysis. However, it needs proper training in basic skills.

Short-term inhalation of sargramostim with concomitant high-dose steroids does not hasten recovery in moderate COVID-19 pneumonia: a double-blind, randomised, placebo-controlled trial

BACKGROUND

Granulocyte-macrophage colony stimulating factor (GM-CSF) inhalation may alleviate pulmonary inflammation caused by viral pneumonia. To investigate this, we evaluated its efficacy on COVID-19 pneumonia.

METHODS

This double-blind, randomised, placebo-controlled study (ClinicalTrials.gov: NCT04642950) evaluated patients in the first half of 2021 at seven Japanese hospitals. Hospitalised patients with COVID-19 pneumonia with moderate hypoxaemia inhaled sargramostim or placebo for 5 days. The primary endpoint was days to achieve a ≥ 2-category improvement from baseline on a modified 7-category ordinal scale. Secondary endpoints included degree of oxygenation, defined by amount of oxygen supply, and serum CCL17 level.

RESULTS

Seventy-five patients were randomly assigned in a 2:1 ratio to receive sargramostim or placebo, of which 47 and 23 were analysed, respectively. No difference was observed between groups regarding the primary endpoint (8.0 and 7.0 days for sargramostim and placebo, respectively) or in the secondary endpoints, except for CCL17. A post hoc sub-analysis indicated that endpoint assessments were influenced by concomitant corticosteroid therapy. When the cumulative corticosteroid dose was ≤500 mg during Days 1-5, recovery and oxygenation were faster in the sargramostim group than for placebo. Bolus dose corticosteroids were associated with temporarily impaired oxygenation and delayed clinical recovery. The increase in serum CCL17, a candidate prognostic factor, reflected improvement with sargramostim inhalation. The number of adverse events was similar between groups. Two serious adverse events were observed in the sargramostim group without causal relation.

CONCLUSIONS

Inhaled sargramostim was likely to be effective for COVID-19 pneumonia unless the concomitant corticosteroid dose was high.

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.

Clinical Guidance on the Monitoring and Management of Trastuzumab Deruxtecan (T-DXd)-Related Adverse Events: Insights from an Asia-Pacific Multidisciplinary Panel

Trastuzumab deruxtecan (T-DXd)-an antibody-drug conjugate targeting the human epidermal growth factor receptor 2 (HER2)-improved outcomes of patients with HER2-positive and HER2-low metastatic breast cancer. Guidance on monitoring and managing T-DXd-related adverse events (AEs) is an emerging unmet need as translating clinical trial experience into real-world practice may be difficult due to practical and cultural considerations and differences in health care infrastructure. Thus, 13 experts including oncologists, pulmonologists and a radiologist from the Asia-Pacific region gathered to provide recommendations for T-DXd-related AE monitoring and management by using the latest evidence from the DESTINY-Breast trials, our own clinical trial experience and loco-regional health care considerations. While subgroup analysis of Asian (excluding Japanese) versus overall population in the DESTINY-Breast03 uncovered no major differences in the AE profile, we concluded that proactive monitoring and management are essential in maximising the benefits with T-DXd. As interstitial lung disease (ILD)/pneumonitis is a serious AE, patients should undergo regular computed tomography scans, but the frequency may have to account for the median time of ILD/pneumonitis onset and access. Trastuzumab deruxtecan appears to be a highly emetic regimen, and prophylaxis with serotonin receptor antagonists and dexamethasone (with or without neurokinin-1 receptor antagonist) should be considered. Health care professionals should be vigilant for treatable causes of fatigue, and patients should be encouraged to use support groups and practice low-intensity exercises. To increase treatment acceptance, patients should be made aware of alopecia risk prior to starting T-DXd. Detailed monitoring and management recommendations for T-DXd-related AEs are discussed further.

Secure medical image transmission using deep neural network in e-health applications

Recently, medical technologies have developed, and the diagnosis of diseases through medical images has become very important. Medical images often pass through the branches of the network from one end to the other. Hence, high-level security is required. Problems arise due to unauthorized use of data in the image. One of the methods used to secure data in the image is encryption, which is one of the most effective techniques in this field. Confusion and diffusion are the two main steps addressed here. The contribution here is the adaptation of the deep neural network by the weight that has the highest impact on the output, whether it is an intermediate output or a semi-final output in additional to a chaotic system that is not detectable using deep neural network algorithm. The colour and grayscale images were used in the proposed method by dividing the images according to the Region of Interest by the deep neural network algorithm. The algorithm was then used to generate random numbers to randomly create a chaotic system based on the replacement of columns and rows, and randomly distribute the pixels on the designated area. The proposed algorithm evaluated in several ways, and compared with the existing methods to prove the worth of the proposed method.

Interstitial lung disease diagnosis and prognosis using an AI system integrating longitudinal data

For accurate diagnosis of interstitial lung disease (ILD), a consensus of radiologic, pathological, and clinical findings is vital. Management of ILD also requires thorough follow-up with computed tomography (CT) studies and lung function tests to assess disease progression, severity, and response to treatment. However, accurate classification of ILD subtypes can be challenging, especially for those not accustomed to reading chest CTs regularly. Dynamic models to predict patient survival rates based on longitudinal data are challenging to create due to disease complexity, variation, and irregular visit intervals. Here, we utilize RadImageNet pretrained models to diagnose five types of ILD with multimodal data and a transformer model to determine a patient's 3-year survival rate. When clinical history and associated CT scans are available, the proposed deep learning system can help clinicians diagnose and classify ILD patients and, importantly, dynamically predict disease progression and prognosis.

Artificial Intelligence in Quantitative Chest Imaging Analysis for Occupational Lung Disease

Occupational lung disease manifests complex radiologic findings which have long been a challenge for computer-assisted diagnosis (CAD). This journey started in the 1970s when texture analysis was developed and applied to diffuse lung disease. Pneumoconiosis appears on radiography as a combination of small opacities, large opacities, and pleural shadows. The International Labor Organization International Classification of Radiograph of Pneumoconioses has been the main tool used to describe pneumoconioses and is an ideal system that can be adapted for CAD using artificial intelligence (AI). AI includes machine learning which utilizes deep learning or an artificial neural network. This in turn includes a convolutional neural network. The tasks of CAD are systematically described as classification, detection, and segmentation of the target lesions. Alex-net, VGG16, and U-Net are among the most common algorithms used in the development of systems for the diagnosis of diffuse lung disease, including occupational lung disease. We describe the long journey in the pursuit of CAD of pneumoconioses including our recent proposal of a new expert system.

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.

Present and future perspectives in early diagnosis and monitoring for progressive fibrosing interstitial lung diseases

Progressive fibrosing interstitial lung diseases (PF-ILDs) represent a group of conditions of both known and unknown origin which continue to worsen despite standard treatments, leading to respiratory failure and early mortality. Given the potential to slow down progression by initiating antifibrotic therapies where appropriate, there is ample opportunity to implement innovative strategies for early diagnosis and monitoring with the goal of improving clinical outcomes. Early diagnosis can be facilitated by standardizing ILD multidisciplinary team (MDT) discussions, implementing machine learning algorithms for chest computed-tomography quantitative analysis and novel magnetic-resonance imaging techniques, as well as measuring blood biomarker signatures and genetic testing for telomere length and identification of deleterious mutations in telomere-related genes and other single-nucleotide polymorphisms (SNPs) linked to pulmonary fibrosis such as rs35705950 in the MUC5B promoter region. Assessing disease progression in the post COVID-19 era also led to a number of advances in home monitoring using digitally-enabled home spirometers, pulse oximeters and other wearable devices. While validation for many of these innovations is still in progress, significant changes to current clinical practice for PF-ILDs can be expected in the near future.

Novel diagnostic techniques in interstitial lung disease

Research into novel diagnostic techniques and targeted therapeutics in interstitial lung disease (ILD) is moving the field toward increased precision and improved patient outcomes. An array of molecular techniques, machine learning approaches and other innovative methods including electronic nose technology and endobronchial optical coherence tomography are promising tools with potential to increase diagnostic accuracy. This review provides a comprehensive overview of the current evidence regarding evolving diagnostic methods in ILD and to consider their future role in routine clinical care.

A Survey on AI Techniques for Thoracic Diseases Diagnosis Using Medical Images

Thoracic diseases refer to disorders that affect the lungs, heart, and other parts of the rib cage, such as pneumonia, novel coronavirus disease (COVID-19), tuberculosis, cardiomegaly, and fracture. Millions of people die every year from thoracic diseases. Therefore, early detection of these diseases is essential and can save many lives. Earlier, only highly experienced radiologists examined thoracic diseases, but recent developments in image processing and deep learning techniques are opening the door for the automated detection of these diseases. In this paper, we present a comprehensive review including: types of thoracic diseases; examination types of thoracic images; image pre-processing; models of deep learning applied to the detection of thoracic diseases (e.g., pneumonia, COVID-19, edema, fibrosis, tuberculosis, chronic obstructive pulmonary disease (COPD), and lung cancer); transfer learning background knowledge; ensemble learning; and future initiatives for improving the efficacy of deep learning models in applications that detect thoracic diseases. Through this survey paper, researchers may be able to gain an overall and systematic knowledge of deep learning applications in medical thoracic images. The review investigates a performance comparison of various models and a comparison of various datasets.

Diagnosing interstitial lung disease by multidisciplinary discussion: A review

The multidisciplinary meeting (MDM) has been endorsed in current international consensus guidelines as the gold standard method for diagnosis of interstitial lung disease (ILD). In the absence of an accurate and reliable diagnostic test, the agreement between multidisciplinary meetings has been used as a surrogate marker for diagnostic accuracy. Although the ILD MDM has been shown to improve inter-clinician agreement on ILD diagnosis, result in a change in diagnosis in a significant proportion of patients and reduce unclassifiable diagnoses, the ideal form for an ILD MDM remains unclear, with constitution and processes of ILD MDMs varying greatly around the world. It is likely that this variation of practice contributes to the lack of agreement seen between MDMs, as well as suboptimal diagnostic accuracy. A recent Delphi study has confirmed the essential components required for the operation of an ILD MDM. The ILD MDM is a changing entity, as it incorporates new diagnostic tests and genetic markers, while also adapting in its form in response to the obstacles of the COVID-19 pandemic. The aim of this review was to evaluate the current evidence regarding ILD MDM and their role in the diagnosis of ILD, the practice of ILD MDM around the world, approaches to ILD MDM standardization and future directions to improve diagnostic accuracy in ILD.

Exploring the Value of Features of Lung Texture in Distinguishing Between Usual and Nonspecific Interstitial Pneumonia

RATIONALE AND OBJECTIVES

This article aims to explore the potential use of lung texture assessed in CT images in distinguishing between the usual interstitial pneumonia and the nonspecific interstitial pneumonia.

MATERIALS AND METHODS

A retrospective analysis of 96 cases of interstitial pneumonia was performed. Among these cases, there were 40 cases of usual interstitial pneumonia (UIP) and 56 cases of the nonspecific interstitial pneumonia (NSIP) . All of the patients underwent computed tomography (CT) scans. A lung intelligence kit (LK) was utilized to perform lung segmentation and texture feature extraction. The significant variables were determined by variance analysis, least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression. Finally, a multivariate logistic regression model was established to distinguish between the two types of interstitial pneumonia. Receiver operating characteristic (ROC) curves, area under the curve (AUC) values, sensitivity, and specificity were used to evaluate the performance of the established model.

RESULTS

A total of 100 texture features were extracted from the whole lung that was segmented by LK, and 8 features remained after feature reduction. The AUC, sensitivity, and specificity of the multivariate logistic regression model in the training group and the test group were 0.952 and 0.838, 0.821 and 0.667, and 0.949 and 0.824, respectively.

CONCLUSION

It is possible to distinguish between UIP and NSIP using lung texture features obtained from CT images.

From Macroscopy to Ultrastructure: An Integrative Approach to Pulmonary Pathology

Pathology and radiology are complimentary tools, and their joint application is often crucial in obtaining an accurate diagnosis in non-neoplastic pulmonary diseases. However, both come with significant limitations of their own: Computed Tomography (CT) can only visualize larger structures due to its inherent–relatively–poor resolution, while (histo) pathology is often limited due to small sample size and sampling error and only allows for a 2D investigation. An innovative approach of inflating whole lung specimens and subjecting these subsequently to CT and whole lung microCT allows for an accurate matching of CT-imaging and histopathology data of exactly the same areas. Systematic application of this approach allows for a more targeted assessment of localized disease extent and more specifically can be used to investigate early mechanisms of lung diseases on a morphological and molecular level. Therefore, this technique is suitable to selectively investigate changes in the large and small airways, as well as the pulmonary arteries, veins and capillaries in relation to the disease extent in the same lung specimen. In this perspective we provide an overview of the different strategies that are currently being used, as well as how this growing field could further evolve.