Radiomic measures from chest high-resolution computed tomography associated with lung function in sarcoidosis

Quantitative parameters of HRCT target scan to predict the risk of lung adenocarcinoma based on the detection of lung ground-glass nodules

BACKGROUND

To explore the value of high-resolution computed tomography (HRCT) in the differential diagnosis of benign and malignant ground-glass nodules (GGNs), and to provide a theoretical basis for the clinical application of HRCT.

METHODS

A total of 208 patients with GGN who had been clinically confirmed by surgical pathology and clinical confirmation were collected, and HRCT target scanning technology was used to scan and collect general information of patients, and observe the distribution of GGN, GGN size, GGN cross-sectional area, diameter, transverse diameter, solid composition, relationship with bronchi, and relationship with blood vessels and other indicators. Multivariate regression analysis and risk factor prediction are performed.

RESULTS

The differences were statistically significant in multivariate regression analysis, such as nodule location, maximum diameter, maximum cross-sectional area, GGN status, nodule boundary and relationship with blood vessels (P < 0.05). The results of ROC curve showed that the AUC value of nodule site and nodule boundary was greater than 0.5, and the nodule boundary AUC value was 0.676, which was more sensitive to predict whether GGN deteriorated to lung adenocarcinoma (LUAD).

CONCLUSION

Nodule site and nodule boundary are effective risk predictors for LUAD in patients with GGN, and nodule boundary is the most valuable independent predictor.

The textures of sarcoidosis: quantifying lung disease through variograms

Objective. Sarcoidosis is a granulomatous disease affecting the lungs in over 90% of patients. Qualitative assessment of chest CT by radiologists is standard clinical practice and reliable quantification of disease from CT would support ongoing efforts to identify sarcoidosis phenotypes. Standard imaging feature engineering techniques such as radiomics suffer from extreme sensitivity to image acquisition and processing, potentially impeding generalizability of research to clinical populations. In this work, we instead investigate approaches to engineering variogram-based features with the intent to identify a robust, generalizable pipeline for image quantification in the study of sarcoidosis.Approach. For a cohort of more than 300 individuals with sarcoidosis, we investigated 24 feature engineering pipelines differing by decisions for image registration to a template lung, empirical and model variogram estimation methods, and feature harmonization for CT scanner model, and subsequently 48 sets of phenotypes produced through unsupervised clustering. We then assessed sensitivity of engineered features, phenotypes produced through unsupervised clustering, and sarcoidosis disease signal strength to pipeline.Main results. We found that variogram features had low to mild association with scanner model and associations were reduced by image registration. For each feature type, features were also typically robust to all pipeline decisions except image registration. Strength of disease signal as measured by association with pulmonary function testing and some radiologist visual assessments was strong (optimistic AUC ≈ 0.9,p≪0.0001in models for architectural distortion, conglomerate mass, fibrotic abnormality, and traction bronchiectasis) and fairly consistent across engineering approaches regardless of registration and harmonization for CT scanner.Significance. Variogram-based features appear to be a suitable approach to image quantification in support of generalizable research in pulmonary sarcoidosis.

From images to clinical insights: an educational review on radiomics in lung diseases

Radiological imaging is a cornerstone in the clinical workup of lung diseases. Radiomics represents a significant advancement in clinical lung imaging, offering a powerful tool to complement traditional qualitative image analysis. Radiomic features are quantitative and computationally describe shape, intensity, texture and wavelet characteristics from medical images that can uncover detailed and often subtle information that goes beyond the visual capabilities of radiological examiners. By extracting this quantitative information, radiomics can provide deep insights into the pathophysiology of lung diseases and support clinical decision-making as well as personalised medicine approaches. In this educational review, we provide a step-by-step guide to radiomics-based medical image analysis, discussing the technical challenges and pitfalls, and outline the potential clinical applications of radiomics in diagnosing, prognosticating and evaluating treatment responses in respiratory medicine.

Understanding the Added Value of High-Resolution CT Beyond Chest X-Ray in Determining Extent of Physiologic Impairment

BACKGROUND

Sarcoidosis staging primarily has relied on the Scadding chest radiographic system, although chest CT imaging is finding increased clinical use.

RESEARCH QUESTION

Whether standardized chest CT scan assessment provides additional understanding of lung function beyond Scadding stage and demographics is unknown and the focus of this study.

STUDY DESIGN AND METHODS

We used National Heart, Lung, and Blood Institute study Genomics Research in Alpha-1 Antitrypsin Deficiency and Sarcoidosis (GRADS) cases of sarcoidosis (n = 351) with Scadding stage and chest CT scans obtained in a standardized manner. One chest radiologist scored all CT scans with a visual scoring system, with a subset read by another chest radiologist. We compared demographic features, Scadding stage and CT scan findings, and the correlation between these measures. Associations between spirometry and diffusing capacity of the lungs for carbon monoxide (Dlco) results and CT scan findings and Scadding stage were determined using regression analysis (n = 318). Agreement between readers was evaluated using Cohen's κ value.

RESULTS

CT scan features were inconsistent with Scadding stage in approximately 40% of cases. Most CT scan features assessed on visual scoring were associated negatively with lung function. Associations persisted for FEV1 and Dlco when adjusting for Scadding stage, although some CT scan feature associations with FVC became insignificant. Scadding stage was associated primarily with FEV1, and inclusion of CT scan features reduced significance in association between Scadding stage and lung function. Multivariable regression modeling to identify radiologic measures explaining lung function included Scadding stage for FEV1 and FEV1 to FVC ratio (P < .05) and marginally for Dlco (P < .15). Combinations of CT scan measures accounted for Scadding stage for FVC. Correlations among Scadding stage and CT scan features were noted. Agreement between readers was poor to moderate for presence or absence of CT scan features and poor for degree and location of abnormality.

INTERPRETATION

In this study, CT scan features explained additional variability in lung function beyond Scadding stage, with some CT scan features obviating the associations between lung function and Scadding stage. Whether CT scan features, phenotypes, or endotypes could be useful for treating patients with sarcoidosis needs more study.

Radiomics based on HRCT can predict RP-ILD and mortality in anti-MDA5 + dermatomyositis patients: a multi-center retrospective study

OBJECTIVES

To assess the effectiveness of HRCT-based radiomics in predicting rapidly progressive interstitial lung disease (RP-ILD) and mortality in anti-MDA5 positive dermatomyositis-related interstitial lung disease (anti-MDA5 + DM-ILD).

METHODS

From August 2014 to March 2022, 160 patients from Institution 1 were retrospectively and consecutively enrolled and were randomly divided into the training dataset (n = 119) and internal validation dataset (n = 41), while 29 patients from Institution 2 were retrospectively and consecutively enrolled as external validation dataset. We generated four Risk-scores based on radiomics features extracted from four areas of HRCT. A nomogram was established by integrating the selected clinico-radiologic variables and the Risk-score of the most discriminative radiomics model. The RP-ILD prediction performance of the models was evaluated by using the area under the receiver operating characteristic curves, calibration curves, and decision curves. Survival analysis was conducted with Kaplan-Meier curves, Mantel-Haenszel test, and Cox regression.

RESULTS

Over a median follow-up time of 31.6 months (interquartile range: 12.9-49.1 months), 24 patients lost to follow-up and 46 patients lost their lives (27.9%, 46/165). The Risk-score based on bilateral lungs performed best, attaining AUCs of 0.869 and 0.905 in the internal and external validation datasets. The nomogram outperformed clinico-radiologic model and Risk-score with AUCs of 0.882 and 0.916 in the internal and external validation datasets. Patients were classified into low- and high-risk groups with 50:50 based on nomogram. High-risk group patients demonstrated a significantly higher risk of mortality than low-risk group patients in institution 1 (HR = 4.117) and institution 2 cohorts (HR = 7.515).

CONCLUSION

For anti-MDA5 + DM-ILD, the nomogram, mainly based on radiomics, can predict RP-ILD and is an independent predictor of mortality.

The textures of sarcoidosis: quantifying lung disease through variograms

Objective

Sarcoidosis is a granulomatous disease affecting the lungs in over 90% of patients. Qualitative assessment of chest CT by radiologists is standard clinical practice and reliable quantification of disease from CT would support ongoing efforts to identify sarcoidosis phenotypes. Standard imaging feature engineering techniques such as radiomics suffer from extreme sensitivity to image acquisition and processing, potentially impeding generalizability of research to clinical populations. In this work, we instead investigate approaches to engineering variogram-based features with the intent to identify a robust, generalizable pipeline for image quantification in the study of sarcoidosis.

Approach

For a cohort of more than 300 individuals with sarcoidosis, we investigated 24 feature engineering pipelines differing by decisions for image registration to a template lung, empirical and model variogram estimation methods, and feature harmonization for CT scanner model, and subsequently 48 sets of phenotypes produced through unsupervised clustering. We then assessed sensitivity of engineered features, phenotypes produced through unsupervised clustering, and sarcoidosis disease signal strength to pipeline.

Main results

We found that variogram features had low to mild association with scanner model and associations were reduced by image registration. For each feature type, features were also typically robust to all pipeline decisions except image registration. Strength of disease signal as measured by association with pulmonary function testing and some radiologist visual assessments was strong (optimistic AUC ≈ 0.9, p ≪ 0.0001 in models for architectural distortion, conglomerate mass, fibrotic abnormality, and traction bronchiectasis) and fairly consistent across engineering approaches regardless of registration and harmonization for CT scanner.

Significance

Variogram-based features appear to be a suitable approach to image quantification in support of generalizable research in pulmonary sarcoidosis.

High-Resolution CT Scan Fibrotic Patterns in Stage IV Pulmonary Sarcoidosis: Impact on Pulmonary Function and Survival

BACKGROUND

Different patterns of fibrosis on high-resolution CT scans (HRCT) have been associated with reduced survival in some interstitial lung diseases. Nothing is known about HRCT scan patterns and survival in sarcoidosis.

RESEARCH QUESTION

Will a detailed description of the extent and pattern of HRCT scan fibrosis in patients with stage IV pulmonary sarcoidosis impact pulmonary function and survival?

STUDY DESIGN AND METHODS

Two hundred forty patients with stage IV sarcoidosis at two large tertiary institutions were studied. The earliest HRCT scan with fibrosis was reviewed for extent of fibrosis (< 10%, 10%-20%, and > 20%) and presence of bronchiectasis, upper lobe fibrocystic changes, basal subpleural honeycombing, ground-glass opacities (GGOs), large bullae, and mycetomas. Presence of sarcoidosis-associated pulmonary hypertension (SAPH) and pulmonary function testing performed within 1 year of HRCT were recorded. Patients were followed up until last clinic visit, death, or lung transplantation.

RESULTS

The mean age was 58.4 years. Seventy-four percent were Black, 63% were female, and mean follow-up was 7.4 years. Death or LT occurred in 53 patients (22%). Thirty-one percent had > 20% fibrosis, 25% had 10%-20% fibrosis, and 44% had < 10% fibrosis. The most common HRCT abnormalities were bronchiectasis (76%), upper lobe fibrocystic changes (36%), and GGOs (28%). Twelve percent had basal subpleural honeycombing, and 32% had SAPH. Patients with > 20% fibrosis had more severe pulmonary impairment, were more likely to have SAPH (53%), and had worse survival (44% mortality; P < .001). Upper lobe fibrocystic changes, basal subpleural honeycombing, and large bullae were associated with worse pulmonary function and worse survival. Patients with basal subpleural honeycombing had the worst pulmonary function and survival (55% mortality; P < .001). GGOs were associated with worse pulmonary function but not worse survival, and mycetomas were associated with worse survival but not worse pulmonary function. A Cox proportional hazards model indicated that basal subpleural honeycombing (hazard ratio, 7.95), diffusion capacity for carbon monoxide < 40% (HR, 5.67) and White race (hazard ratio, 2.61) were independent predictors of reduced survival.

INTERPRETATION

HRCT scan features of fibrotic pulmonary sarcoidosis had an impact on pulmonary function and survival. Presence of >20% fibrosis and basal subpleural honeycombing are predictive of worse pulmonary function and worse survival in patients with stage IV pulmonary sarcoidosis.

High-Risk Sarcoidosis: A Focus on Pulmonary, Cardiac, Hepatic and Renal Advanced Diseases, as Well as on Calcium Metabolism Abnormalities

Although sarcoidosis is generally regarded as a benign condition, approximately 20-30% of patients will develop a chronic and progressive disease. Advanced pulmonary fibrotic sarcoidosis and cardiac involvement are the main contributors to sarcoidosis morbidity and mortality, with failure of the liver and/or kidneys representing additional life-threatening situations. In this review, we discuss diagnosis and treatment of each of these complications and highlight how the integration of clinical, pathological and radiological features may help predict the development of such high-risk situations in sarcoid patients.

Treatment of Granulomatous Inflammation in Pulmonary Sarcoidosis

The management of pulmonary sarcoidosis is a complex interplay of disease characteristics, the impact of medications, and patient preferences. Foremost, it is important to weigh the risk of anti-granulomatous treatment with the benefits of lung preservation and improvement in quality of life. Because of its high spontaneous resolution rate, pulmonary sarcoidosis should only be treated in cases of significant symptoms due to granulomatous inflammation, lung function decline, or substantial inflammation on imaging that can lead to irreversible fibrosis. The longstanding basis of treatment has historically been corticosteroid therapy for the control of granulomatous inflammation. However, several corticosteroid-sparing options have increasing evidence for use in refractory disease, inability to taper steroids to an acceptable dose, or in those with toxicity to corticosteroids. Treatment of sarcoidosis should be individualized for each patient due to the heterogeneity of the clinical course, comorbid conditions, response to therapy, and tolerance of medication side effects.

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.

Current Applications of Artificial Intelligence in Sarcoidosis

PURPOSE

Sarcoidosis is a complex disease which can affect nearly every organ system with manifestations ranging from asymptomatic imaging findings to sudden cardiac death. As such, diagnosis and prognostication are topics of continued investigation. Recent technological advancements have introduced multiple modalities of artificial intelligence (AI) to the study of sarcoidosis. Machine learning, deep learning, and radiomics have predominantly been used to study sarcoidosis.

METHODS

Articles were collected by searching online databases using keywords such as sarcoid, machine learning, artificial intelligence, radiomics, and deep learning. Article titles and abstracts were reviewed for relevance by a single reviewer. Articles written in languages other than English were excluded.

CONCLUSIONS

Machine learning may be used to help diagnose pulmonary sarcoidosis and prognosticate in cardiac sarcoidosis. Deep learning is most comprehensively studied for diagnosis of pulmonary sarcoidosis and has less frequently been applied to prognostication in cardiac sarcoidosis. Radiomics has primarily been used to differentiate sarcoidosis from malignancy. To date, the use of AI in sarcoidosis is limited by the rarity of this disease, leading to small, suboptimal training sets. Nevertheless, there are applications of AI that have been used to study other systemic diseases, which may be adapted for use in sarcoidosis. These applications include discovery of new disease phenotypes, discovery of biomarkers of disease onset and activity, and treatment optimization.

Sarcoidosis-associated pulmonary fibrosis: joining the dots

Sarcoidosis is a multisystem granulomatous disorder of unknown aetiology. A minority of patients with sarcoidosis develop sarcoidosis-associated pulmonary fibrosis (SAPF), which may become progressive. Genetic profiles differ between patients with progressive and self-limiting disease. The mechanisms of fibrosis in SAPF are not fully understood, but SAPF is likely a distinct clinicopathological entity, rather than a continuum of acute inflammatory sarcoidosis. Risk factors for the development of SAPF have been identified; however, at present, it is not possible to make a robust prediction of risk for an individual patient. The bulk of fibrotic abnormalities in SAPF are located in the upper and middle zones of the lungs. A greater extent of SAPF on imaging is associated with a worse prognosis. Patients with SAPF are typically treated with corticosteroids, second-line agents such as methotrexate or azathioprine, or third-line agents such as tumour necrosis factor inhibitors. The antifibrotic drug nintedanib is an approved treatment for slowing the decline in lung function in patients with progressive fibrosing interstitial lung diseases, but more evidence is needed to assess its efficacy in SAPF. The management of patients with SAPF should include the identification and treatment of complications such as bronchiectasis and pulmonary hypertension. Further research is needed into the mechanisms underlying SAPF and biomarkers that predict its clinical course.

The Symptoms of Pulmonary Sarcoidosis

The aim of this manuscript is to provide a comprehensive review of the etiology, measurement, and treatment of common pulmonary symptoms associated with sarcoidosis. The assessment of symptoms associated with pulmonary sarcoidosis is an important component of disease management. Some symptoms of pulmonary sarcoidosis are sensitive but nonspecific markers of disease activity, and the absence of such symptoms provides evidence that the disease is quiescent. Although quantifiable objective measurements of pulmonary physiology and chest imaging are important in the assessment of pulmonary sarcoidosis, they correlate poorly with the patient's quality of life. Because the symptoms of pulmonary sarcoidosis directly relate to how the patient feels, they are reasonable endpoints in terms of clinical research and individual patient care. Recently, the symptoms of pulmonary sarcoidosis are capable of being quantified via patient-reported outcome measures and electronic devices. We conclude that a thorough assessment of the symptoms associated with pulmonary sarcoidosis improves patient care because it is a useful screen for manifestations of the disease, provides insight into the pathophysiology of manifestations of sarcoidosis, and may assist in optimizing treatment.

Assessment of a computed tomography-based radiomics approach for assessing lung function in lung cancer patients

PURPOSE

We aimed to assess radiomics approaches for estimating three pulmonary function test (PFT) results (forced expiratory volume in one second [FEV1], forced vital capacity [FVC], and the ratio of FEV1 to FVC [FEV₁/FVC]) using data extracted from chest computed tomography (CT) images.

METHODS

This retrospective study included 85 lung cancer patients (mean age, 75 years ±8; 69 men) who underwent stereotactic body radiotherapy between 2012 and 2020. Their pretreatment chest breath-hold CT and PFT data before radiotherapy were obtained. A total of 107 radiomics features (Shape: 14, Intensity: 18, Texture: 75) were extracted using two methods: extraction of the lung tissue (<-250 HU) (APPROACH 1), and extraction of small blood vessels and lung tissue (APPROACH 2). The PFT results were estimated using the least absolute shrinkage and selection operator regression. Pearson's correlation coefficients (r) were determined for all PFT results, and the area under the curve (AUC) was calculated for FEV₁/FVC (<70 %). Finally, we compared our approaches with the conventional formula (Conventional).

RESULTS

For the estimated FEV₁/FVC, the Pearson's r were 0.21 (P =.06), 0.69 (P <.01), and 0.73 (P <.01) for Conventional, APPROACH 1, and APPROACH 2, respectively; the AUCs for FEV₁/FVC (<70 %) were 0.67 (95 % confidence interval [CI]: 0.55, 0.79), 0.82 (CI: 0.72, 0.91; P =.047) and 0.86 (CI: 0.78, 0.94; P =.01), respectively.

CONCLUSIONS

The radiomics approach performed better than the conventional equation and may be useful for assessing lung function based on CT images.

Cluster activation mapping with application to computed tomography scans of the lung

Purpose: An open question in deep clustering is how to explain what in the image is driving the cluster assignments. This is especially important for applications in medical imaging when the derived cluster assignments may inform decision-making or create new disease subtypes. We develop cluster activation mapping (CLAM), which is methodology to create localization maps highlighting the image regions important for cluster assignment. Approach: Our approach uses a linear combination of the activation channels from the last layer of the encoder within a pretrained autoencoder. The activation channels are weighted by a channelwise confidence measure, which is a modification of score-CAM. Results: Our approach performs well under medical imaging-based simulation experiments, when the image clusters differ based on size, location, and intensity of abnormalities. Under simulation, the cluster assignments were predicted with 100% accuracy when the number of clusters was set at the true value. In addition, applied to computed tomography scans from a sarcoidosis population, CLAM identified two subtypes of sarcoidosis based purely on CT scan presentation, which were significantly associated with pulmonary function tests and visual assessment scores, such as ground-glass, fibrosis, and honeycombing. Conclusions: CLAM is a transparent methodology for identifying explainable groupings of medical imaging data. As deep learning networks are often criticized and not trusted due to their lack of interpretability, our contribution of CLAM to deep clustering architectures is critical to our understanding of cluster assignments, which can ultimately lead to new subtypes of diseases.

Impact of image pre-processing methods on computed tomography radiomics features in chronic obstructive pulmonary disease

Computed tomography (CT) imaging texture-based radiomics analysis can be used to assess chronic obstructive pulmonary disease (COPD). However, different image pre-processing methods are commonly used, and how these different methods impact radiomics features and lung disease assessment, is unknown. The purpose of this study was to develop an image pre-processing pipeline to investigate how various pre-processing combinations impact radiomics features and their use for COPD assessment. Spirometry and CT images were obtained from the multi-centered Canadian Cohort of Obstructive Lung Disease study. Participants were divided based on assessment site and were further dichotomized as No COPD or COPD within their participant groups. An image pre-processing pipeline was developed, calculating 32 grey level co-occurrence matrix radiomics features. The pipeline included lung segmentation, airway segmentation or no segmentation, image resampling or no resampling, and either no pre-processing, binning, edgmentation, or thresholding pre-processing techniques. A three-way analysis of variance was used for method comparison. A nested 10-fold cross validation using logistic regression and multiple linear regression models were constructed to classify COPD and assess correlation with lung function, respectively. Logistic regression performance was evaluated using the area under the receiver operating characteristic curve (AUC). A total of 1210 participants (Sites 1-8: No COPD:n = 447, COPD:n = 413; and Site 9: No COPD:n = 155, COPD:n = 195) were evaluated. Between the two participant groups, at least 16/32 features were different between airway segmentation/no segmentation (P ≤ 0.04), at least 29/32 features were different between no resampling/resampling (P ≤ 0.04), and 32/32 features were different between the pre-processing techniques (P < 0.0001). Features generated using the resampling/edgmentation and resampling/thresholding pre-processing combinations, regardless of airway segmentation, performed the best in COPD classification (AUC ≥ 0.718), and explained the most variance with lung function (R² ≥ 0.353). Therefore, the image pre-processing methods completed prior to CT radiomics feature extraction significantly impacted extracted features and their ability to assess COPD.

Management of Advanced Pulmonary Sarcoidosis

The term "advanced sarcoidosis" is used for forms of sarcoidosis with a significant risk of loss of organ function or death. Advanced sarcoidosis often involves the lung and is described as "Advanced Pulmonary Sarcoidosis" (APS) which includes advanced pulmonary fibrosis, associated complications such as bronchiectasis and infections, and pulmonary hypertension. While APS affects a small proportion of patients with sarcoidosis, it is the leading cause of poor outcomes including death. Herein we review the major patterns of APS with a focus on the current management as well as potential approaches for improved outcomes for this most serious sarcoidosis phenotype.

Patterns of medication use and imaging following initial diagnosis of sarcoidosis

INTRODUCTION

Sarcoidosis is a rare inflammatory disease with unclear natural history. Using a large, retrospective, longitudinal, population-based cohort, we sought to define its natural history in order to guide future clinical studies.

METHODS

We identified 722 newly diagnosed cases of sarcoidosis within Kaiser Permanente Northwest health care records between 1995 and 2015. We investigated immunosuppressive medication use in the two years following diagnosis, analyzed demographic and clinical characteristics, and quantified chest imaging and pulmonary function testing (PFTs) across the clinical course.

RESULTS

Over two years of follow-up, 41% of patients were treated with prednisone. Of those, 75% tapered off their first course within 100 days, although half of those patients required recurrent therapy. Five percent of the entire cohort remained on prednisone for longer than one year, with an average daily dose of 10-20 mg. Chest imaging was associated with early prednisone use, and chest CT was associated with changes in prednisone dose. PFTs or demographics were not associated with prednisone use. Cumulative prednisone doses were significantly higher in African Americans (1,845 mg additional) and those who had a chest CT (2,015 mg additional). Overall, PFTs were less frequently obtained than chest imaging and had no significant change over disease course.

DISCUSSION

The natural history of sarcoidosis varies greatly. For those requiring therapy, corticosteroid burden is high. Chest imaging drives medication dose changes as compared to PFTs, but neither outcome fully captures the entire history of disease. Prospective cohorts are needed with purposefully collected, repeated measures that include objective clinical assessments and symptoms.

Novelties in Imaging of Thoracic Sarcoidosis

Sarcoidosis is a systemic granulomatous disease affecting various organs, and the lungs are the most commonly involved. According to guidelines, diagnosis relies on a consistent clinical picture, histological demonstration of non-caseating granulomas, and exclusion of other diseases with similar histological or clinical picture. Nevertheless, chest imaging plays an important role in both diagnostic assessment, allowing to avoid biopsy in some situations, and prognostic evaluation. Despite the demonstrated lower sensitivity of chest X-ray (CXR) in the evaluation of chest findings compared to high-resolution computed tomography (HRCT), CXR still retains a pivotal role in both diagnostic and prognostic assessment in sarcoidosis. Moreover, despite the huge progress made in the field of radiation dose reduction, chest magnetic resonance (MR), and quantitative imaging, very little research has focused on their application in sarcoidosis. In this review, we aim to describe the latest novelties in diagnostic and prognostic assessment of thoracic sarcoidosis and to identify the fields of research that require investigation.

Quantification of Cystic Fibrosis Lung Disease with Radiomics-based CT Scores

Purpose

To develop radiomics-based CT scores for assessing lung disease severity and exacerbation risk in adult patients with cystic fibrosis (CF).

Materials and Methods

This two-center retrospective observational study was approved by an institutional ethics committee, and the need for patient consent was waived. A total of 215 outpatients with CF referred for unenhanced follow-up chest CT were evaluated in two different centers between January 2013 and December 2016. After lung segmentation, chest CT scans from center 1 (training cohort, 162 patients [median age, 29 years; interquartile range {IQR}, 24-36 years; 84 men]) were used to build CT scores from 38 extracted CT features, using five different machine learning techniques trained to predict a clinical prognostic score, the Nkam score. The correlations between the developed CT scores, two different clinical prognostic scores (Liou and CF-ABLE), forced expiratory volume in 1 second (FEV₁), and risk of respiratory exacerbations were evaluated in the test cohort (center 2, 53 patients [median age, 27 years; IQR, 22-35 years; 34 men]) using the Spearman rank coefficient.

Results

In the test cohort, all radiomics-based CT scores showed moderate to strong correlation with the Nkam score (R = 0.57 to 0.63, P < .001) and Liou scores (R = -0.55 to -0.65, P < .001), whereas the correlation with CF-ABLE score was weaker (R = 0.28 to 0.38, P = .005 to .048). The developed CT scores showed strong correlation with predicted FEV₁ (R = -0.62 to -0.66, P < .001) and weak to moderate correlation with the number of pulmonary exacerbations to occur in the 12 months after the CT examination (R = 0.38 to 0.55, P < .001 to P = .006).

Conclusion

Radiomics can be used to build automated CT scores that correlate to clinical severity and exacerbation risk in adult patients with CF.Supplemental material is available for this article.See also the commentary by Elicker and Sohn in this issue.© RSNA, 2020.

Prospects and Challenges of Radiomics by Using Nononcologic Routine Chest CT

Chest CT scans are one of the most common medical imaging procedures. The automatic extraction and quantification of imaging features may help in diagnosis, prognosis of, or treatment decision in cardiovascular, pulmonary, and metabolic diseases. However, an adequate sample size as a statistical necessity for radiomics studies is often difficult to achieve in prospective trials. By exploiting imaging data from clinical routine, a much larger amount of data could be used than in clinical trials. Still, there is only little literature on the implementation of radiomics in clinical routine chest CT scans. Reasons are heterogeneous CT scanning protocols and the resulting technical variability (eg, different slice thicknesses, reconstruction kernels or timings after contrast material administration) in routine CT imaging data. This review summarizes the recent state of the art of studies aiming to develop quantifiable imaging biomarkers at chest CT, such as for osteoporosis, chronic obstructive pulmonary disease, interstitial lung disease, and coronary artery disease. This review explains solutions to overcome heterogeneity in routine data such as the use of imaging repositories, the standardization of radiomic features, algorithmic approaches to improve feature stability, test-retest studies, and the evolution of deep learning for modeling radiomics features. Supplemental material is available for this article. © RSNA, 2020 See also the commentary by Kay in this issue.

Template Creation for High-Resolution Computed Tomography Scans of the Lung in R Software

Rationale and Objectives.

A standard lung template could improve population-level analyses for computed tomography (CT) scans of the lung. We develop a fully-automated pre-processing pipeline for image analysis of the lungs using updated methodologies and R software that results in the creation of a standard lung template. We apply this pipeline to CT scans from a sarcoidosis population, exploring the influence of registration on radiomic analyses.

Materials and Methods.

Using 65 high-resolution CT scans from healthy adults, we create a standard lung template by segmenting the left and right lungs, non-linearly registering lung masks to an initial template mask, and using an unbiased, iterative procedure to converge to a standard lung shape (Dice similarity coefficient ≥0.99). We compare three-dimensional radiomic features between control and sarcoidosis patients, before and after registration to a study-specific lung template.

Results.

The final lung template had a right lung volume of 2967 cm³ and left lung volume of 2623 cm³, with a median HU = −862. Registration significantly affected radiomic features, shifting the HU distribution to the left, decreasing variability, and increasing smoothness (p<0.0001). The registration improved detective ability of radiomics; for contrast, autocorrelation, energy and homogeneity, the group effect was significant post-registration (p<0.05), but was not significant pre-registration.

Conclusion.

The final lung template and software used for its creation are publicly available via the lungct R package to facilitate its use in practice. This study advances lung imaging by developing tools to improve population-level analyses for various lung diseases.