Radiomics to predict the mortality of patients with rheumatoid arthritis-associated interstitial lung disease: A proof-of-concept study

Tracheal computed tomography radiomics model for prediction of the Omicron variant of severe acute respiratory syndrome coronavirus 2

OBJECTIVES

The Omicron variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is highly contagious, fast-spreading, and insidious. Most patients present with normal findings on lung computed tomography (CT). The current study aimed to develop and validate a tracheal CT radiomics model to predict Omicron variant infection.

MATERIALS AND METHODS

In this retrospective study, a radiomics model was developed based on a training set consisting of 157 patients with an Omicron variant infection and 239 healthy controls between 1 January and 30 April 2022. A set of morphological expansions, with dilations of 1, 3, 5, 7, and 9 voxels, was applied to the trachea, and radiomic features were extracted from different dilation voxels of the trachea. Logistic regression (LR), support vector machines (SVM), and random forests (RF) were developed and evaluated; the models were validated on 67 patients with the Omicron variant and on 103 healthy controls between 1 May and 30 July 2022.

RESULTS

Logistic regression with 12 radiomic features extracted from the tracheal wall with dilation of 5 voxels achieved the highest classification performance compared with the other models. The LR model achieved an area under the curve of 0.993 (95% confidence interval [CI]: 0.987-0.998) in the training set and 0.989 (95% CI: 0.979-0.999) in the validation set. Sensitivity, specificity, and accuracy of the model for the training set were 0.994, 0.946, and 0.965, respectively, whereas those for the validation set were 0.970, 0.952, and 0.959, respectively.

CONCLUSION

The tracheal CT radiomics model reliably identified the Omicron variant of SARS-CoV‑2, and may help in clinical decision-making in future, especially in cases of normal lung CT findings.

Advancing Rheumatology Care Through Machine Learning

Rheumatologic diseases are marked by their complexity, involving immune-, metabolic- and mechanically mediated processes which can affect different organ systems. Despite a growing arsenal of targeted medications, many rheumatology patients fail to achieve full remission. Assessing disease activity remains challenging, as patients prioritize different symptoms and disease phenotypes vary. This is also reflected in clinical trials where the efficacy of drugs is not necessarily measured in an optimal way with the traditional outcome assessment. The recent COVID-19 pandemic has catalyzed a digital transformation in healthcare, embracing telemonitoring and patient-reported data via apps and wearables. As a further driver of digital medicine, electronic medical record (EMR) providers are actively engaged in developing algorithms for clinical decision support, heralding a shift towards patient-centered, decentralized care. Machine learning algorithms have emerged as valuable tools for handling the increasing volume of patient data, promising to enhance treatment quality and patient well-being. Convolutional neural networks (CNN) are particularly promising for radiological image analysis, aiding in the detection of specific lesions such as erosions, sacroiliitis, or osteoarthritis, with several FDA-approved applications. Clinical predictions, including numerical disease activity forecasts and medication choices, offer the potential to optimize treatment strategies. Numeric predictions can be integrated into clinical workflows, allowing for shared decision making with patients. Clustering patients based on disease characteristics provides a personalized care approach. Digital biomarkers, such as patient-reported outcomes and wearables data, offer insights into disease progression and therapy response more flexibly and outside patient consultations. In association with patient-reported outcomes, disease-specific digital biomarkers via image recognition or single-camera motion capture enables more efficient remote patient monitoring. Digital biomarkers may also play a major role in clinical trials in the future as continuous, disease-specific outcome measurement facilitating decentralized studies. Prediction models can help with patient selection in clinical trials, such as by predicting high disease activity. Efforts are underway to integrate these advancements into clinical workflows using digital pathways and remote patient monitoring platforms. In summary, machine learning, digital biomarkers, and advanced imaging technologies hold immense promise for enhancing clinical decision support and clinical trials in rheumatology. Effective integration will require a multidisciplinary approach and continued validation through prospective studies.

CCL18, CHI3L1, ANG2, IL-6 systemic levels are associated with the extent of lung damage and radiomic features in SARS-CoV-2 infection

OBJECTIVE AND DESIGN

We aimed to identify cytokines whose concentrations are related to lung damage, radiomic features, and clinical outcomes in COVID-19 patients.

MATERIAL OR SUBJECTS

Two hundred twenty-six patients with SARS-CoV-2 infection and chest computed tomography (CT) images were enrolled.

METHODS

CCL18, CHI3L1/YKL-40, GAL3, ANG2, IP-10, IL-10, TNFα, IL-6, soluble gp130, soluble IL-6R were quantified in plasma samples using Luminex assays. The Mann-Whitney U test, the Kruskal-Wallis test, correlation and regression analyses were performed. Mediation analyses were used to investigate the possible causal relationships between cytokines, lung damage, and outcomes. AVIEW lung cancer screening software, pyradiomics, and XGBoost classifier were used for radiomic feature analyses.

RESULTS

CCL18, CHI3L1, and ANG2 systemic levels mainly reflected the extent of lung injury. Increased levels of every cytokine, but particularly of IL-6, were associated with the three outcomes: hospitalization, mechanical ventilation, and death. Soluble IL-6R showed a slight protective effect on death. The effect of age on COVID-19 outcomes was partially mediated by cytokine levels, while CT scores considerably mediated the effect of cytokine levels on outcomes. Radiomic-feature-based models confirmed the association between lung imaging characteristics and CCL18 and CHI3L1.

CONCLUSION

Data suggest a causal link between cytokines (risk factor), lung damage (mediator), and COVID-19 outcomes.

Successful Treatment of a Patient with Drug-Refractory Rheumatoid Arthritis-Associated Interstitial Lung Disease with Upadacitinib: A Case Report

Insufficient evidence exists regarding the efficacy of Janus kinase inhibitors (JAKis), a class of targeted synthetic disease-modifying anti-rheumatic drugs (tsDMARDs), in the treatment of rheumatoid arthritis (RA)-associated interstitial lung disease (ILD). Herein, we present a case of RA-ILD refractory to previous treatments that exhibited favorable response to upadacitinib. A 69-year-old man, former smoker, was diagnosed with RA-ILD based on persistent symmetric polyarthritis, elevated C-reactive protein levels and erythrocyte sedimentation rate, reduced diffusing capacity for carbon monoxide/alveolar volume (DLCO 69.9%), and bilateral ground-glass attenuation with traction bronchiectasis, predominantly in the lower lung lobe. Initial treatment with oral prednisolone and methotrexate was started; however, the patient showed worsening dyspnea, chest high-resolution computed tomography abnormalities, and decreased pulmonary function. The dose of prednisolone was increased, and methotrexate was shifted to tacrolimus; however, tacrolimus was eventually discontinued because of renal dysfunction. Subsequent treatment changes included abatacept followed by intravenous cyclophosphamide, but ILD activity continued to worsen and met the criteria of progressive pulmonary fibrosis. Approximately 4.5 years after the RA diagnosis, dyspnea, radiological abnormalities, and DLCO improved following treatment switch to upadacitinib, one of JAKis. JAKi therapy may have potential as a treatment option for refractory RA-ILD.

Non-contrast computed tomography-based radiomics for staging of connective tissue disease-associated interstitial lung disease

Rationale and introduction

It is of significance to assess the severity and predict the mortality of patients with connective tissue disease-associated interstitial lung disease (CTD-ILD). In this double-center retrospective study, we developed and validated a radiomics nomogram for clinical management by using the ILD-GAP (gender, age, and pulmonary physiology) index system.

Materials and methods

Patients with CTD-ILD were staged using the ILD-GAP index system. A clinical factor model was built by demographics and CT features, and a radiomics signature was developed using radiomics features extracted from CT images. Combined with the radiomics signature and independent clinical factors, a radiomics nomogram was constructed and evaluated by the area under the curve (AUC) from receiver operating characteristic (ROC) analyses. The models were externally validated in dataset 2 to evaluate the model generalization ability using ROC analysis.

Results

A total of 245 patients from two clinical centers (dataset 1, n = 202; dataset 2, n = 43) were screened. Pack-years of smoking, traction bronchiectasis, and nine radiomics features were used to build the radiomics nomogram, which showed favorable calibration and discrimination in the training cohort {AUC, 0.887 [95% confidence interval (CI): 0.827-0.940]}, the internal validation cohort [AUC, 0.885 (95% CI: 0.816-0.922)], and the external validation cohort [AUC, 0.85 (95% CI: 0.720-0.919)]. Decision curve analysis demonstrated that the nomogram outperformed the clinical factor model and radiomics signature in terms of clinical usefulness.

Conclusion

The CT-based radiomics nomogram showed favorable efficacy in predicting individual ILD-GAP stages.

A computed tomography-based radiomics nomogram for predicting overall survival in patients with connective tissue disease-associated interstitial lung disease

OBJECTIVES

Accurate prognostic prediction is beneficial for the management of patients with connective tissue disease-associated interstitial lung disease (CTD-ILD). The purpose of the present study was to develop and validate a nomogram using clinical features and computed tomography (CT) based radiomics features to predict overall survival (OS) in patients with CTD-ILD, and to assess the incremental prognostic value the radiomics might add to clinical risk factors.

MATERIALS & METHODS

Patients from two clinical centers with CTD-ILD were enrolled in the present retrospective study. A radiomics signature, a clinical model and a combined nomogram were developed and assessed in the cohorts. The incremental value of radiomics signature to the clinical independent risk factors in survival prediction was evaluated. The models were externally validated to evaluate the model generalization ability.

RESULTS

A total of 215 patients (mean age, 53 years ± 14 [standard deviation], 45 men) were evaluated. Patients with higher radiomics scores had higher mortality risk than those with lower radiomics scores (Hazard ratio, 12.396; 95% CI, 3.364-45.680; P < 0.001). The combined nomogram showed better predictive capability than the clinical model did with higher C-indices (0.800, 0.738, 0.742 vs. 0.747, 0.631, 0.587 in the training, internal- and external-validation cohort, respectively), time-AUCs and overall net-benefit.

CONCLUSION

The radiomics signature is a potential prognostic biomarker of CTD-ILD and add incremental value to the clinical independent risk factors. The combined nomogram can provide a more accurate estimation of OS than the clinical model for CTD-ILD patients.

CLINICAL RELEVANCE STATEMENT

The developed combined nomogram showed accurate prognostic prediction performance, which is beneficial for the management of CTD-ILD patients. It also proved radiomics could extract prognostic information from CT images.

Acute Exacerbations of Interstitial Lung Diseases: Focus on Biomarkers

Interstitial lung diseases (ILDs) are a large group of pulmonary disorders characterized histologically by the cardinal involvement of the pulmonary interstitium. The prototype of ILDs is idiopathic pulmonary fibrosis (IPF), an incurable disease characterized by progressive distortion and loss of normal lung architecture through unchecked collagen deposition. Acute exacerbations are dramatic events during the clinical course of ILDs, associated with high morbidity and mortality. Infections, microaspiration, and advanced lung disease might be involved in the pathogenesis of acute exacerbations. Despite clinical scores, the prediction of the onset and outcome of acute exacerbations is still inaccurate. Biomarkers are necessary to characterize acute exacerbations better. We review the evidence for alveolar epithelial cell, fibropoliferation, and immunity molecules as potential biomarkers for acute exacerbations of interstitial lung disease.