Dataset Information

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

ABSTRACT:

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.

SUBMITTER: Qin S

PROVIDER: S-EPMC10587549 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Publications

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

Qin Songnan S Jiao Bingxuan B Kang Bing B Li Haiou H Liu Hongwu H Ji Congshan C Yang Shifeng S Yuan Hongtao H Wang Ximing X

Frontiers in immunology 20231006

<h4>Rationale and introduction</h4>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.<h4>Materials and methods</h4>Patients with CTD-ILD were staged using the ILD-GAP index system. A clinical factor ...[more]

PMID: 37868980

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Project description:BackgroundHigh-resolution computed tomography (HRCT) is recommended diagnosing and monitoring connective tissue disease-associated interstitial lung disease (CTD-ILD). Quantitative computed tomography has the potential to precisely assess the radiological severity of CTD-ILD, but has still been under study.ObjectiveTo investigate whether dual-energy computed tomography (DECT), a novel quantitative technique, can be used for quantitative severity assessment in CTD-ILD.MethodsThis cross sectional study recruited adult CTD-ILD patients who underwent DECT scans from the ICE study between October 2019 and November 2021. DECT parameters, including effective atomic number (Zeff), lung (lobe) volume, and monochromatic CT number (MCTN) of each lung lobe, were evaluated. CTD-ILD was classified into extensive CTD-ILD and limited CTD-ILD by staging algorithm using combined forced vital capacity (FVC)%predicted and total extent of ILD (TEI) on CT. Dyspnea, cough, and life quality were scored by Borg dyspnea score, Leicester cough questionnaire (LCQ), and short-form 36 health survey questionnaire (SF-36), respectively.ResultsThere was a total of 147 patients with DECT scans enrolled. Higher Zeff value (3.104 vs 2.256, p < 0.001), higher MCTN (- 722.87 HU vs - 802.20 HU, p < 0.001), and lower lung volume (2309.51cm3 vs 3475.21cm3, p < 0.001) were found in extensive CTD-ILD compared with limited CTD-ILD. DECT parameters had significant moderate correlations with FVC%predicted (|r|= 0.542-0.667, p < 0.01), DLCO%predicted (|r|= 0.371-0.427, p < 0.01), and TEI (|r|= 0.485-0.742, p < 0.01). Receiver operating characteristic (ROC) analysis indicated MCTN averaged over the whole lung had the best performance for extensive CTD-ILD discrimination (AUC = 0.901, cut-off: - 762.30 HU, p < 0.001), with a sensitivity of 82.1% and a specificity of 85.4%. The Zeff value was the independent risk factor for dyspnea (OR = 3.644, 95% CI: 1.846-7.192, p < 0.001) and cough (OR = 3.101, 95% CI: 1.528-6.294, p = 0.002), and lung volume significantly contributed to the mental component summary (MCS) in SF-36 (standardized β = 0.198, p < 0.05).ConclusionsDECT can be applied to evaluate the severity of CTD-ILD.

Project description:The lung is a common site of complications of systemic connective tissue disease (CTD), and lung involvement can present in several ways. Interstitial lung disease (ILD) and pulmonary hypertension are the most common lung manifestations in CTD. Although it is generally thought that interstitial lung disease develops later on in CTD it is often the initial presentation ("lung dominant" CTD). ILD can be present in most types of CTD, including rheumatoid arthritis, scleroderma, systemic lupus erythematosus, polymyositis or dermatomyositis, Sjögren's syndrome, and mixed connective tissue disease. Despite similarities in clinical and pathologic presentation, the prognosis and treatment of CTD associated ILD (CTD-ILD) can differ greatly from that of other forms of ILD, such as idiopathic pulmonary fibrosis. Pulmonary hypertension (PH) can present as a primary vasculopathy in pulmonary arterial hypertension or in association with ILD (PH-ILD). Therefore, detailed history, physical examination, targeted serologic testing, and, occasionally, lung biopsy are needed to diagnose CTD-ILD, whereas both non-invasive and invasive assessments of pulmonary hemodynamics are needed to diagnose pulmonary hypertension. Immunosuppression is the mainstay of treatment for ILD, although data from randomized controlled trials (RCTs) to support specific treatments are lacking. Furthermore, treatment strategies vary according to the clinical situation-for example, the treatment of a patient newly diagnosed as having CTD-ILD differs from that of someone with an acute exacerbation of the disease. Immunosuppression is indicated only in select cases of pulmonary arterial hypertension related to CTD; more commonly, selective pulmonary vasodilators are used. For both diseases, comorbidities such as sleep disordered breathing, symptoms of dyspnea, and cough should be evaluated and treated. Lung transplantation should be considered in patients with advanced disease but is not always feasible because of other manifestations of CTD and comorbidities. Clinical trials of novel therapies including immunosuppressive therapies are needed to inform best treatment strategies.

Project description:BackgroundThe accuracy of preoperative staging is crucial for cT4 stage gastric cancer patients. The aim of this study was to develop the radiomics model and evaluate its predictive potential for differentiating preoperative cT4 stage gastric cancer patients into pT4b and no-pT4b patients.MethodsA multicenter retrospective analysis of 704 gastric cancer patients with preoperative contrast-enhanced computed tomography (CE-CT) staging cT4 between January 2008 and December 2021. These patients were divided into the training cohort (478 patients, the Affiliated Hospital of Qingdao University) and validation cohort (226 patients, the Weihai Wendeng District People's Hospital). According to the pathological stage of the tumors, the patients were divided into pT4b or no-pT4b stage. In the training cohort, the clinical and radiomics features were analyzed to construct the clinical model, tri-phase radiomics signatures and nomogram. Two kinds of methods were employed to achieve dimensionality reduction: (I) the least absolute shrinkage and selection operator (LASSO); and (II) the minimum redundancy maximum relevance (mRMR) algorithms. We utilized Logistic regression, support vector machine (SVM), Decision tree and Adaptive boosted tree (AdaBoost) algorithms as the machine learning classifiers. The nomogram was constructed on the clinical characteristics and the Rad-score. The performance of the models was evaluated by receiver operating characteristic (ROC) area under the curve (AUC), Decision Curve Analysis (DCA) curve and calibration curve.ResultsThe 345 pT4b and 359 no-pT4b stage patients were included in this study. In the validation cohort, the AUC of the clinical model was 0.793 (95% CI: 0.732-0.855). The tri-phase radiomics features combined with the SVM algorithm was the best radiomics signature with an AUC of 0.862 (95% CI: 0.812-0.912). The nomogram was the best predictive model of all with an AUC of 0.893 (95% CI: 0.834-0.927). In the training and validation cohorts, the calibration curves and DCA curves of the nomogram showed satisfactory result.ConclusionsCE-CT-based radiomics nomogram offers good accuracy and stability in differentiating preoperative cT4 stage gastric cancer patients into pT4b and non-pT4b stages, which has a great clinical relevance for selecting the course of treatment for cT4 stage gastric cancer patients.

Project description:ObjectiveThe aim of this study was to assess cardiovascular involvement in patients with connective tissue disease (CTD), and determine whether interstitial lung disease (ILD) in these patients is associated with elevated cardiovascular risk.MethodsThis study evaluated a retrospective cohort of 436 CTD patients admitted to a large teaching hospital in Zhejiang province, China, along with an additional 436 participants of an annual community health screening conducted in the physical examination center who served as age- and gender-matched controls. Demographic, clinical, serologic and imaging characteristics, as well as medications used by each participant were recorded. Cardiovascular involvement was defined by uniform criteria. Correlations between clinical/serologic factors and cardiovascular involvement were determined by univariate and multivariate analyses.ResultsCTD patients had a significantly higher cardiovascular involvement rate than controls (64.7% vs 23.4%), with higher rates of diabetes, hypertension, and hyperlipidemia, elevated systolic and diastolic pressures, C-reactive protein, total cholesterol, and low-density lipoprotein cholesterol, and lower albumin and high-density lipoprotein cholesterol (all p < 0.05). Furthermore, CTP patients with cardiovascular involvement were significantly older, had higher systolic and diastolic pressures, C-reactive protein, glucose, and uric acid, higher rates of diabetes, hypertension, and use of moderate- to high-dose glucocorticoids, and longer disease duration compared to patients without involvement (all p < 0.05). Moreover, CTD in patients with cardiovascular involvement was more likely to be complicated by ILD (p < 0.01), which manifested as a higher alveolar inflammation score (p < 0.05). In the multivariate analysis, cardiovascular involvement in CTD patients was associated with age, systolic pressure, body mass index, uric acid, disease duration > 2 years, use of moderate- to high-dose glucocorticoids, and ILD with a high alveolar inflammation score.ConclusionCardiovascular involvement is increased in CTD patients, and is associated with ILD with a higher alveolar inflammation score. Thus, early-stage echocardiography and CT scans should be used to detect potential cardiovascular complications in these patients.

Dataset Information

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

Rationale and introduction

Materials and methods

Results

Conclusion

Publications

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

Similar Datasets

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