Selection of glucocorticoid-sensitive patients in interstitial lung disease secondary to connective tissue diseases population by radiomics

Feng, Ding-Yun; Zhou, Yuqi; Xing, Yan‐Fang; Li, Chuang-Feng; Lv, Qing; Jie, Dong; Qin, Jie; Guo, Vivian Yawei; Jiang, Nan; Huang, Chencui; Hu, Haitao; Guo, Xinghua; Chen, Jie; Li, Yin; Zhang, Tiantuo; Li, Xing

doi:10.2147/tcrm.s181043

Cited by 13 publications

(13 citation statements)

References 26 publications

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“…Researchers used radiomics method to seek association between hidden information in medical images and clinical data, to enrich diagnosis ability for radiologists, and to show advantages in prognostic power. For example, Feng et al 32 suggested that radiomics‐based predictive models reliably identified glucocorticoid‐sensitive CTD‐ILD patients. Therefore, high‐throughput (3234 radiomics features) and high‐dimensional texture features of lesions were extracted, which may reflect the underlying histological differences between PI and CAP.…”

Section: Discussionmentioning

confidence: 99%

Radiomics study on pulmonary infarction mimicking community‐acquired pneumonia

Gao

Zhai

et al. 2021

Clinical Respiratory J

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Introduction and Objectives Pulmonary infarction (PI) shares similar symptoms and imaging presentations with community‐acquired pneumonia (CAP), which might delay diagnosis and lead to devastating consequences. Noncontrast computed tomography (CT) is the first‐line examination for the patients with the respiratory symptoms. This study aimed to investigate a radiomics method to differentiate PI from CAP using noncontrast‐enhanced CT. Methods Noncontrast‐enhanced CT images of 54 patients with PI and 64 patients with CAP were retrospectively selected. All patients were confirmed using computed tomography pulmonary angiography (CTPA). A radiomics model was built with 18 texture features that showed significant differences between PI and CAP patients. For comparison, a clinical model using clinical biomarkers and an integrated model combining the radiomics and clinical biomarkers were also generated. An experienced radiologist performed diagnoses using the noncontrast‐enhanced CT images. The parameters of the models were generated using a training dataset of 61 patients, whereas the performance of the models was evaluated using receiver operating characteristic (ROC) analysis and Harrell's concordance index (C‐index) applied to a separate validation dataset of 57 patients. Results The integrated model achieved the best performance (C‐index 0.760, sensitivity 0.703, specificity 0.867, positive predictive value [PPV] 0.826, and negative predictive value [NPV] 0.765). The radiomics model was better than both the clinical model and the radiologist's interpretations (C‐index 0.721, 0.707, 0.665, respectively; sensitivity 0.667, 0.630, 0.593; specificity 0.800, 0.785, 0.733; PPV 0.750, 0.739, 0.667; and NPV 0.727, 0.706, 0.667). Conclusions Radiomics features generated from noncontrast‐enhanced CT images allow PI to be differentiated from CAP with considerable accuracy. The radiomics‐based method could provide useful information in clinical practice.

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Section: Discussionmentioning

confidence: 99%

Radiomics study on pulmonary infarction mimicking community‐acquired pneumonia

Gao

Zhai

et al. 2021

Clinical Respiratory J

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“…Kloth et al noted differences in texture analysis parameters between active alveolitis and lung fibrosis in chest CT scans of patients with systemic sclerosis (30). Feng et al reported that radiomicsbased predictive models reliably identified patients with glucocorticoid-sensitive connective tissue disease-associated interstitial lung disease (31). Another study showed that these models could be used to differentiate primary progressive pulmonary tuberculosis from community- acquired pneumonia in children (32).…”

Section: Discussionmentioning

confidence: 99%

Early recognition of necrotizing pneumonia in children based on non-contrast-enhanced computed tomography radiomics signatures

Chen¹,

Li²,

Wang³

et al. 2021

Transl Pediatr

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Background: Necrotizing pneumonia (NP) is an infrequent but severe complication of pneumonia in children. In the early stages of NP, CT imaging shows lung consolidation, which cannot be detected in time.This study aimed to explore the ability of non-contrast-enhanced CT radiomics features to recognize NP in early stage.Methods: This was a retrospective study, and 250 patients who presented with lung consolidation on initial CT images were included in this study. After a follow-up period of 1-3 weeks, 116 patients developed NP, whose CT or X-ray shows cavitation or liquefied necrosis. Manual segmentation of lesion sites in the initial non-contrast-enhanced CT scans was performed with RadCloud (Huiying Medical Technology Co., Ltd., China), and 1,409 radiomics features were extracted. We used Variance threshold (0.8), SelectKBest, and the least absolute shrinkage and selection operator (LASSO) methods for feature dimension reduction.Three machine learning algorithms, k-nearest neighbor (KNN), support vector machine (SVM), and logistic regression (LR) models, were established to recognize NP early. To assess the recognition performance, the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and other indicators were used in the validation cohort.Results: Radiomics features helped to recognize NP in early stage in both the training and validation cohorts. The AUC (sensitivity, specificity) for the training and validation cohorts were 0.81 (0.73, 0.68) and 0.71 (0.61, 0.65) for KNN, respectively; 0.81 (0.72, 0.70) and 0.77 (0.66, 0.65) for SVM, respectively; and 0.82 (0.73, 0.73) and 0.76 (0.63, 0.70) for LR, respectively. Recall and F1-scores determined that LR performed better at diagnosing early NP, with the values of the above two indexes being 0.70 and 0.67, respectively.Conclusions: Non-contrast-enhanced CT-based radiomics models may be helpful for recognizing NP in early stage.

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“…A previous study applied radiomics-based predictive models using random forest (RF) and kNN classifiers to identify glucocorticoid-sensitive connective tissue disease-related interstitial lung disease, obtaining AUC of 0.66 in RF models and 0.61 in kNN model. 32 Zhang et al 33 incorporated CT-radiomics and PET metabolic parameters to build a classification model (using a SVM method) for distinguishing benign and malignant lung lesions. Their model showed a substantial diagnostic capacity.…”

Section: Discussionmentioning

confidence: 99%

A CT-based radiomics nomogram for predicting prognosis of coronavirus disease 2019 (COVID-19) radiomics nomogram predicting COVID-19

Chen

Zeng

Wang

et al. 2020

The British Journal of Radiology

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Objectives: To identify the value of radiomics method derived from CT images to predict prognosis in patients with COVID-19. Methods: A total of 40 patients with COVID-19 were enrolled in the study. Baseline clinical data, CT images, and laboratory testing results were collected from all patients. We defined that ROIs in the absorption group decreased in the density and scope in GGO, and ROIs in the progress group progressed to consolidation. A total of 180 ROIs from absorption group (n = 118) and consolidation group (n = 62) were randomly divided into a training set (n = 145) and a validation set (n = 35) (8:2). Radiomics features were extracted from CT images, and the radiomics-based models were built with three classifiers. A radiomics score (Rad-score) was calculated by a linear combination of selected features. The Rad-score and clinical factors were incorporated into the radiomics nomogram construction. The prediction performance of the clinical factors model and the radiomics nomogram for prognosis was estimated. Results: A total of 15 radiomics features with respective coefficients were calculated. The AUC values of radiomics models (kNN, SVM, and LR) were 0.88, 0.88, and 0.84, respectively, showing a good performance. The C-index of the clinical factors model was 0.82 [95% CI (0.75–0.88)] in the training set and 0.77 [95% CI (0.59–0.90)] in the validation set. The radiomics nomogram showed optimal prediction performance. In the training set, the C-index was 0.91 [95% CI (0.85–0.95)], and in the validation set, the C-index was 0.85 [95% CI (0.69–0.95)]. For the training set, the C-index of the radiomics nomogram was significantly higher than the clinical factors model (p = 0.0021). Decision curve analysis showed that radiomics nomogram outperformed the clinical model in terms of clinical usefulness. Conclusions: The radiomics nomogram based on CT images showed favorable prediction performance in the prognosis of COVID-19. The radiomics nomogram could be used as a potential biomarker for more accurate categorization of patients into different stages for clinical decision-making process. Advances in knowledge: Radiomics features based on chest CT images help clinicians to categorize the patients of COVID-19 into different stages. Radiomics nomogram based on CT images has favorable predictive performance in the prognosis of COVID-19. Radiomics act as a potential modality to supplement conventional medical examinations.

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Selection of glucocorticoid-sensitive patients in interstitial lung disease secondary to connective tissue diseases population by radiomics

Cited by 13 publications

References 26 publications

Radiomics study on pulmonary infarction mimicking community‐acquired pneumonia

Radiomics study on pulmonary infarction mimicking community‐acquired pneumonia

Early recognition of necrotizing pneumonia in children based on non-contrast-enhanced computed tomography radiomics signatures

A CT-based radiomics nomogram for predicting prognosis of coronavirus disease 2019 (COVID-19) radiomics nomogram predicting COVID-19

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