Radiomics approach for survival prediction in chronic obstructive pulmonary disease

Cho, Yong-Hoon; Seo, Joon Beom; Lee, Sang Min; Kim, Namkug; Yun, Jihye; Hwang, Jeong Eun; Lee, Jae Seung; Oh, Yeon‐Mok; Lee, Sang Do; Loh, Li‐Cher; Ong, C.K.

doi:10.1007/s00330-021-07747-7

Cited by 13 publications

(10 citation statements)

References 38 publications

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“…In addition, studies have shown that imaging omics methods for predicting survival and risk stratification in patients with COPD are feasible. 22 The constructed radiomics model exhibits acceptable performance and has been confirmed by consistent results from external validation groups. 22 Li et al constructed a highly effective COPD diagnostic model based on CT imaging omics; 23 Lafata et al constructed a predictive model based on CT imaging omics to quantify changes in lung function and accurately diagnose the severity of COPD related to spirometry testing.…”

Section: Discussionsupporting

confidence: 60%

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Study on Predicting Clinical Stage of Patients with Bronchial Asthma Based on CT Radiomics

Chen,

Wang,

Huang

et al. 2024

JAA

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Objective To explore the value of a new model based on CT radiomics in predicting the staging of patients with bronchial asthma (BA). Methods Patients with BA from 2018 to 2021 were retrospectively analyzed and underwent plain chest CT before treatment. According to the guidelines for the prevention and treatment of BA (2016 edition), they were divided into two groups: acute attack and non-acute attack. The images were processed as follows: using Lung Kit software for image standardization and segmentation, using AK software for image feature extraction, and using R language for data analysis and model construction (training set: test set = 7: 3). The efficacy and clinical effects of the constructed model were evaluated with ROC curve, sensitivity, specificity, calibration curve and decision curve. Results A total of 112 patients with BA were enrolled, including 80 patients with acute attack (range: 2–86 years old, mean: 53.89±17.306 years old, males of 33) and 32 patients with non-acute attack (range: 4–79 years old, mean: 57.38±19.223 years old, males of 18). A total of 10 imaging features are finally retained and used to construct model using multi-factor logical regression method. In the training group, the AUC, sensitivity and specificity of the model was 0.881 (95% CI:0.808–0.955), 0.804 and 0.818, separately; while in the test group, it was 0.792 (95% CI:0.608–0.976), 0.792 and 0.80, respectively. Conclusion The model constructed based on radiomics has a good effect on predicting the staging of patients with BA, which provides a new method for clinical diagnosis of staging in BA patients.

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Section: Discussionsupporting

confidence: 60%

“… 6 Studies have shown that quantitative imaging plays an important role in describing asthma phenotypes and predicting future asthma incidence. 7 , 8 This study explores the value of CT radiomics models in predicting the clinical stage of bronchial asthma, and constructs and evaluates prediction models.…”

Section: Introductionmentioning

confidence: 99%

Study on Predicting Clinical Stage of Patients with Bronchial Asthma Based on CT Radiomics

Chen,

Wang,

Huang

et al. 2024

JAA

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“…However, it is difficult to detect early fSAD before the change of pulmonary function. We lack methods to determine which patients will develop fSAD that will eventually evolve into COPD ( 14 ). Currently, there are no effective predictive biomarkers for fSAD.…”

Section: Discussionmentioning

confidence: 99%

A novel computed tomography radiomic nomogram for early evaluation of small airway dysfunction development

Cui

Shu²,

Ma³

et al. 2022

Front. Med.

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The common respiratory abnormality, small airway dysfunction (fSAD), is easily neglected. Its prognostic factors, prevalence, and risk factors are unclear. This study aimed to explore the early detection of fSAD using radiomic analysis of computed tomography (CT) images to predict fSAD progress. The patients were divided into fSAD and non-fSAD groups and divided randomly into a training group (n = 190) and a validation group (n = 82) at a 7:3 ratio. Lung kit software was used for automatic delineation of regions of interest (ROI) on chest CT images. The most valuable imaging features were selected and a radiomic score was established for risk assessment. Multivariate logistic regression analysis showed that age, radiomic score, smoking, and history of asthma were significant predictors of fSAD (P < 0.05). Results suggested that the radiomic nomogram model provides clinicians with useful data and could represent a reliable reference to form fSAD clinical treatment strategies.

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“…[15,16] Successful applications of radiomics in pulmonary lesion have been reported in detection of lung cancer, prediction of histology and subtype, assessment of treatment effect, and prediction of survival. [1720] However, to the best of our knowledge, no study has evaluated radiomics for its ability to differentiate pneumonia-like PPL from infectious pneumonia.…”

Section: Introductionmentioning

confidence: 99%

Development and validation of a CT-based radiomics model for differentiating pneumonia-like primary pulmonary lymphoma from infectious pneumonia: A multicenter study

Kang

Nie

et al. 2023

Chinese Medical Journal

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Background: Pneumonia-like primary pulmonary lymphoma (PPL) was commonly misdiagnosed as infectious pneumonia, leading to delayed treatment. The purpose of this study was to establish a computed tomography (CT)-based radiomics model to differentiate pneumonia-like PPL from infectious pneumonia. Methods: In this retrospective study, 79 patients with pneumonia-like PPL and 176 patients with infectious pneumonia from 12 medical centers were enrolled. Patients from center 1 to center 7 were assigned to the training or validation cohort, and the remaining patients from other centers were used as the external test cohort. Radiomics features were extracted from CT images. A three-step procedure was applied for radiomics feature selection and radiomics signature building, including the inter-and intra-class correlation coefficients (ICCs), a one-way analysis of variance (ANOVA), and least absolute shrinkage and selection operator (LASSO). Univariate and multivariate analyses were used to identify the significant clinicoradiological variables and construct a clinical factor model. Two radiologists reviewed the CT images for the external test set. Performance of the radiomics model, clinical factor model, and each radiologist were assessed by receiver operating characteristic, and area under the curve (AUC) was compared. Results: A total of 144 patients (44 with pneumonia-like PPL and 100 infectious pneumonia) were in the training cohort, 38 patients (12 with pneumonia-like PPL and 26 infectious pneumonia) were in the validation cohort, and 73 patients (23 with pneumonia-like PPL and 50 infectious pneumonia) were in the external test cohort. Twenty-three radiomics features were selected to build the radiomics model, which yielded AUCs of 0.95 (95% confidence interval [CI]: 0.94 -0.99), 0.93 (95% CI: 0.85 -0.98), and 0.94 (95% CI: 0.87-0.99) in the training, validation, and external test cohort, respectively. The AUCs for the two readers and clinical factor model were 0.74 (95% CI: 0.63-0.83), 0.72 (95% CI: 0.62-0.82), and 0.73 (95% CI: 0.62-0.84) in the external test cohort, respectively. The radiomics model outperformed both the readers' interpretation and clinical factor model (P <0.05). Conclusions:The CT-based radiomics model may provide an effective and non-invasive tool to differentiate pneumonia-like PPL from infectious pneumonia, which might provide assistance for clinicians in tailoring precise therapy.

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Radiomics approach for survival prediction in chronic obstructive pulmonary disease

Cited by 13 publications

References 38 publications

Study on Predicting Clinical Stage of Patients with Bronchial Asthma Based on CT Radiomics

Study on Predicting Clinical Stage of Patients with Bronchial Asthma Based on CT Radiomics

A novel computed tomography radiomic nomogram for early evaluation of small airway dysfunction development

Development and validation of a CT-based radiomics model for differentiating pneumonia-like primary pulmonary lymphoma from infectious pneumonia: A multicenter study

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