Radiomics for Improved Detection of Chronic Obstructive Pulmonary                     Disease in Low-Dose and Standard-Dose Chest CT Scans

Puchakayala, P.R. Amudala; Sthanam, V.; Nakhmani, Arie; Chaudhary, Muhammad F. A.; Puliyakote, A.S. Kizhakke; Reinhardt, Joseph M.; Zhang, Chengcui; Bhatt, Surya P.; Bodduluri, Sandeep

doi:10.1148/radiol.222998

Cited by 15 publications

(15 citation statements)

References 29 publications

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“…The AUC of the combined model with clinical and radiomic features was 0.893, 0.873, and 0.853 respectively, which was superior to the clinical model in the three cohorts, and slightly better than the radiomic model in the training and external validation cohorts. These findings are similar to the recent study published in Radiology [ 17 ], which indicated radiomic alone is a potent tool for identifying COPD. However, to provide a predicting tool for the probability of COPD occurrence at the individual level, we still constructed a nomogram based on the combined model.…”

Section: Discussionsupporting

confidence: 92%

“…In our research, we established a whole-lung radiomic signature describing airways, blood vessels, and emphysema, similar to Cho et al [ 23 ]. We found the radiomic model outperformed the clinical model, which is similar to a recent study by Amudala Puchakayala et al [ 17 ].…”

Section: Discussionsupporting

confidence: 91%

“…A recent study revealed that CT-based radiomic features extracted only from inspiratory CT scans outperformed existing advanced methods in detecting COPD on both standard- and low-dose CT scans. The model was constructed with the standard-dose CT radiomic feature [ 17 ].…”

Section: Discussionmentioning

confidence: 99%

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CT whole lung radiomic nomogram: a potential biomarker for lung function evaluation and identification of COPD

Zhou,

et al. 2024

Military Med Res

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Background Computed tomography (CT) plays a great role in characterizing and quantifying changes in lung structure and function of chronic obstructive pulmonary disease (COPD). This study aimed to explore the performance of CT-based whole lung radiomic in discriminating COPD patients and non-COPD patients. Methods This retrospective study was performed on 2785 patients who underwent pulmonary function examination in 5 hospitals and were divided into non-COPD group and COPD group. The radiomic features of the whole lung volume were extracted. Least absolute shrinkage and selection operator (LASSO) logistic regression was applied for feature selection and radiomic signature construction. A radiomic nomogram was established by combining the radiomic score and clinical factors. Receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) were used to evaluate the predictive performance of the radiomic nomogram in the training, internal validation, and independent external validation cohorts. Results Eighteen radiomic features were collected from the whole lung volume to construct a radiomic model. The area under the curve (AUC) of the radiomic model in the training, internal, and independent external validation cohorts were 0.888 [95% confidence interval (CI) 0.869–0.906], 0.874 (95%CI 0.844–0.904) and 0.846 (95%CI 0.822–0.870), respectively. All were higher than the clinical model (AUC were 0.732, 0.714, and 0.777, respectively, P < 0.001). DCA demonstrated that the nomogram constructed by combining radiomic score, age, sex, height, and smoking status was superior to the clinical factor model. Conclusions The intuitive nomogram constructed by CT-based whole-lung radiomic has shown good performance and high accuracy in identifying COPD in this multicenter study.

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Section: Discussionsupporting

confidence: 92%

Section: Discussionsupporting

confidence: 91%

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CT whole lung radiomic nomogram: a potential biomarker for lung function evaluation and identification of COPD

Zhou,

et al. 2024

Military Med Res

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“…A recent study of COPD identification by a ML support vector machine classifier had an AUC of 0.97 (95% CI: 0.964–0.977) in the test set ( 14 ). Another study for COPD detection by radiomics features showed that the radiomics features model had an AUC of 0.90 (95% CI: 0.89–0.92) in the standard-dose CT scans and an AUC of 0.87 (95% CI: 0.83–0.91) in the LDCT scans ( 15 ). The performance of the model was better than that of the models in our study.…”

Section: Discussionmentioning

confidence: 99%

“…Li et al ( 14 ) evaluated the role of two radiomics classification CT-based methods in the identification of COPD, and the models achieved AUCs of 0.970 (95% CI: 0.964–0.977) and 0.972 (95% CI: 0.969–0.975) in the test set, respectively. Another recent study ( 15 ) assessed the performance of radiomics features in COPD detection using CT images and reported an AUC of 0.90 (95% CI: 0.89–0.92) in the standard-dose CT model, and an AUC of 0.87 (95% CI: 0.83–0.91) in the LDCT model. Many investigations have developed approaches to predict COPD based on CT radiomics and AI; however, it is still unclear which approach has the best performance and could be most beneficial to apply as a clinical decision support system.…”

Section: Introductionmentioning

confidence: 99%

Comparison of deep-learning and radiomics-based machine-learning methods for the identification of chronic obstructive pulmonary disease on low-dose computed tomography images

Guan,

Zhang,

Zhou

et al. 2024

Quant Imaging Med Surg

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Background Radiomics and artificial intelligence approaches have been developed to predict chronic obstructive pulmonary disease (COPD), but it is still unclear which approach has the best performance. Therefore, we established five prediction models that employed deep-learning (DL) and radiomics-based machine-learning (ML) approaches to identify COPD on low-dose computed tomography (LDCT) images and compared the relative performance of the different models to find the best model for identifying COPD. Methods This retrospective analysis included 1,024 subjects (169 COPD patients and 855 control subjects) who underwent LDCT scans from August 2018 to July 2021. Five prediction models, including models that employed computed tomography (CT)-based radiomics features, chest CT images, quantitative lung density parameters, and demographic and clinical characteristics, were established to identify COPD by DL or ML approaches. Model 1 used CT-based radiomics features by ML method. Model 2 used a combination of CT-based radiomics features, lung density parameters, and demographic and clinical characteristics by ML method. Model 3 used CT images only by DL method. Model 4 used a combination of CT images, lung density parameters, and demographic and clinical characteristics by DL method. Model 5 used a combination of CT images, CT-based radiomics features, lung density parameters, and demographic and clinical characteristics by DL method. The accuracy, sensitivity, specificity, highest negative predictive values (NPVs), positive predictive values, and areas under the receiver operating characteristic (AUC) curve of the five prediction models were compared to examine their performance. The DeLong test was used to compare the AUCs of the different models. Results In total, 107 radiomics features were extracted from each subject’s CT images, 17 lung density parameters were acquired by quantitative measurement, and 18 selected demographic and clinical characteristics were recorded in this study. Model 2 had the highest AUC [0.73, 95% confidence interval (CI): 0.64–0.82], while model 3 had the lowest AUC (0.65, 95% CI: 0.55–0.75) in the test set. Model 2 also had the highest sensitivity (0.84), the highest accuracy (0.81), and the highest NPV (0.36). In the test set, based on the AUC results, Model 2 significantly outperformed Model 1 (P=0.03). Conclusions The results showed that the identification ability of models that employ CT-based radiomics features combined with lung density parameters, and demographic and clinical characteristics using ML methods performed better than the chest CT image-based DL methods. ML methods are more suitable and beneficial for COPD identification.

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Acute exacerbation prediction of COPD based on Auto-metric graph neural network with inspiratory and expiratory chest CT images

Wang,

Li,

Zeng

et al. 2024

Heliyon

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Radiomics for Improved Detection of Chronic Obstructive Pulmonary Disease in Low-Dose and Standard-Dose Chest CT Scans

Cited by 15 publications

References 29 publications

CT whole lung radiomic nomogram: a potential biomarker for lung function evaluation and identification of COPD

CT whole lung radiomic nomogram: a potential biomarker for lung function evaluation and identification of COPD

Comparison of deep-learning and radiomics-based machine-learning methods for the identification of chronic obstructive pulmonary disease on low-dose computed tomography images

Acute exacerbation prediction of COPD based on Auto-metric graph neural network with inspiratory and expiratory chest CT images

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