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A deep learning integrated radiomics model for identification of coronavirus disease 2019 using computed tomography.

ABSTRACT: Since its first outbreak, Coronavirus Disease 2019 (COVID-19) has been rapidly spreading worldwide and caused a global pandemic. Rapid and early detection is essential to contain COVID-19. Here, we first developed a deep learning (DL) integrated radiomics model for end-to-end identification of COVID-19 using CT scans and then validated its clinical feasibility. We retrospectively collected CT images of 386 patients (129 with COVID-19 and 257 with other community-acquired pneumonia) from three medical centers to train and externally validate the developed models. A pre-trained DL algorithm was utilized to automatically segment infected lesions (ROIs) on CT images which were used for feature extraction. Five feature selection methods and four machine learning algorithms were utilized to develop radiomics models. Trained with features selected by L1 regularized logistic regression, classifier multi-layer perceptron (MLP) demonstrated the optimal performance with AUC of 0.922 (95% CI 0.856-0.988) and 0.959 (95% CI 0.910-1.000), the same sensitivity of 0.879, and specificity of 0.900 and 0.887 on internal and external testing datasets, which was equivalent to the senior radiologist in a reader study. Additionally, diagnostic time of DL-MLP was more efficient than radiologists (38 s vs 5.15 min). With an adequate performance for identifying COVID-19, DL-MLP may help in screening of suspected cases.

SUBMITTER: Zhang X

PROVIDER: S-EPMC7886892 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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A deep learning integrated radiomics model for identification of coronavirus disease 2019 using computed tomography.

Zhang Xiaoguo X Wang Dawei D Shao Jiang J Tian Song S Tan Weixiong W Ma Yan Y Xu Qingnan Q Ma Xiaoman X Li Dasheng D Chai Jun J Wang Dingjun D Liu Wenwen W Lin Lingbo L Wu Jiangfen J Xia Chen C Zhang Zhongfa Z

Scientific reports 20210216 1

Since its first outbreak, Coronavirus Disease 2019 (COVID-19) has been rapidly spreading worldwide and caused a global pandemic. Rapid and early detection is essential to contain COVID-19. Here, we first developed a deep learning (DL) integrated radiomics model for end-to-end identification of COVID-19 using CT scans and then validated its clinical feasibility. We retrospectively collected CT images of 386 patients (129 with COVID-19 and 257 with other community-acquired pneumonia) from three me ...[more]

PMID: 33594159

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Project description:BackgroundRadiomics 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.MethodsThis 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.ResultsIn 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).ConclusionsThe 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.

Project description:Rationale: Although non-contrast computed tomography (NCCT) is the recommended examination for the suspected acute ischemic stroke (AIS), it cannot detect significant changes in the early infarction. We aimed to develop a deep-learning model to identify early invisible AIS in NCCT and evaluate its diagnostic performance and capacity for assisting radiologists in decision making. Methods: In this multi-center, multi-manufacturer retrospective study, 1136 patients with suspected AIS but invisible lesions in NCCT were collected from two geographically distant institutions between May 2012 to May 2021. The AIS lesions were confirmed based on the follow-up diffusion-weighted imaging and clinical diagnosis. The deep-learning model was comprised of two deep convolutional neural networks to locate and classify. The performance of the model and radiologists was evaluated by the area under the receiver operator characteristic curve (AUC), sensitivity, specificity, and accuracy values with 95% confidence intervals. Delong's test was used to compare the AUC values, and a chi-squared test was used to evaluate the rate differences. Results: 986 patients (728 AIS, median age, 55 years, interquartile range [IQR]: 47-65 years; 664 males) were assigned to the training and internal validation cohorts. 150 patients (74 AIS, median age, 63 years, IQR: 53-75 years; 100 males) were included as an external validation cohort. The AUCs of the model were 83.61% (sensitivity, 68.99%; specificity, 98.22%; and accuracy, 89.87%) and 76.32% (sensitivity, 62.99%; specificity, 89.65%; and accuracy, 88.61%) for the internal and external validation cohorts based on the slices. The AUC of the model was much higher than that of two experienced radiologists (65.52% and 59.48% in the internal validation cohort; 64.01% and 64.39% in external validation cohort; all P < 0.001). The accuracy of two radiologists increased from 62.00% and 58.67% to 92.00% and 84.67% when assisted by the model for patients in the external validation cohort. Conclusions: This deep-learning model represents a breakthrough in solving the challenge that early invisible AIS lesions cannot be detected by NCCT. The model we developed in this study can screen early AIS and save more time. The radiologists assisted with the model can provide more effective guidance in making patients' treatment plan in clinic.

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Project description:Purpose To develop handcrafted radiomics (HCR) and deep learning (DL) based automated diagnostic tools that can differentiate between idiopathic pulmonary fibrosis (IPF) and non-IPF interstitial lung diseases (ILDs) in patients using high-resolution computed tomography (HRCT) scans. Material and Methods In this retrospective study, 474 HRCT scans were included (mean age, 64.10 years ± 9.57 [SD]). Five-fold cross-validation was performed on 365 HRCT scans. Furthermore, an external dataset comprising 109 patients was used as a test set. An HCR model, a DL model, and an ensemble of HCR and DL model were developed. A virtual in-silico trial was conducted with two radiologists and one pulmonologist on the same external test set for performance comparison. The performance was compared using DeLong method and McNemar test. Shapley Additive exPlanations (SHAP) plots and Grad-CAM heatmaps were used for the post-hoc interpretability of HCR and DL models, respectively. Results In five-fold cross-validation, the HCR model, DL model, and the ensemble of HCR and DL models achieved accuracies of 76.2 ± 6.8, 77.9 ± 4.6, and 85.2 ± 2.7%, respectively. For the diagnosis of IPF and non-IPF ILDs on the external test set, the HCR, DL, and the ensemble of HCR and DL models achieved accuracies of 76.1, 77.9, and 85.3%, respectively. The ensemble model outperformed the diagnostic performance of clinicians who achieved a mean accuracy of 66.3 ± 6.7% (p < 0.05) during the in-silico trial. The area under the receiver operating characteristic curve (AUC) for the ensemble model on the test set was 0.917 which was significantly higher than the HCR model (0.817, p = 0.02) and the DL model (0.823, p = 0.005). The agreement between HCR and DL models was 61.4%, and the accuracy and specificity for the predictions when both the models agree were 93 and 97%, respectively. SHAP analysis showed the texture features as the most important features for IPF diagnosis and Grad-CAM showed that the model focused on the clinically relevant part of the image. Conclusion Deep learning and HCR models can complement each other and serve as useful clinical aids for the diagnosis of IPF and non-IPF ILDs.

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A deep learning integrated radiomics model for identification of coronavirus disease 2019 using computed tomography.

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A deep learning integrated radiomics model for identification of coronavirus disease 2019 using computed tomography.

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