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Dong W, Xiong S, Wang X, Hu S, Liu Y, Liu H, Wang X, Chen J, Qiu Y, Fan B. Development and validation of a contrast-enhanced CT-based radiomics nomogram for differentiating mass-like thymic hyperplasia and low-risk thymoma. J Cancer Res Clin Oncol 2023; 149:14901-14910. [PMID: 37604939 DOI: 10.1007/s00432-023-05263-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 08/08/2023] [Indexed: 08/23/2023] [Imported: 08/29/2023]
Abstract
PURPOSE To explore the efficiency of a contrast-enhanced CT-based radiomics nomogram integrated with radiomics signature and clinically independent predictors to distinguish mass-like thymic hyperplasia (ml-TH) from low-risk thymoma (LRT) preoperatively. METHODS 135 Patients with histopathology confirmed ml-TH (n = 65) and LRT (n = 70) were randomly divided into training set (n = 94) and validation set (n = 41) at a ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) algorithm was used to obtain the optimal features. Based on the selected features, four machine learning models, support vector machine (SVM), logistic regression (LR), extreme gradient boosting (XGBOOST), and random forest (RF) were constructed. Multivariate logistic regression was used to establish a radiomics nomogram containing clinically independent predictors and radiomics signature. Receiver operating characteristic (ROC), DeLong test, and calibration curves were used to detect the performance of the radiomics nomogram in training set and validation set. RESULTS In the validation set, the area under the curve (AUC) value of LR (0.857; 95% CI: 0.741, 0.973) was the highest of the four machine learning models. Radiomics nomogram containing radiomics signature and clinically independent predictors (including age, shape, and net enhancement degree) had better calibration and identification in the training set (AUC: 0.959; 95% CI: 0.922, 0.996) and validation set (AUC: 0.895; 95% CI: 0.795, 0.996). CONCLUSION We constructed a contrast-enhanced CT-based radiomics nomogram containing clinically independent predictors and radiomics signature as a noninvasive preoperative prediction method to distinguish ml-TH from LRT. The radiomics nomogram we constructed has potential for preoperative clinical decision making.
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Affiliation(s)
- Wentao Dong
- Department of Radiology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
| | - Situ Xiong
- Medical College of Nanchang University, Nanchang University, Nanchang, China
| | - Xiaolian Wang
- Department of Radiology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
| | - Shaobo Hu
- Department of Radiology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
| | - Yangchun Liu
- Department of Thoracic Surgery, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
| | - Hao Liu
- R&D, Yizhun Medical AI, Beijing, China
| | - Xin Wang
- R&D, Yizhun Medical AI, Beijing, China
| | | | - Yingying Qiu
- Department of Radiology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China.
| | - Bing Fan
- Department of Radiology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China.
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Liu J, Li S, Lin H, Pang P, Luo P, Fan B, Yu J. Development of MRI-based radiomics predictive model for classifying endometrial lesions. Sci Rep 2023; 13:1590. [PMID: 36709399 DOI: 10.1038/s41598-023-28819-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Accepted: 01/25/2023] [Indexed: 01/30/2023] [Imported: 08/29/2023] Open
Abstract
An unbiased and accurate diagnosis of benign and malignant endometrial lesions is essential for the gynecologist, as each type might require distinct treatment. Radiomics is a quantitative method that could facilitate deep mining of information and quantification of the heterogeneity in images, thereby aiding clinicians in proper lesion diagnosis. The aim of this study is to develop an appropriate predictive model for the classification of benign and malignant endometrial lesions, and evaluate potential clinical applicability of the model. 139 patients with pathologically-confirmed endometrial lesions from January 2018 to July 2020 in two independent centers (center A and B) were finally analyzed. Center A was used for training set, while center B was used for test set. The lesions were manually drawn on the largest slice based on the lesion area by two radiologists. After feature extraction and feature selection, the possible associations between radiomics features and clinical parameters were assessed by Uni- and multi- variable logistic regression. The receiver operator characteristic (ROC) curve and DeLong validation were employed to evaluate the possible predictive performance of the models. Decision curve analysis (DCA) was used to evaluate the net benefit of the radiomics nomogram. A radiomics prediction model was established from the 15 selected features, and were found to be relatively high discriminative on the basis of the area under the ROC curve (AUC) for both the training and the test cohorts (AUC = 0.90 and 0.85, respectively). The radiomics nomogram also showed good performance of discrimination for both the training and test cohorts (AUC = 0.91 and 0.86, respectively), and the DeLong test shows that AUCs were significantly different between clinical parameters and nomogram. The result of DCA demonstrated the clinical usefulness of this novel nomogram method. The predictive model constructed based on MRI radiomics and clinical parameters indicated a highly diagnostic efficiency, thereby implying its potential clinical usefulness for the precise identification and prediction of endometrial lesions.
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Dong W, Xiong S, Lei P, Wang X, Liu H, Liu Y, Zou H, Fan B, Qiu Y. Application of a combined radiomics nomogram based on CE-CT in the preoperative prediction of thymomas risk categorization. Front Oncol 2022; 12:944005. [PMID: 36081562 PMCID: PMC9446086 DOI: 10.3389/fonc.2022.944005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Accepted: 08/01/2022] [Indexed: 12/04/2022] [Imported: 08/29/2023] Open
Abstract
Objective This study aimed to establish a combined radiomics nomogram to preoperatively predict the risk categorization of thymomas by using contrast-enhanced computed tomography (CE-CT) images. Materials and Methods The clinical, pathological, and CT data of 110 patients with thymoma (50 patients with low-risk thymomas and 60 patients with high-risk thymomas) collected in our Hospital from July 2017 to March 2022 were retrospectively analyzed. The study subjects were randomly divided into the training set (n = 77) and validation set (n = 33) in a 7:3 ratio. Radiomics features were extracted from the CT images, and the least absolute shrinkage and selection operator (LASSO) algorithm was performed to select 13 representative features. Five models, including logistic regression (LR), support vector machine (SVM), random forest (RF), decision tree (DT), and gradient boosting decision tree (GBDT) were constructed to predict thymoma risks based on these features. A combined radiomics nomogram was further established based on the clinical factors and radiomics scores. The performance of the models was evaluated using receiver operating characteristic (ROC) curve, DeLong tests, and decision curve analysis. Results Maximum tumor diameter and boundary were selected to build the clinical factors model. Thirteen features were acquired by LASSO algorithm screening as the optimal features for machine learning model construction. The LR model exhibited the highest AUC value (0.819) among the five machine learning models in the validation set. Furthermore, the radiomics nomogram combining the selected clinical variables and radiomics signature predicted the categorization of thymomas at different risks more effectively (the training set, AUC = 0.923; the validation set, AUC = 0.870). Finally, the calibration curve and DCA were utilized to confirm the clinical value of this combined radiomics nomogram. Conclusion We demonstrated the clinical diagnostic value of machine learning models based on CT semantic features and the selected clinical variables, providing a non-invasive, appropriate, and accurate method for preoperative prediction of thymomas risk categorization.
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Affiliation(s)
- Wentao Dong
- Department of Radiology, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
| | - Situ Xiong
- Department of Urology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Pinggui Lei
- Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Xiaolian Wang
- Department of Radiology, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
| | - Hao Liu
- R&D, Yizhun Medical AI, Beijing, China
| | - Yangchun Liu
- Department of Radiology, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
| | - Huachun Zou
- Department of Radiology, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
| | - Bing Fan
- Department of Radiology, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
- *Correspondence: Bing Fan, ; Yingying Qiu,
| | - Yingying Qiu
- Department of Radiology, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
- *Correspondence: Bing Fan, ; Yingying Qiu,
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Liao F, Luo Z, Huang Z, Xu R, Qi W, Shao M, Lei P, Fan B, Vali R. Application of 18F-FDG PET/CT in Langerhans Cell Histiocytosis. Contrast Media & Molecular Imaging 2022; 2022:1-8. [PMID: 36051931 PMCID: PMC9417783 DOI: 10.1155/2022/8385332] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/19/2022] [Revised: 06/28/2022] [Accepted: 07/29/2022] [Indexed: 11/17/2022] [Imported: 08/29/2023]
Abstract
Purpose This study aims to explore the application value of the 18F-FDG PET/CT imaging in diagnosing, staging, and typing Langerhans cell histiocytosis (LCH) via the morphological and metabolic analyses of the 18F-FDG PET/CT images. Methods We retrospectively analyzed the 18F-FDG PET/CT images and clinical data of nineteen patients with LCH. The shape, size, density, distribution, and 18F-FDG uptake of all lesions were documented. In addition, the SUVmax of the lesions, liver, and blood pool was measured prior to calculating the lesion-to-liver and lesion-to-blood pool ratios. Results Among the 19 analyzed patients, the positive rate of the PET/CT image was 94.7% (18/19), with 1 false negative (5.3%, 1/19) case occurring in the cutaneous LCH. Among the 76 lesions, 69 were FDG-avid lesions (69/76, 90.8%). Additionally, we observed no FDG uptake in 7 lesions (7/76, 9.2%). In contrast, 59 lesions (59/76, 77.6%) were abnormal on diagnostic CT scan, but 17 lesions (17/76, 22.4%) were undetected. The 18F-FDG PET/CT image revealed additional 6 lesions in the bone, 4 in the lymph node, 3 in the spleen, and 3 occult lesions, which CT scan did not detect. Additionally, there were 6 cases with single-system LCH. The remaining 13 cases were multisystem LCH. Our 18F-FDG PET/CT image analyses altered the typing of 4 LCH patients. In the case of all lesions, the mean SUVmax of the 18F-FDG-avid lesions was 5.4 ± 5.1 (range, 0.8∼26.2), and the mean lesion-to-liver SUVmax ratio was 3.1 ± 2.52 (range, 0.7∼11.9), and the mean lesion-to-blood pool SUVmax ratio was 4.6 ± 3.4 (range 0.7∼17.5). Conclusion The 18F-FDG PET/CT image plays an essential role in LCH diagnosis, primary staging, and typing. It can accurately evaluate the distribution, range, and metabolic information of LCH, providing a vital imaging basis for the clinical evaluation of disease conditions, selection of treatment schemes, and determining patient prognosis.
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Gui S, Lan M, Wang C, Nie S, Fan B. Application Value of Radiomic Nomogram in the Differential Diagnosis of Prostate Cancer and Hyperplasia. Front Oncol 2022; 12:859625. [PMID: 35494065 PMCID: PMC9047828 DOI: 10.3389/fonc.2022.859625] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 03/17/2022] [Indexed: 12/12/2022] [Imported: 08/29/2023] Open
Abstract
Objective Prostate cancer and hyperplasia require different treatment strategies and have completely different outcomes; thus, preoperative identification of prostate cancer and hyperplasia is very important. The purpose of this study was to evaluate the application value of magnetic resonance imaging (MRI)-derived radiomic nomogram based on T2-weighted images (T2WI) in differentiating prostate cancer and hyperplasia. Materials and Methods One hundred forty-six patients (66 cases of prostate cancer and 80 cases of prostate hyperplasia) who were confirmed by surgical pathology between September 2019 and September 2019 were selected. We manually delineated T2WI of all patients using ITK-SNAP software and radiomic analysis using Analysis Kit (AK) software. A total of 396 tumor texture features were extracted. Subsequently, the effective features were selected using the LASSO algorithm, and the radiomic feature model was constructed. Next, combined with independent clinical risk factors, a multivariate Logistic regression model was used to establish a radiomic nomogram. The receiver operator characteristic (ROC) curve was used to evaluate the prediction performance of the radiomic nomogram. Finally, the clinical application value of the nomogram was evaluated by decision curve analysis. Results The PSA and the selected imaging features were significantly correlated with the differential diagnosis of prostate cancer and hyperplasia. The radiomic model had good discrimination efficiency for prostate cancer and hyperplasia. The training set (AUC = 0.85; 95% CI: 0.77–0.92) and testing set (AUC = 0.84; 95% CI: 0.72–0.96) were effective. The radiomic nomogram, combined with the radiomic characteristics of MRI and independent clinical risk factors, showed better differentiation efficiency in the training set (AUC = 0.91; 95% CI: 0.85–0.97) and testing set (AUC = 0.90; 95% CI: 0.81–0.99). The decision curve showed the clinical application value of the radiomic nomogram. Conclusion The radiomic nomogram of T2-MRI combined with clinical risk factors can easily identify prostate cancer and hyperplasia. It also provides suggestions for further clinical events.
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Affiliation(s)
- Shaogao Gui
- Department of Radiology, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
| | - Min Lan
- Department of Orthopedics, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
| | - Chaoxiong Wang
- Department of Radiology, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
| | - Si Nie
- Department of Radiology, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
- *Correspondence: Si Nie, ; Bing Fan,
| | - Bing Fan
- Department of Radiology, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China
- *Correspondence: Si Nie, ; Bing Fan,
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Zhou H, Xu R, Mei H, Zhang L, Yu Q, Liu R, Fan B. Application of Enhanced T1WI of MRI Radiomics in Glioma Grading. Int J Clin Pract 2022; 2022:3252574. [PMID: 35685548 PMCID: PMC9159237 DOI: 10.1155/2022/3252574] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Revised: 04/19/2022] [Accepted: 04/29/2022] [Indexed: 11/17/2022] [Imported: 08/29/2023] Open
Abstract
OBJECTIVE To explore the application value of the radiomics method based on enhanced T1WI in glioma grading. MATERIALS AND METHODS A retrospective analysis was performed using data of 114 patients with glioma, which was confirmed using surgery and pathological tests, at our hospital between January 2017 and November 2020. The patients were randomly divided into the training and test groups in a ratio of 7 : 3. The Analysis Kit (AK) software was used for radiomic analysis, and a total of 461 tumor texture features were extracted. Spearman correlation analysis and the least absolute shrinkage and selection (LASSO) algorithm were employed to perform feature dimensionality reduction on the training group. A radiomics model was then constructed for glioma grading, and the validation group was used for verification. RESULTS The area under the ROC curve (AUC) of the proposed model was calculated to identify its performance in the training group, which was 0.95 (95% CI = 0.905-0.994), accuracy was 84.8%, sensitivity was 100%, and specificity was 77.8%. The AUC of the validation group was 0.952 (95% CI = 0.871-1.000), accuracy was 93.9%, sensitivity was 90.0%, and specificity was 95.6%. CONCLUSIONS The radiomics model based on enhanced T1WI improved the accuracy of glioma grading and better assisted clinical decision-making.
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Affiliation(s)
- Hongzhang Zhou
- Medical College of Nanchang University, Nanchang 330036, China
- Jiangxi Provincial People's Hospital, Nanchang 330006, China
| | - Rong Xu
- Jiangxi Provincial People's Hospital, Nanchang 330006, China
- The First Affiliated Hospital of Nanchang Medical College, Nanchang 330006, China
| | - Haitao Mei
- Jiangxi Provincial People's Hospital, Nanchang 330006, China
- The First Affiliated Hospital of Nanchang Medical College, Nanchang 330006, China
| | - Ling Zhang
- Medical College of Nanchang University, Nanchang 330036, China
- Jiangxi Provincial People's Hospital, Nanchang 330006, China
| | - Qiyun Yu
- Jiangxi Provincial People's Hospital, Nanchang 330006, China
- The First Affiliated Hospital of Nanchang Medical College, Nanchang 330006, China
| | - Rong Liu
- Jiangxi Provincial People's Hospital, Nanchang 330006, China
- The First Affiliated Hospital of Nanchang Medical College, Nanchang 330006, China
| | - Bing Fan
- Jiangxi Provincial People's Hospital, Nanchang 330006, China
- The First Affiliated Hospital of Nanchang Medical College, Nanchang 330006, China
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Yu Q, Liu J, Lin H, Lei P, Fan B. Application of Radiomics Model of CT Images in the Identification of Ureteral Calculus and Phlebolith. Int J Clin Pract 2022; 2022:5478908. [PMID: 36474549 PMCID: PMC9678460 DOI: 10.1155/2022/5478908] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 10/24/2022] [Accepted: 11/05/2022] [Indexed: 11/16/2022] [Imported: 08/29/2023] Open
Abstract
OBJECTIVE To investigate the clinical application of the three-dimensional (3D) radiomics model of the CT image in the diagnosis and identification of ureteral calculus and phlebolith. METHOD Sixty-one cases of ureteral calculus and 61 cases of phlebolith were retrospectively investigated. The enrolled patients were randomly categorized into the training set (n = 86) and the testing set (n = 36) with a ratio of 7 : 3. The plain CT scan images of all samples were manually segmented by the ITK-SNAP software, followed by radiomics analysis through the Analysis Kit software. A total of 1316 texture features were extracted. Then, the maximum correlation minimum redundancy criterion and the least absolute shrinkage and selection operator algorithm were used for texture feature selection. The feature subset with the most predictability was selected to establish the 3D radiomics model. The performance of the model was evaluated by the receiver operating characteristic (ROC) curve, and the area under the ROC curve (AUC) was also calculated. Additionally, the decision curve was used to evaluate the clinical application of the model. RESULTS The 10 selected radiomics features were significantly related to the identification and diagnosis of ureteral calculus and phlebolith. The radiomics model showed good identification efficiency for ureteral calculus and phlebolith in the training set (AUC = 0.98; 95%CI: 0.96-1.00) and testing set (AUC = 0.98; 95%CI: 0.95-1.00). The decision curve thus demonstrated the clinical application of the radiomics model. CONCLUSIONS The 3D radiomics model based on plain CT scan images indicated good performance in the identification and prediction of ureteral calculus and phlebolith and was expected to provide an effective detection method for clinical diagnosis.
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Affiliation(s)
- Qiuyue Yu
- Department of Radiology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang 330006, China
| | - Jiaqi Liu
- Department of Radiology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang 330006, China
| | - Huashan Lin
- Department of Pharmaceutical Diagnosis, GE Healthcare, Changsha 410005, China
| | - Pinggui Lei
- Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang 550000, China
| | - Bing Fan
- Department of Radiology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang 330006, China
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Chu H, Pang P, He J, Zhang D, Zhang M, Qiu Y, Li X, Lei P, Fan B, Xu R. Value of radiomics model based on enhanced computed tomography in risk grade prediction of gastrointestinal stromal tumors. Sci Rep 2021; 11:12009. [PMID: 34103619 DOI: 10.1038/s41598-021-91508-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2020] [Accepted: 05/24/2021] [Indexed: 01/08/2023] [Imported: 08/29/2023] Open
Abstract
To explore the application of computed tomography (CT)-enhanced radiomics for the risk-grade prediction of gastrointestinal stromal tumors (GIST). GIST patients (n = 292) confirmed by surgery or endoscopic pathology during June 2013–2019 were reviewed and categorized into low-grade (very low to low risk) and high-grade (medium to high risk) groups. The tumor region of interest (ROI) was depicted layer by layer on each patient’s enhanced CT venous phase images using the ITK-SNAP. The texture features were extracted using the Analysis Kit (AK) and then randomly divided into the training (n = 205) and test (n = 87) groups in a ratio of 7:3. After dimension reduction by the least absolute shrinkage and the selection operator algorithm (LASSO), a prediction model was constructed using the logistic regression method. The clinical data of the two groups were statistically analyzed, and the multivariate regression prediction model was constructed by using statistically significant features. The ROC curve was applied to evaluate the prediction performance of the proposed model. A radiomics-prediction model was constructed based on 10 characteristic parameters selected from 396 quantitative feature parameters extracted from the CT images. The proposed radiomics model exhibited effective risk-grade prediction of GIST. For the training group, the area under curve (AUC), sensitivity, specificity, and accuracy rate were 0.793 (95%CI: 0.733–0.854), 83.3%, 64.3%, and 72.7%, respectively; the corresponding values for the test group were 0.791 (95%CI: 0.696–0.886), 84.2%, 69.3%, and 75.9%, respectively. There were significant differences in age (t value: − 3.133, P = 0.008), maximum tumor diameter (Z value: − 12.163, P = 0.000) and tumor morphology (χ2 value:10.409, P = 0.001) between the two groups, which were used to establish a clinical prediction model. The area under the receiver operating characteristic curve of the clinical model was 0.718 (95%CI: 0.659–0.776). The proposed CT-enhanced radiomics model exhibited better accuracy and effective performance than the clinical model, which can be used for the assessment of risk grades of GIST.
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Li S, Liu J, Xiong Y, Pang P, Lei P, Zou H, Zhang M, Fan B, Luo P. A radiomics approach for automated diagnosis of ovarian neoplasm malignancy in computed tomography. Sci Rep 2021; 11:8730. [PMID: 33888749 PMCID: PMC8062553 DOI: 10.1038/s41598-021-87775-x] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 04/05/2021] [Indexed: 12/13/2022] [Imported: 08/29/2023] Open
Abstract
This paper develops a two-dimensional (2D) radiomics approach with computed tomography (CT) to differentiate between benign and malignant ovarian neoplasms. A retrospective study was conducted from July 2017 to June 2019 for 134 patients with surgically-verified benign or malignant ovarian tumors. The patients were randomly divided in a ratio of 7:3 into two sets, namely a training set (of n = 95) and a test set (of n = 39). The ITK-SNAP software was used to delineate the regions of interest (ROI) associated with lesions of the largest diameters in plain CT image slices. Texture features were extracted by the Analysis Kit (AK) software. The training set was used to select the best features according to the maximum-relevance minimum-redundancy (mRMR) criterion, in addition to the algorithm of the least absolute shrinkage and selection operator (LASSO). Then, we employed a radiomics model for classification via multivariate logistic regression. Finally, we evaluated the overall performance of our method using the receiver operating characteristics (ROC), the DeLong test. and tested in an external validation test sample of patients of ovarian neoplasm. We created a radiomics prediction model from 14 selected features. The radiomic signature was found to be highly discriminative according to the area under the ROC curve (AUC) for both the training set (AUC = 0.88), and the test set (AUC = 0.87). The radiomics nomogram also demonstrated good calibration and differentiation for both the training (AUC = 0.95) and test (AUC = 0.96) samples. External validation tests gave a good performance in radiomic signature (AUC = 0.83) and radiomics nomogram (AUC = 0.95). The decision curve explicitly indicated the clinical usefulness of our nomogram method in the sense that it can influence major clinical events such as the ordering or abortion of other tests, treatments or invasive procedures. Our radiomics model based on plain CT images has a high diagnostic efficiency, which is helpful for the identification and prediction of benign and malignant ovarian neoplasms.
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Affiliation(s)
- Shiyun Li
- Department of Gynecology, Jiangxi Provincial People's Hospital Affiliated to Nanchang University, Nanchang, 330006, China
| | - Jiaqi Liu
- Department of Radiology, Jiangxi Provincial People's Hospital Affiliated to Nanchang University, Nanchang, 330006, China
| | - Yuanhuan Xiong
- Department of Gynecology, Jiangxi Provincial People's Hospital Affiliated to Nanchang University, Nanchang, 330006, China
| | | | - Pinggui Lei
- Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang, 550000, China
| | - Huachun Zou
- Department of Radiology, Jiangxi Provincial People's Hospital Affiliated to Nanchang University, Nanchang, 330006, China
| | - Mei Zhang
- Department of Radiology, Jiangxi Provincial People's Hospital Affiliated to Nanchang University, Nanchang, 330006, China
| | - Bing Fan
- Department of Radiology, Jiangxi Provincial People's Hospital Affiliated to Nanchang University, Nanchang, 330006, China.
| | - Puying Luo
- Department of Gynecology, Jiangxi Provincial People's Hospital Affiliated to Nanchang University, Nanchang, 330006, China.
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Wang C, Lei P, Wan Y, Fu P, Fan B, Liu J, Hu F, Xu R. Retroperitoneal dendritic cell sarcoma: A case report. Medicine (Baltimore) 2021; 100:e24459. [PMID: 33655917 PMCID: PMC7939173 DOI: 10.1097/md.0000000000024459] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Accepted: 01/06/2021] [Indexed: 01/04/2023] [Imported: 08/29/2023] Open
Abstract
RATIOANLE Interdigitating dendritic cell sarcoma (IDCS) is a rare sarcoma that originates from interdigitating dendritic cells in lymphoid tissue, the imaging characteristics of which are poorly defined. Pathological examination can identify the tumor, but reports on the imaging characteristics of IDCS are limited. PATIENT CONCERNS Here, we report a case of IDCS in a 48-year-old female involving the retroperitoneal area. The patient had a lumbar mass on her right lower back for 4 years, and which started increasing in size 1 year before. DIAGNOSES An irregular soft tissue mass (10.1cm × 8.5 cm in size) in the right lower back of retroperitoneum was detected by CT examination with unclear borders, uneven density, and necrosis. The solid components of the mass were significantly enhanced on postcontrast imaging. The soft tissue was irregular and uneven. Cystic solid masses were observed on MRI examination in the right retroperitoneum, lateral abdominal wall, waist, and back. Necrosis, hemorrhage, and cystic transformation were observed inside the lesion. The cyst wall, separation, and wall nodules were significantly enhanced on the postcontrast image. No distant metastasis was observed. Postoperative pathology confirmed the diagnosis of IDCS. INTERVENTIONS The patient underwent surgical resection. The resected margin was positive, and the patient received adjuvant radiotherapy 2 months after the surgery. OUTCOMES Twelve months after radiotherapy, the patient's chest CT showed multiple metastases in both lungs. The patient was started on combination chemotherapy of doxorubicin and ifosfamide, and the follow-up is still ongoing. LESSONS Imaging provides a unique advantage to determine the extent of the IDCS, the invasion of adjacent tissues, and the presence or absence of distant metastases.
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Affiliation(s)
| | - Pinggui Lei
- Department of Radiology, the Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | | | - Ping Fu
- Department of General Surgery, Jiangxi Provincial People's Hospital Affiliated to Nanchang University, Nanchang
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11
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Lei P, Fan B, Sun Y. COVID-19 Carrier or Pneumonia: Positive Real-Time Reverse-Transcriptase Polymerase Chain Reaction but Negative or Positive Chest CT Results. Korean J Radiol 2020; 21:925-928. [PMID: 32524793 PMCID: PMC7289689 DOI: 10.3348/kjr.2020.0360] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Revised: 03/26/2020] [Accepted: 04/03/2020] [Indexed: 01/08/2023] [Imported: 08/29/2023] Open
Affiliation(s)
- Pinggui Lei
- Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Bing Fan
- Department of Radiology, Jiangxi Provincial People's Hospital, Nanchang, China.
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12
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Li M, Lei P, Zeng B, Li Z, Yu P, Fan B, Wang C, Li Z, Zhou J, Hu S, Liu H. Coronavirus Disease (COVID-19): Spectrum of CT Findings and Temporal Progression of the Disease. Acad Radiol 2020; 27:603-608. [PMID: 32204987 PMCID: PMC7156150 DOI: 10.1016/j.acra.2020.03.003] [Citation(s) in RCA: 164] [Impact Index Per Article: 41.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Revised: 03/05/2020] [Accepted: 03/05/2020] [Indexed: 02/07/2023] [Imported: 08/29/2023]
Abstract
Coronavirus disease is an emerging infection caused by a novel coronavirus that is moving rapidly. High resolution computed tomography (CT) allows objective evaluation of the lung lesions, thus enabling us to better understand the pathogenesis of the disease. With serial CT examinations, the occurrence, development, and prognosis of the disease can be better understood. The imaging can be sorted into four phases: early phase, progressive phase, severe phase, and dissipative phase. The CT appearance of each phase and temporal progression of the imaging findings are demonstrated.
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13
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Long C, Xu H, Shen Q, Zhang X, Fan B, Wang C, Zeng B, Li Z, Li X, Li H. Diagnosis of the Coronavirus disease (COVID-19): rRT-PCR or CT? Eur J Radiol 2020; 126:108961. [PMID: 32229322 PMCID: PMC7102545 DOI: 10.1016/j.ejrad.2020.108961] [Citation(s) in RCA: 538] [Impact Index Per Article: 134.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Revised: 03/11/2020] [Accepted: 03/11/2020] [Indexed: 12/16/2022] [Imported: 08/29/2023]
Abstract
The COVID-19 outbreak highlights the need for early diagnosis, isolation and treatment The sensitivity of the CT was 97.2%, while the sensitivity of initial rRT-PCR was 83.3%. Patients with typical CT findings but negative rRT-PCR results should be isolated.
Purpose To evaluate the diagnostic value of computed tomography (CT) and real-time reverse-transcriptase-polymerase chain reaction (rRT-PCR) for COVID-19 pneumonia. Methods This retrospective study included all patients with COVID-19 pneumonia suspicion, who were examined by both CT and rRT-PCR at initial presentation. The sensitivities of both tests were then compared. For patients with a final confirmed diagnosis, clinical and laboratory data, in addition to CT imaging findings were evaluated. Results A total of 36 patients were finally diagnosed with COVID-19 pneumonia. Thirty-five patients had abnormal CT findings at presentation, whereas one patient had a normal CT. Using rRT-PCR, 30 patients were tested positive, with 6 cases initially missed. Amongst these 6 patients, 3 became positive in the second rRT-PCR assay(after 2 days, 2 days and 3 days respectively), and the other 3 became positive only in the third round of rRT-PCR tests(after 5 days, 6 days and 8 days respectively). At presentation, CT sensitivity was therefore 97.2%, whereas the sensitivity of initial rRT-PCR was only 83.3%. Conclusion rRT-PCR may produce initial false negative results. We suggest that patients with typical CT findings but negative rRT-PCR results should be isolated, and rRT-PCR should be repeated to avoid misdiagnosis.
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Affiliation(s)
- Chunqin Long
- Radiology Department, Yichang Yiling Hospital, Yichang 443100, China
| | - Huaxiang Xu
- Medical Cosmetology Department, Jiangxi Provincial People's Hospital, Nanchang 330006, China
| | - Qinglin Shen
- Institute of Clinical Medicine, Jiangxi Provincial People's Hospital, Nanchang 330006, China
| | - Xianghai Zhang
- Radiology Department, Yichang Yiling Hospital, Yichang 443100, China
| | - Bing Fan
- Radiology Department, Jiangxi Provincial People's Hospital, Nanchang 330006, China.
| | - Chuanhong Wang
- Radiology Department, Jiangxi Provincial People's Hospital, Nanchang 330006, China
| | - Bingliang Zeng
- Radiology Department, Jiangxi Provincial People's Hospital, Nanchang 330006, China
| | - Zicong Li
- Radiology Department, Jiangxi Provincial People's Hospital, Nanchang 330006, China
| | - Xiaofen Li
- Radiology Department, Jiangxi Provincial People's Hospital, Nanchang 330006, China
| | - Honglu Li
- Radiology Department, Jiangxi Provincial People's Hospital, Nanchang 330006, China
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14
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Lei P, Fan B, Yuan Y. The evolution of CT characteristics in the patients with COVID-19 pneumonia. J Infect 2020; 80:e29. [PMID: 32201155 PMCID: PMC7195075 DOI: 10.1016/j.jinf.2020.03.014] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Accepted: 03/14/2020] [Indexed: 11/17/2022] [Imported: 08/29/2023]
Affiliation(s)
- Pinggui Lei
- Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang 550004, China
| | - Bing Fan
- Department of Radiology, Jiangxi Provincial People's Hospital, Aiguo Road No. 92, Donghu, Nanchang, Jiangxi 330006, China.
| | - Yingnan Yuan
- Department of Interventional Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang 550004, China
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15
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Wei J, Xu H, Xiong J, Shen Q, Fan B, Ye C, Dong W, Hu F. 2019 Novel Coronavirus (COVID-19) Pneumonia: Serial Computed Tomography Findings. Korean J Radiol 2020; 21:501-504. [PMID: 32100486 PMCID: PMC7082663 DOI: 10.3348/kjr.2020.0112] [Citation(s) in RCA: 70] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Accepted: 02/17/2020] [Indexed: 02/06/2023] [Imported: 08/29/2023] Open
Abstract
From December 2019, Coronavirus disease 2019 (COVID-19) pneumonia (formerly known as the 2019 novel Coronavirus [2019-nCoV]) broke out in Wuhan, China. In this study, we present serial CT findings in a 40-year-old female patient with COVID-19 pneumonia who presented with the symptoms of fever, chest tightness, and fatigue. She was diagnosed with COVID-19 infection confirmed by real-time reverse-transcriptase-polymerase chain reaction. CT showed rapidly progressing peripheral consolidations and ground-glass opacities in both lungs. After treatment, the lesions were shown to be almost absorbed leaving the fibrous lesions.
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Affiliation(s)
- Jiangping Wei
- Department of Radiology, Jiangxi Provincial People's Hospital, Nanchang, China
| | - Huaxiang Xu
- Department of Medical Cosmetology, Jiangxi Provincial People's Hospital, Nanchang, China
| | - Jingliang Xiong
- Department of Radiology, Jiangxi Chest Hospital, Nanchang, China
| | - Qinglin Shen
- Institute of Clinical Medicine, Jiangxi Provincial People's Hospital, Nanchang, China
| | - Bing Fan
- Department of Radiology, Jiangxi Provincial People's Hospital, Nanchang, China.
| | - Chenglong Ye
- Department of Radiology, Jiangxi Provincial People's Hospital, Nanchang, China
| | - Wentao Dong
- Department of Radiology, Jiangxi Provincial People's Hospital, Nanchang, China
| | - Fangfang Hu
- Department of Radiology, Jiangxi Provincial People's Hospital, Nanchang, China
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16
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Lin X, Gong Z, Xiao Z, Xiong J, Fan B, Liu J. Novel Coronavirus Pneumonia Outbreak in 2019: Computed Tomographic Findings in Two Cases. Korean J Radiol 2020; 21:365-368. [PMID: 32056397 PMCID: PMC7039714 DOI: 10.3348/kjr.2020.0078] [Citation(s) in RCA: 87] [Impact Index Per Article: 21.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2020] [Accepted: 02/04/2020] [Indexed: 02/06/2023] [Imported: 08/29/2023] Open
Abstract
Since the 2019 novel coronavirus (2019-nCoV or officially named by the World Health Organization as COVID-19) outbreak in Wuhan, Hubei Province, China in 2019, there have been a few reports of its imaging findings. Here, we report two confirmed cases of 2019-nCoV pneumonia with chest computed tomography findings of multiple regions of patchy consolidation and ground-glass opacities in both lungs. These findings were characteristically located along the bronchial bundle or subpleural lungs.
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Affiliation(s)
- Xiaoqi Lin
- Department of Radiology, Jiangxi Provincial People's Hospital, Jiangxi, China
| | - Zhenyu Gong
- Department of Prevention and Health Care, Jiangxi Provincial People's Hospital, Jiangxi, China
| | - Zuke Xiao
- Department of Respiratory, Jiangxi Provincial People's Hospital, Jiangxi, China
| | - Jingliang Xiong
- Department of Radiology, Jiangxi Chest Hospital, Jiangxi, China
| | - Bing Fan
- Department of Radiology, Jiangxi Provincial People's Hospital, Jiangxi, China.
| | - Jiaqi Liu
- Department of Radiology, Jiangxi Provincial People's Hospital, Jiangxi, China
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Zhang H, Liu X, Yu P, Cheng M, Wang W, Sun Y, Zeng B, Fan B. Dynamic CT assessment of disease change and prognosis of patients with moderate COVID-19 pneumonia. J Xray Sci Technol 2020; 28:851-861. [PMID: 32741802 PMCID: PMC7592657 DOI: 10.3233/xst-200711] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/17/2023] [Imported: 08/29/2023]
Abstract
OBJECTIVES To assess prognosis or dynamic change from initial diagnosis until recovery of the patients with moderate coronavirus disease (COVID-19) pneumonia using chest CT images. MATERIALS AND METHODS In this retrospective study, 33 patients (18 men, 15 women; median age, 49.0 years) with confirmed with moderate COVID-19 pneumonia in a multicenter hospital were included. The patients underwent at least four chest non-contrast-enhanced computed tomography (CT) scans at approximately 5-day intervals. We analyzed the clinical and CT characteristics of the patients. Moreover, the total CT score and the sum of lung involvement were determined for every CT scan. RESULTS The most widespread presenting symptoms were fever (32/33, 97.0%) and cough (17/33, 51.5%), which were often accompanied by decreased lymphocyte count (15/33, 45.5%) and increased C-reactive protein levels (18/33, 54.6%). Bilateral, multifocal ground glass opacities (32/33, 97.0%), consolidation (25/33, 75.8%), vascular thickening (23/33, 69.7%), and bronchial wall thickening (21/33, 63.6%) with peripheral distribution were the most frequent CT findings during moderate COVID-19 pneumonia. In patients recovering from moderate COVID-19 pneumonia, four stages (stages 1-4) of evolution were identified on chest CT with average CT scores of 3.4±2.3, 6.0±4.4, 5.6±3.8, and 4.9±3.2, respectively, from the onset of symptoms. For most patients, the peak of average total CT score increased for approximately 8 days after the onset of symptoms, after which it decreased gradually. The mean CT score of all patients was 4.7 at the time of discharge. CONCLUSION The moderate COVID-19 pneumonia CT score increased rapidly in a short period of time initially, followed by a slow decline over a relatively long time. The peak of the course occurred in stage 2. Complete recovery of patients with moderate COVID-19 pneumonia with high mean CT score at the time of discharge requires longer time.
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Affiliation(s)
- Hua Zhang
- Department of Radiology, the Third Affiliated Hospital of Nanchang University (also known as the First Hospital of Nanchang), Jiangxi, China
| | - Xiaohong Liu
- Department of Radiology, JiangXi PingXiang people’s hospital, Jiangxi, China
| | - Peng Yu
- Department of Radiology, JiangXi JinXian people’s hospital, Jiangxi, China
| | - Mingyuan Cheng
- Department of Radiology, the Third Affiliated Hospital of Nanchang University (also known as the First Hospital of Nanchang), Jiangxi, China
| | - Weiting Wang
- Department of Radiology, Jiangxi provincial chest hospital, Jiangxi, China
| | | | - Bingliang Zeng
- Jiangxi Provincial People’s Hospital Affiliated to Nanchang University, Nanchang, Jiangxi, China
- Corresponding author: Bingliang Zeng; Department of Radiology, Jiangxi Provincial People’s Hospital Affiliated to Nanchang University, Nanchang 330006, China. Tel.: +86 18970038858; E-mail: ; Bing Fan; Department of Radiology, Jiangxi Provincial People’s Hospital Affiliated to Nanchang University, Nanchang 330006, China. Tel.: +86 19917922166; E-mail:
| | - Bing Fan
- Jiangxi Provincial People’s Hospital Affiliated to Nanchang University, Nanchang, Jiangxi, China
- Corresponding author: Bingliang Zeng; Department of Radiology, Jiangxi Provincial People’s Hospital Affiliated to Nanchang University, Nanchang 330006, China. Tel.: +86 18970038858; E-mail: ; Bing Fan; Department of Radiology, Jiangxi Provincial People’s Hospital Affiliated to Nanchang University, Nanchang 330006, China. Tel.: +86 19917922166; E-mail:
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Li Z, Zeng B, Lei P, Liu J, Fan B, Shen Q, Pang P, Xu R. Differentiating pneumonia with and without COVID-19 using chest CT images: from qualitative to quantitative. J Xray Sci Technol 2020; 28:583-589. [PMID: 32568167 PMCID: PMC7505000 DOI: 10.3233/xst-200689] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Revised: 05/17/2020] [Accepted: 05/23/2020] [Indexed: 05/17/2023] [Imported: 08/29/2023]
Abstract
BACKGROUND Pneumonia caused by COVID-19 shares overlapping imaging manifestations with other types of pneumonia. How to objectively and quantitatively differentiate pneumonia patients with and without COVID-19 virus remains clinical challenge. OBJECTIVE To formulate standardized scoring criteria and an objective quantization standard to guide decision making in detection and diagnosis of COVID-19 virus induced pneumonia in clinical practice. METHODS A retrospective dataset includes computed tomography (CT) images acquired from 43 pneumonia patients with COVID-19 virus detected by reverse transcription-polymerase chain reaction (RT-PCR) tests and 49 pneumonia patients without COVID-19 virus. All patients were treated during the same time period in two hospitals. Key indicators of differential diagnosis were identified in relevant literature and the scores were quantified namely, patients with more than 8 points were identified as high risk, those with 6-8 points as moderate risk, and those with fewer than 6 points as low risk for COVID-19 virus. In the study, 3 radiologists determined the scores for all patients. Diagnostic sensitivity and specificity were subsequently calculated. RESULTS A total of 61 patients were determined as high risk, among which 42 were COVID-19 positive by RT-PCR tests. Next, 9 were identified as moderate risk, one of whom was COVID-19 positive. Last, 22 were classified into the low-risk group, all of them are COVID-19 negative. Based on these results, the sensitivity of detection COVID-19 positive cases between the high-risk group and the non-high-risk group was 0.98 with 95% confidence interval [0.88, 1.00], and the specificity was 0.61 [0.46, 0.75]. The detection sensitivity between the moderate-/high-risk group and the low-risk group was 1.00 [0.92, 1.00], and the specificity was 0.45 [0.31, 0.60]. CONCLUSION The proposed quantitative scoring criteria showed high sensitivity and moderate specificity in detecting COVID-19 using CT images, which indicates that these criteria may be beneficial for screening in real-world practice and helpful for long-term disease control.
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Affiliation(s)
- Zicong Li
- Department of Radiology, Jiangxi Provincial People’s Hospital, Nanchang, China
| | - Bingliang Zeng
- Department of Radiology, Jiangxi Provincial People’s Hospital, Nanchang, China
| | - Pinggui Lei
- Department of Radiology, the Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Jiaqi Liu
- Department of Radiology, Jiangxi Provincial People’s Hospital, Nanchang, China
| | - Bing Fan
- Department of Radiology, Jiangxi Provincial People’s Hospital, Nanchang, China
- Corresponding authors: Bing Fan and Rongchun Xu, Department of Radiology, Jiangxi Provincial People’s Hospital, Nanchang 330006, China. Tel.: +86 19917922166 (Bing Fan), +86 13320116782 (Rongchun Xu); E-mail: (Bing Fan), E-mail: (Rongchun Xu)
| | - Qinglin Shen
- Institute of Clinical Medicine, Jiangxi Provincial People’s Hospital, Nanchang, China
| | | | - Rongchun Xu
- Department of Radiology, Jiangxi Provincial People’s Hospital, Nanchang, China
- Corresponding authors: Bing Fan and Rongchun Xu, Department of Radiology, Jiangxi Provincial People’s Hospital, Nanchang 330006, China. Tel.: +86 19917922166 (Bing Fan), +86 13320116782 (Rongchun Xu); E-mail: (Bing Fan), E-mail: (Rongchun Xu)
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Wei J, Yang H, Lei P, Fan B, Qiu Y, Zeng B, Yu P, Lv J, Jian Y, Wan C. Analysis of thin-section CT in patients with coronavirus disease (COVID-19) after hospital discharge. J Xray Sci Technol 2020; 28:383-389. [PMID: 32474479 PMCID: PMC7369060 DOI: 10.3233/xst-200685] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023] [Imported: 08/29/2023]
Abstract
PURPOSE To analyze clinical and thin-section computed tomographic (CT) data from the patients with coronavirus disease (COVID-19) to predict the development of pulmonary fibrosis after hospital discharge. MATERIALS AND METHODS Fifty-nine patients (31 males and 28 females ranging from 25 to 70 years old) with confirmed COVID-19 infection performed follow-up thin-section thorax CT. After 31.5±7.9 days (range, 24 to 39 days) of hospital admission, the results of CT were analyzed for parenchymal abnormality (ground-glass opacification, interstitial thickening, and consolidation) and evidence of fibrosis (parenchymal band, traction bronchiectasis, and irregular interfaces). Patients were analyzed based on the evidence of fibrosis and divided into two groups namely, groups A and B (with and without CT evidence of fibrosis), respectively. Patient demographics, length of stay (LOS), rate of intensive care unit (ICU) admission, peak C-reactive protein level, and CT score were compared between the two groups. RESULTS Among the 59 patients, 89.8% (53/59) had a typical transition from early phase to advanced phase and advanced phase to dissipating phase. Also, 39% (23/59) patients developed fibrosis (group A), whereas 61% (36/59) patients did not show definite fibrosis (group B). Patients in group A were older (mean age, 45.4±16.9 vs. 33.8±10.2 years) (P = 0.001), with longer LOS (19.1±5.2 vs. 15.0±2.5 days) (P = 0.001), higher rate of ICU admission (21.7% (5/23) vs. 5.6% (2/36)) (P = 0.061), higher peak C-reactive protein level (30.7±26.4 vs. 18.1±17.9 mg/L) (P = 0.041), and higher maximal CT score (5.2±4.3 vs. 4.0±2.2) (P = 0.06) than those in group B. CONCLUSIONS Pulmonary fibrosis may develop early in patients with COVID-19 after hospital discharge. Older patients with severe illness during treatment were more prone to develop fibrosis according to thin-section CT results.
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Affiliation(s)
- Jiangping Wei
- Department of Radiology, Jiangxi Provincial People’s Hospital, Nanchang, China
| | - Hong Yang
- Department of Radiology, Nanxishan Hospital of Guangxi Zhuang Autonomous Region, Guilin, China
| | - Pinggui Lei
- Department of Radiology, the Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Bing Fan
- Department of Radiology, Jiangxi Provincial People’s Hospital, Nanchang, China
- Corresponding author: Bing Fan, Department of Radiology, Jiangxi Provincial People’s Hospital, Nanchang 330006, China. Tel: +86 19917922166; E-mail:
| | - Yingying Qiu
- Department of Radiology, Jiangxi Provincial People’s Hospital, Nanchang, China
| | - Bingliang Zeng
- Department of Radiology, Jiangxi Provincial People’s Hospital, Nanchang, China
| | - Peng Yu
- Department of Radiology, Jinxian County People’s Hospital, Nanchang, China
| | - Jian Lv
- Department of Radiology, Nanxishan Hospital of Guangxi Zhuang Autonomous Region, Guilin, China
| | - Yinchao Jian
- Department of Radiology, Jiangxi Provincial People’s Hospital, Nanchang, China
| | - Chengfeng Wan
- Department of Radiology, Jiangxi Provincial People’s Hospital, Nanchang, China
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