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Kang SR, Wang K. Radiomic nomogram based on lumbar spine magnetic resonance images to diagnose osteoporosis. Acta Radiol 2024; 65:950-958. [PMID: 38651258 DOI: 10.1177/02841851241242052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/25/2024]
Abstract
BACKGROUND We aimed to establish a novel model using a radiomics analysis of magnetic resonance (MR) images for predicting osteoporosis. PURPOSE To investigate the effectiveness of a radiomics approach utilizing magnetic resonance imaging (MRI) of the lumbar spine in identifying osteoporosis. MATERIAL AND METHODS In this retrospective study, a total of 291 patients who underwent MRI were analyzed. Radiomics features were extracted from the MRI scans of all 1455 lumbar vertebrae, and build the radiomics model based on T2-weighted (T2W), T1-weighted (T1W), and T2W + T1W imaging. The performance of the combined model was assessed using metrics such as the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. The AUCs of these models were compared using the DeLong test. Their clinical usefulness was assessed using a decision curve analysis. RESULTS T2W, T1W, and T1W + T2W imaging retained 27, 27, and 17 non-zero coefficients, respectively. The AUCS about radiomics scores based on T2W, T1W, and T1W + T2W imaging were 0.894, 0.934, and 0.945, respectively, which all performed better than the clinical model significantly. The rad-signatures based on T1W + T2W imaging, which exhibited a stronger predictive power, were included in the creation of the nomogram for osteoporosis diagnosis, and the AUC was 0.965 (95% confidence interval (CI)=0.944-0.986) in the training cohort and 0.917 (95% CI=0.738-1.000) in the test cohort. The calibration curve indicated that the radiomics nomogram had considerable clinical usefulness in prediction, observation, and decision curve analysis. CONCLUSION A reliable and powerful tool for identifying osteoporosis can be provided by the nomogram that combines the T1W and T2W imaging radiomics score with clinical risk factors.
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Affiliation(s)
- Si-Ru Kang
- Department of Radiology, Xiaogan Hospital Affiliated to Wuhan University of Science and Technology, Xiaogan, PR China
| | - Kai Wang
- Department of Orthopedics, Xiaogan Hospital Affiliated to Wuhan University of Science and Technology, Xiaogan, PR China
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Liu J, Wang H, Shan X, Zhang L, Cui S, Shi Z, Liu Y, Zhang Y, Wang L. Hybrid transformer convolutional neural network-based radiomics models for osteoporosis screening in routine CT. BMC Med Imaging 2024; 24:62. [PMID: 38486185 PMCID: PMC10938662 DOI: 10.1186/s12880-024-01240-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 03/06/2024] [Indexed: 03/18/2024] Open
Abstract
OBJECTIVE Early diagnosis of osteoporosis is crucial to prevent osteoporotic vertebral fracture and complications of spine surgery. We aimed to conduct a hybrid transformer convolutional neural network (HTCNN)-based radiomics model for osteoporosis screening in routine CT. METHODS To investigate the HTCNN algorithm for vertebrae and trabecular segmentation, 92 training subjects and 45 test subjects were employed. Furthermore, we included 283 vertebral bodies and randomly divided them into the training cohort (n = 204) and test cohort (n = 79) for radiomics analysis. Area receiver operating characteristic curves (AUCs) and decision curve analysis (DCA) were applied to compare the performance and clinical value between radiomics models and Hounsfield Unit (HU) values to detect dual-energy X-ray absorptiometry (DXA) based osteoporosis. RESULTS HTCNN algorithm revealed high precision for the segmentation of the vertebral body and trabecular compartment. In test sets, the mean dice scores reach 0.968 and 0.961. 12 features from the trabecular compartment and 15 features from the entire vertebral body were used to calculate the radiomics score (rad score). Compared with HU values and trabecular rad-score, the vertebrae rad-score suggested the best efficacy for osteoporosis and non-osteoporosis discrimination (training group: AUC = 0.95, 95%CI 0.91-0.99; test group: AUC = 0.97, 95%CI 0.93-1.00) and the differences were significant in test group according to the DeLong test (p < 0.05). CONCLUSIONS This retrospective study demonstrated the superiority of the HTCNN-based vertebrae radiomics model for osteoporosis discrimination in routine CT.
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Affiliation(s)
- Jiachen Liu
- Department of Orthopedics, Shengjing Hospital of China Medical University, 110004, Shenyang, People's Republic of China
| | - Huan Wang
- Department of Orthopedics, Shengjing Hospital of China Medical University, 110004, Shenyang, People's Republic of China
| | - Xiuqi Shan
- Department of Orthopedics, Shengjing Hospital of China Medical University, 110004, Shenyang, People's Republic of China
| | - Lei Zhang
- Department of Orthopedics, Shengjing Hospital of China Medical University, 110004, Shenyang, People's Republic of China
| | - Shaoqian Cui
- Department of Orthopedics, Shengjing Hospital of China Medical University, 110004, Shenyang, People's Republic of China
| | - Zelin Shi
- Shenyang Institute of Automation, Chinese Academy of Sciences, 110016, Shenyang, People's Republic of China
| | - Yunpeng Liu
- Shenyang Institute of Automation, Chinese Academy of Sciences, 110016, Shenyang, People's Republic of China
| | - Yingdi Zhang
- Shenyang Institute of Automation, Chinese Academy of Sciences, 110016, Shenyang, People's Republic of China
| | - Lanbo Wang
- Department of Radiology, Shengjing Hospital of China Medical University, 110004, Shenyang, People's Republic of China.
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Feng Q, Xu S, Gong X, Wang T, He X, Liao D, Han F. An MRI-Based Radiomics Nomogram for Differentiation of Benign and Malignant Vertebral Compression Fracture. Acad Radiol 2024; 31:605-616. [PMID: 37586940 DOI: 10.1016/j.acra.2023.07.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 07/11/2023] [Accepted: 07/11/2023] [Indexed: 08/18/2023]
Abstract
RATIONALE AND OBJECTIVES This study aimed to develop and validate a magnetic resonance imaging (MRI)-based radiomics nomogram combining radiomics signatures and clinical factors to differentiate between benign and malignant vertebral compression fractures (VCFs). MATERIALS AND METHODS A total of 189 patients with benign VCFs (n = 112) or malignant VCFs (n = 77) were divided into training (n = 133) and validation (n = 56) cohorts. Radiomics features were extracted from MRI T1-weighted images and short-TI inversion recovery images to develop the radiomics signature, and the Rad score was constructed using least absolute shrinkage and selection operator regression. Demographic and MRI morphological characteristics were assessed to build a clinical factor model using multivariate logistic regression analysis. A radiomics nomogram was constructed based on the Rad score and independent clinical factors. Finally, the diagnostic performance of the radiomics nomogram, clinical model, and radiomics signature was validated using receiver operating characteristic and decision curve analysis (DCA). RESULTS Six features were used to build a combined radiomics model (combined-RS). Pedicle or posterior element involvement, paraspinal mass, and fluid sign were identified as the most important morphological factors for building the clinical factor model. The radiomics signature was superior to the clinical model in terms of the area under the curve (AUC), accuracy, and specificity. The radiomics nomogram integrating the combined-RS, pedicle or posterior element involvement, paraspinal mass, and fluid sign achieved favorable predictive efficacy, generating AUCs of 0.92 and 0.90 in the training and validation cohorts, respectively. The DCA indicated good clinical usefulness of the radiomics nomogram. CONCLUSION The MRI-based radiomics nomogram, combining the radiomics signature and clinical factors, showed favorable predictive efficacy for differentiating benign from malignant VCFs.
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Affiliation(s)
- Qianqian Feng
- Department of Radiology, Qionglai Medical Center Hospital, No. 172 Xinglin Road, Wenjun Street, Qionglai, Sichuan, 611530, People's Republic of China (Q.F., T.W.)
| | - Shan Xu
- Department of Radiology, Luzhou Traditional Chinese Medicine Hospital, No. 11 Jiangyang South Road, Luzhou, Sichuan, 646000, People's Republic of China (S.X.)
| | - Xiaoli Gong
- Department of Radiology, Jiangan County Traditional Chinese Medicine Hospital, No. 800 West Section of Raocheng Road, Yibin, Sichuan, 644200, People's Republic of China (X.G.)
| | - Teng Wang
- Department of Radiology, Qionglai Medical Center Hospital, No. 172 Xinglin Road, Wenjun Street, Qionglai, Sichuan, 611530, People's Republic of China (Q.F., T.W.)
| | - Xiaopeng He
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, No.25 Taiping Street, Luzhou, Sichuan, 646000, People's Republic of China (X.H., D.L., F.H.).
| | - Dawei Liao
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, No.25 Taiping Street, Luzhou, Sichuan, 646000, People's Republic of China (X.H., D.L., F.H.)
| | - Fugang Han
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, No.25 Taiping Street, Luzhou, Sichuan, 646000, People's Republic of China (X.H., D.L., F.H.)
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Zhang H, Deng Y, Xiaojie M, Zou Q, Liu H, Tang N, Luo Y, Xiang X. CT radiomics for predicting the prognosis of patients with stage II rectal cancer during the three-year period after surgery, chemotherapy and radiotherapy. Heliyon 2024; 10:e23923. [PMID: 38223741 PMCID: PMC10787243 DOI: 10.1016/j.heliyon.2023.e23923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 11/29/2023] [Accepted: 12/15/2023] [Indexed: 01/16/2024] Open
Abstract
Objective Pre-treatment enhanced CT image data were used to train and build models to predict the efficacy of non-small cell lung cancer after conventional radiotherapy and chemotherapy using two classification algorithms, Logistic Regression (LR) and Gaussian Naive Baye (GNB). Methods In this study, we used pre-treatment enhanced CT image data for region of interest (ROI) sketching and feature extraction. We utilized the least absolute shrinkage and selection operator (LASSO) mutual confidence method for feature screening. We pre-screened logistic regression (LR) and Gaussian naive Bayes (GNB) classification algorithms and trained and modeled the screened features. We plotted 5-fold and 10-fold cross-validated receiver operating characteristic (ROC) curves to calculate the area under the curve (AUC). We performed DeLong's test for validation and plotted calibration curves and decision curves to assess model performance. Results A total of 102 patients were included in this study, and after a comparative analysis of the two models, LR had only slightly lower specificity than GNB, and higher sensitivity, accuracy, AUC value, precision, and F1 value than GNB (training set accuracy: 0.787, AUC value: 0.851; test set accuracy: 0.772, AUC value: 0.849), and the LR model has better performance in both the decision curve and the calibration curve. Conclusion CT can be used for efficacy prediction after radiotherapy and chemotherapy in NSCLC patients. LR is more suitable for predicting whether NSCLC prognosis is in remission without considering the computing speed.
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Affiliation(s)
- Hanjing Zhang
- Department of Oncology, Affiliated Hospital of Chuanbei Medical College, Nanchong, Sichuan Province, 637000, China
| | - Yu Deng
- The Affiliated Cancer Hospital of Guizhou Medical University, GuiYang, Guizhou Province, 550000, China
| | - M.A. Xiaojie
- Department of Oncology, Affiliated Hospital of Chuanbei Medical College, Nanchong, Sichuan Province, 637000, China
| | - Qian Zou
- Department of Oncology, Affiliated Hospital of Chuanbei Medical College, Nanchong, Sichuan Province, 637000, China
| | - Huanhui Liu
- Department of Oncology, Affiliated Hospital of Chuanbei Medical College, Nanchong, Sichuan Province, 637000, China
| | - Ni Tang
- Department of Oncology, Affiliated Hospital of Chuanbei Medical College, Nanchong, Sichuan Province, 637000, China
| | - Yuanyuan Luo
- Department of Oncology, Affiliated Hospital of Chuanbei Medical College, Nanchong, Sichuan Province, 637000, China
| | - Xuejing Xiang
- Department of Oncology, Affiliated Hospital of Chuanbei Medical College, Nanchong, Sichuan Province, 637000, China
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Zheng W, Mu R, Liu F, Qin X, Li X, Yang P, Li X, Liang Y, Zhu X. Textural features of the frontal white matter could be used to discriminate amnestic mild cognitive impairment patients from the normal population. Brain Behav 2023; 13:e3222. [PMID: 37587901 PMCID: PMC10636424 DOI: 10.1002/brb3.3222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 08/03/2023] [Accepted: 08/04/2023] [Indexed: 08/18/2023] Open
Abstract
OBJECTIVE We aim to develop a radiomics model based on 3-dimensional (3D)-T1WI images to discriminate amnestic mild cognitive impairment (aMCI) patients from the normal population by measuring changes in frontal white matter. METHODS In this study, 126 patients with aMCI and 174 normal controls (NC) were recruited from the local community. All subjects underwent routine magnetic resonance imaging examination (including 3D-T1WI ). Participants were randomly divided into a training set (n = 242, aMCI:102, NC:140) and a testing set (n = 58, aMCI:24, NC:34). Texture features of the frontal lobe were extracted from 3D-T1WI images. The least absolute shrinkage and selection operator (LASSO) was used to reduce feature dimensions and develop a radiomics signature model. Diagnostic performance was assessed in the training and testing sets using the receiver operating characteristic (ROC) curve analysis. The area under the ROC curve (AUC), sensitivity, and specificity were also calculated. The efficacy of the radiomics model in discriminating aMCI patients from the normal population was assessed by decision curve analysis (DCA). RESULTS A total of 108 frontal lobe texture features were extracted from 3D-T1WI images. LASSO selected 58 radiomic features for the final model, including log-sigma (n = 18), original (n = 8), and wavelet (n = 32) features. The performance of radiomic features extracted from 3D T1 imaging for distinguishing aMCI patients from controls was: in the training set, AUC was 1.00, and the accuracy, sensitivity, and specificity were 100%, 98%, and 100%, respectively. In the testing set, AUC was 0.82 (95% CI:0.69-0.95), and the accuracy, sensitivity, and specificity were 69%, 92%, and 55%, respectively. The DCA demonstrated that the model had favorable clinical predictive value. CONCLUSIONS Textural features of white matter in the frontal lobe showed potential for distinguishing aMCI from the normal population, which could be a surrogate protocol to aid aMCI screening in clinical setting.
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Affiliation(s)
- Wei Zheng
- Department of Clinical MedicineGuilin Medical universityGuilinChina
- Department of Medical ImagingNanxishan Hospital of Guangxi ZhuangAutonomous RegionGuilinChina
| | - Ronghua Mu
- Department of Medical ImagingNanxishan Hospital of Guangxi ZhuangAutonomous RegionGuilinChina
| | - Fuzhen Liu
- Department of Medical ImagingNanxishan Hospital of Guangxi ZhuangAutonomous RegionGuilinChina
| | - Xiaoyan Qin
- Department of Medical ImagingNanxishan Hospital of Guangxi ZhuangAutonomous RegionGuilinChina
| | - Xin Li
- Department of Medical ImagingNanxishan Hospital of Guangxi ZhuangAutonomous RegionGuilinChina
| | - Peng Yang
- Department of Medical ImagingNanxishan Hospital of Guangxi ZhuangAutonomous RegionGuilinChina
| | - Xin Li
- Department of Medical ImagingNanxishan Hospital of Guangxi ZhuangAutonomous RegionGuilinChina
| | | | - Xiqi Zhu
- Department of Medical ImagingNanxishan Hospital of Guangxi ZhuangAutonomous RegionGuilinChina
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Wang MY, Jia CG, Xu HQ, Xu CS, Li X, Wei W, Chen JC. Development and Validation of a Deep Learning Predictive Model Combining Clinical and Radiomic Features for Short-Term Postoperative Facial Nerve Function in Acoustic Neuroma Patients. Curr Med Sci 2023; 43:336-343. [PMID: 37059936 PMCID: PMC10103675 DOI: 10.1007/s11596-023-2713-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 01/14/2023] [Indexed: 04/16/2023]
Abstract
OBJECTIVE This study aims to construct and validate a predictable deep learning model associated with clinical data and multi-sequence magnetic resonance imaging (MRI) for short-term postoperative facial nerve function in patients with acoustic neuroma. METHODS A total of 110 patients with acoustic neuroma who underwent surgery through the retrosigmoid sinus approach were included. Clinical data and raw features from four MRI sequences (T1-weighted, T2-weighted, T1-weighted contrast enhancement, and T2-weighted-Flair images) were analyzed. Spearman correlation analysis along with least absolute shrinkage and selection operator regression were used to screen combined clinical and radiomic features. Nomogram, machine learning, and convolutional neural network (CNN) models were constructed to predict the prognosis of facial nerve function on the seventh day after surgery. Receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to evaluate model performance. A total of 1050 radiomic parameters were extracted, from which 13 radiomic and 3 clinical features were selected. RESULTS The CNN model performed best among all prediction models in the test set with an area under the curve (AUC) of 0.89 (95% CI, 0.84-0.91). CONCLUSION CNN modeling that combines clinical and multi-sequence MRI radiomic features provides excellent performance for predicting short-term facial nerve function after surgery in patients with acoustic neuroma. As such, CNN modeling may serve as a potential decision-making tool for neurosurgery.
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Affiliation(s)
- Meng-Yang Wang
- Department of Neurosurgery, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China
| | - Chen-Guang Jia
- Department of Neurosurgery, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China
| | - Huan-Qing Xu
- School of Medical Information Engineering, Anhui University of Chinese Medicine, Hefei, 230012, China
| | - Cheng-Shi Xu
- Department of Neurosurgery, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China
| | - Xiang Li
- Department of Neurosurgery, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China
| | - Wei Wei
- Department of Neurosurgery, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China.
| | - Jin-Cao Chen
- Department of Neurosurgery, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China.
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Zhang J, Liu J, Liang Z, Xia L, Zhang W, Xing Y, Zhang X, Tang G. Differentiation of acute and chronic vertebral compression fractures using conventional CT based on deep transfer learning features and hand-crafted radiomics features. BMC Musculoskelet Disord 2023; 24:165. [PMID: 36879285 PMCID: PMC9987077 DOI: 10.1186/s12891-023-06281-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Accepted: 02/28/2023] [Indexed: 03/08/2023] Open
Abstract
BACKGROUND We evaluated the diagnostic efficacy of deep learning radiomics (DLR) and hand-crafted radiomics (HCR) features in differentiating acute and chronic vertebral compression fractures (VCFs). METHODS A total of 365 patients with VCFs were retrospectively analysed based on their computed tomography (CT) scan data. All patients completed MRI examination within 2 weeks. There were 315 acute VCFs and 205 chronic VCFs. Deep transfer learning (DTL) features and HCR features were extracted from CT images of patients with VCFs using DLR and traditional radiomics, respectively, and feature fusion was performed to establish the least absolute shrinkage and selection operator. The MRI display of vertebral bone marrow oedema was used as the gold standard for acute VCF, and the model performance was evaluated using the receiver operating characteristic (ROC).To separately evaluate the effectiveness of DLR, traditional radiomics and feature fusion in the differential diagnosis of acute and chronic VCFs, we constructed a nomogram based on the clinical baseline data to visualize the classification evaluation. The predictive power of each model was compared using the Delong test, and the clinical value of the nomogram was evaluated using decision curve analysis (DCA). RESULTS Fifty DTL features were obtained from DLR, 41 HCR features were obtained from traditional radiomics, and 77 features fusion were obtained after feature screening and fusion of the two. The area under the curve (AUC) of the DLR model in the training cohort and test cohort were 0.992 (95% confidence interval (CI), 0.983-0.999) and 0.871 (95% CI, 0.805-0.938), respectively. While the AUCs of the conventional radiomics model in the training cohort and test cohort were 0.973 (95% CI, 0.955-0.990) and 0.854 (95% CI, 0.773-0.934), respectively. The AUCs of the features fusion model in the training cohort and test cohort were 0.997 (95% CI, 0.994-0.999) and 0.915 (95% CI, 0.855-0.974), respectively. The AUCs of nomogram constructed by the features fusion in combination with clinical baseline data were 0.998 (95% CI, 0.996-0.999) and 0.946 (95% CI, 0.906-0.987) in the training cohort and test cohort, respectively. The Delong test showed that the differences between the features fusion model and the nomogram in the training cohort and the test cohort were not statistically significant (P values were 0.794 and 0.668, respectively), and the differences in the other prediction models in the training cohort and the test cohort were statistically significant (P < 0.05). DCA showed that the nomogram had high clinical value. CONCLUSION The features fusion model can be used for the differential diagnosis of acute and chronic VCFs, and its differential diagnosis ability is improved when compared with that when either radiomics is used alone. At the same time, the nomogram has a high predictive value for acute and chronic VCFs and can be a potential decision-making tool to assist clinicians, especially when a patient is unable to undergo spinal MRI examination.
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Affiliation(s)
- Jun Zhang
- Department of Radiology, Clinical Medical College of Shanghai Tenth People's Hospital of Nanjing Medical University, 301 Middle Yanchang Road, Shanghai, 200072, P.R. China.,Department of Radiology, Sir RunRun Hospital affiliated to Nanjing Medical University, 109 Longmian Road, Nanjing, Jiangsu, 211002, P.R. China
| | - Jiayi Liu
- Department of Radiology, Sir RunRun Hospital affiliated to Nanjing Medical University, 109 Longmian Road, Nanjing, Jiangsu, 211002, P.R. China
| | - Zhipeng Liang
- Department of Radiology, Sir RunRun Hospital affiliated to Nanjing Medical University, 109 Longmian Road, Nanjing, Jiangsu, 211002, P.R. China
| | - Liang Xia
- Department of Radiology, Sir RunRun Hospital affiliated to Nanjing Medical University, 109 Longmian Road, Nanjing, Jiangsu, 211002, P.R. China
| | - Weixiao Zhang
- Department of Radiology, Sir RunRun Hospital affiliated to Nanjing Medical University, 109 Longmian Road, Nanjing, Jiangsu, 211002, P.R. China
| | - Yanfen Xing
- Department of Radiology, Sir RunRun Hospital affiliated to Nanjing Medical University, 109 Longmian Road, Nanjing, Jiangsu, 211002, P.R. China
| | - Xueli Zhang
- Department of Radiology, Shanghai TenthPeople's Hospital, Tongji University School of Medicine, 301 Middle Yanchang Road, Shanghai, 200072, P.R. China
| | - Guangyu Tang
- Department of Radiology, Clinical Medical College of Shanghai Tenth People's Hospital of Nanjing Medical University, 301 Middle Yanchang Road, Shanghai, 200072, P.R. China. .,Department of Radiology, Shanghai TenthPeople's Hospital, Tongji University School of Medicine, 301 Middle Yanchang Road, Shanghai, 200072, P.R. China.
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Hu X, Zhu Y, Qian Y, Huang R, Yin S, Zeng Z, Xie N, Ma B, Yu Y, Zhao Q, Wu Z, Wang J, Xu W, Ren Y, Li C, Zhu R, Cheng L. Prediction of subsequent osteoporotic vertebral compression fracture on CT radiography via deep learning. VIEW 2022. [DOI: 10.1002/viw.20220012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Affiliation(s)
- Xiao Hu
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Orthopaedic Department of Tongji Hospital, School of Medicine, School of Life Sciences and Technology Tongji University Shanghai China
- Division of Spine Department of Orthopaedics Tongji Hospital Tongji University School of Medicine Shanghai China
| | - Yanjing Zhu
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Orthopaedic Department of Tongji Hospital, School of Medicine, School of Life Sciences and Technology Tongji University Shanghai China
| | - Yadong Qian
- Division of Spine Department of Orthopaedics Tongji Hospital Tongji University School of Medicine Shanghai China
| | - Ruiqi Huang
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Orthopaedic Department of Tongji Hospital, School of Medicine, School of Life Sciences and Technology Tongji University Shanghai China
| | - Shuai Yin
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Orthopaedic Department of Tongji Hospital, School of Medicine, School of Life Sciences and Technology Tongji University Shanghai China
| | - Zhili Zeng
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Orthopaedic Department of Tongji Hospital, School of Medicine, School of Life Sciences and Technology Tongji University Shanghai China
- Division of Spine Department of Orthopaedics Tongji Hospital Tongji University School of Medicine Shanghai China
| | - Ning Xie
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Orthopaedic Department of Tongji Hospital, School of Medicine, School of Life Sciences and Technology Tongji University Shanghai China
- Division of Spine Department of Orthopaedics Tongji Hospital Tongji University School of Medicine Shanghai China
| | - Bin Ma
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Orthopaedic Department of Tongji Hospital, School of Medicine, School of Life Sciences and Technology Tongji University Shanghai China
- Division of Spine Department of Orthopaedics Tongji Hospital Tongji University School of Medicine Shanghai China
| | - Yan Yu
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Orthopaedic Department of Tongji Hospital, School of Medicine, School of Life Sciences and Technology Tongji University Shanghai China
- Division of Spine Department of Orthopaedics Tongji Hospital Tongji University School of Medicine Shanghai China
| | - Qing Zhao
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Orthopaedic Department of Tongji Hospital, School of Medicine, School of Life Sciences and Technology Tongji University Shanghai China
| | - Zhourui Wu
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Orthopaedic Department of Tongji Hospital, School of Medicine, School of Life Sciences and Technology Tongji University Shanghai China
- Division of Spine Department of Orthopaedics Tongji Hospital Tongji University School of Medicine Shanghai China
| | - Jianjie Wang
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Orthopaedic Department of Tongji Hospital, School of Medicine, School of Life Sciences and Technology Tongji University Shanghai China
- Division of Spine Department of Orthopaedics Tongji Hospital Tongji University School of Medicine Shanghai China
| | - Wei Xu
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Orthopaedic Department of Tongji Hospital, School of Medicine, School of Life Sciences and Technology Tongji University Shanghai China
- Division of Spine Department of Orthopaedics Tongji Hospital Tongji University School of Medicine Shanghai China
| | - Yilong Ren
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Orthopaedic Department of Tongji Hospital, School of Medicine, School of Life Sciences and Technology Tongji University Shanghai China
- Division of Spine Department of Orthopaedics Tongji Hospital Tongji University School of Medicine Shanghai China
| | - Chen Li
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Orthopaedic Department of Tongji Hospital, School of Medicine, School of Life Sciences and Technology Tongji University Shanghai China
- Division of Spine Department of Orthopaedics Tongji Hospital Tongji University School of Medicine Shanghai China
| | - Rongrong Zhu
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Orthopaedic Department of Tongji Hospital, School of Medicine, School of Life Sciences and Technology Tongji University Shanghai China
| | - Liming Cheng
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Orthopaedic Department of Tongji Hospital, School of Medicine, School of Life Sciences and Technology Tongji University Shanghai China
- Division of Spine Department of Orthopaedics Tongji Hospital Tongji University School of Medicine Shanghai China
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