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Zheng J, Liu W, Chen J, Sun Y, Chen C, Li J, Yi C, Zeng G, Chen Y, Song W. Differential diagnostic value of radiomics models in benign versus malignant vertebral compression fractures: A systematic review and meta-analysis. Eur J Radiol 2024; 178:111621. [PMID: 39018646 DOI: 10.1016/j.ejrad.2024.111621] [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: 12/08/2023] [Revised: 06/29/2024] [Accepted: 07/11/2024] [Indexed: 07/19/2024]
Abstract
PURPOSE Early diagnosis of benign and malignant vertebral compression fractures by analyzing imaging data is crucial to guide treatment and assess prognosis, and the development of radiomics made it an alternative option to biopsy examination. This systematic review and meta-analysis was conducted with the purpose of quantifying the diagnostic efficacy of radiomics models in distinguishing between benign and malignant vertebral compression fractures. METHODS Searching on PubMed, Embase, Web of Science and Cochrane Library was conducted to identify eligible studies published before September 23, 2023. After evaluating for methodological quality and risk of bias using the Radiomics Quality Score (RQS) and the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2), we selected studies providing confusion matrix results to be included in random-effects meta-analysis. RESULTS A total of sixteen articles, involving 1,519 vertebrae with pathological-diagnosed tumor infiltration, were included in our meta-analysis. The combined sensitivity and specificity of the top-performing models were 0.92 (95 % CI: 0.87-0.96) and 0.93 (95 % CI: 0.88-0.96), respectively. Their AUC was 0.97 (95 % CI: 0.96-0.99). By contrast, radiologists' combined sensitivity was 0.90 (95 %CI: 0.75-0.97) and specificity was 0.92 (95 %CI: 0.67-0.98). The AUC was 0.96 (95 %CI: 0.94-0.97). Subsequent subgroup analysis and sensitivity test suggested that part of the heterogeneity might be explained by differences in imaging modality, segmentation, deep learning and cross-validation. CONCLUSION We found remarkable diagnosis potential in correctly distinguishing vertebral compression fractures in complex clinical contexts. However, the published radiomics models still have a great heterogeneity, and more large-scale clinical trials are essential to validate their generalizability.
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Affiliation(s)
- Jiayuan Zheng
- Department of Orthopedic Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China.
| | - Wenzhou Liu
- Department of Orthopedic Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China.
| | - Jianan Chen
- Department of Orthopedic Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China.
| | - Yujun Sun
- Department of Orthopedic Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China.
| | - Chen Chen
- Department of Orthopedic Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China.
| | - Jiajie Li
- Department of Orthopedic Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China.
| | - Chunyan Yi
- Department of Orthopedic Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China.
| | - Gang Zeng
- Department of Orthopedic Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China.
| | - Yanbo Chen
- Department of Orthopedic Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China.
| | - Weidong Song
- Department of Orthopedic Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China.
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Lee S, Jung JY, Mahatthanatrakul A, Kim JS. Artificial Intelligence in Spinal Imaging and Patient Care: A Review of Recent Advances. Neurospine 2024; 21:474-486. [PMID: 38955525 PMCID: PMC11224760 DOI: 10.14245/ns.2448388.194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Revised: 05/14/2024] [Accepted: 05/23/2024] [Indexed: 07/04/2024] Open
Abstract
Artificial intelligence (AI) is transforming spinal imaging and patient care through automated analysis and enhanced decision-making. This review presents a clinical task-based evaluation, highlighting the specific impact of AI techniques on different aspects of spinal imaging and patient care. We first discuss how AI can potentially improve image quality through techniques like denoising or artifact reduction. We then explore how AI enables efficient quantification of anatomical measurements, spinal curvature parameters, vertebral segmentation, and disc grading. This facilitates objective, accurate interpretation and diagnosis. AI models now reliably detect key spinal pathologies, achieving expert-level performance in tasks like identifying fractures, stenosis, infections, and tumors. Beyond diagnosis, AI also assists surgical planning via synthetic computed tomography generation, augmented reality systems, and robotic guidance. Furthermore, AI image analysis combined with clinical data enables personalized predictions to guide treatment decisions, such as forecasting spine surgery outcomes. However, challenges still need to be addressed in implementing AI clinically, including model interpretability, generalizability, and data limitations. Multicenter collaboration using large, diverse datasets is critical to advance the field further. While adoption barriers persist, AI presents a transformative opportunity to revolutionize spinal imaging workflows, empowering clinicians to translate data into actionable insights for improved patient care.
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Affiliation(s)
- Sungwon Lee
- Department of Radiology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
- Visual Analysis and Learning for Improved Diagnostics (VALID) Lab, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Joon-Yong Jung
- Department of Radiology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
- Visual Analysis and Learning for Improved Diagnostics (VALID) Lab, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Akaworn Mahatthanatrakul
- Department of Orthopaedics, Faculty of Medicine, Naresuan University Hospital, Phitsanulok, Thailand
| | - Jin-Sung Kim
- Spine Center, Department of Neurosurgery, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
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Nakagawa J, Fujima N, Hirata K, Harada T, Wakabayashi N, Takano Y, Homma A, Kano S, Minowa K, Kudo K. Diagnosis of skull-base invasion by nasopharyngeal tumors on CT with a deep-learning approach. Jpn J Radiol 2024; 42:450-459. [PMID: 38280100 PMCID: PMC11056334 DOI: 10.1007/s11604-023-01527-7] [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: 11/10/2023] [Accepted: 12/24/2023] [Indexed: 01/29/2024]
Abstract
PURPOSE To develop a convolutional neural network (CNN) model to diagnose skull-base invasion by nasopharyngeal malignancies in CT images and evaluate the model's diagnostic performance. MATERIALS AND METHODS We divided 100 malignant nasopharyngeal tumor lesions into a training (n = 70) and a test (n = 30) dataset. Two head/neck radiologists reviewed CT and MRI images and determined the positive/negative skull-base invasion status of each case (training dataset: 29 invasion-positive and 41 invasion-negative; test dataset: 13 invasion-positive and 17 invasion-negative). Preprocessing involved extracting continuous slices of the nasopharynx and clivus. The preprocessed training dataset was used for transfer learning with Residual Neural Networks 50 to create a diagnostic CNN model, which was then tested on the preprocessed test dataset to determine the invasion status and model performance. Original CT images from the test dataset were reviewed by a radiologist with extensive head/neck imaging experience (senior reader: SR) and another less-experienced radiologist (junior reader: JR). Gradient-weighted class activation maps (Grad-CAMs) were created to visualize the explainability of the invasion status classification. RESULTS The CNN model's diagnostic accuracy was 0.973, significantly higher than those of the two radiologists (SR: 0.838; JR: 0.595). Receiver operating characteristic curve analysis gave an area under the curve of 0.953 for the CNN model (versus 0.832 and 0.617 for SR and JR; both p < 0.05). The Grad-CAMs suggested that the invasion-negative cases were present predominantly in bone marrow, while the invasion-positive cases exhibited osteosclerosis and nasopharyngeal masses. CONCLUSIONS This CNN technique would be useful for CT-based diagnosis of skull-base invasion by nasopharyngeal malignancies.
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Affiliation(s)
- Junichi Nakagawa
- Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, N15 W7, Kita-Ku, Sapporo, Hokkaido, 060-8638, Japan
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N14 W5, Kita-Ku, Sapporo, Hokkaido, 060-8648, Japan
| | - Noriyuki Fujima
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N14 W5, Kita-Ku, Sapporo, Hokkaido, 060-8648, Japan.
- Global Center for Biomedical Science and Engineering, Faculty of Medicine, Hokkaido University, N14 W5, Kita-Ku, Sapporo, Hokkaido, 060-8638, Japan.
| | - Kenji Hirata
- Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, N15 W7, Kita-Ku, Sapporo, Hokkaido, 060-8638, Japan
- Global Center for Biomedical Science and Engineering, Faculty of Medicine, Hokkaido University, N14 W5, Kita-Ku, Sapporo, Hokkaido, 060-8638, Japan
- Department of Nuclear Medicine, Hokkaido University Hospital, N14 W5, Kita-Ku, Sapporo, Hokkaido, 060-8648, Japan
- Medical AI Research and Development Center, Hokkaido University Hospital, N14 W5, Kita-Ku, Sapporo, Hokkaido, 060-8648, Japan
| | - Taisuke Harada
- Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, N15 W7, Kita-Ku, Sapporo, Hokkaido, 060-8638, Japan
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N14 W5, Kita-Ku, Sapporo, Hokkaido, 060-8648, Japan
- Global Center for Biomedical Science and Engineering, Faculty of Medicine, Hokkaido University, N14 W5, Kita-Ku, Sapporo, Hokkaido, 060-8638, Japan
| | - Naoto Wakabayashi
- Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, N15 W7, Kita-Ku, Sapporo, Hokkaido, 060-8638, Japan
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N14 W5, Kita-Ku, Sapporo, Hokkaido, 060-8648, Japan
| | - Yuki Takano
- Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, N15 W7, Kita-Ku, Sapporo, Hokkaido, 060-8638, Japan
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N14 W5, Kita-Ku, Sapporo, Hokkaido, 060-8648, Japan
| | - Akihiro Homma
- Department of Otolaryngology-Head and Neck Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, N15 W7, Kita Ku, Sapporo, 060-8638, Japan
| | - Satoshi Kano
- Department of Otolaryngology-Head and Neck Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, N15 W7, Kita Ku, Sapporo, 060-8638, Japan
| | - Kazuyuki Minowa
- Faculty of Dental Medicine Department of Radiology, Hokkaido University, N13 W7, Kita-Ku, Sapporo, Hokkaido, 060-8586, Japan
| | - Kohsuke Kudo
- Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, N15 W7, Kita-Ku, Sapporo, Hokkaido, 060-8638, Japan
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N14 W5, Kita-Ku, Sapporo, Hokkaido, 060-8648, Japan
- Department of Nuclear Medicine, Hokkaido University Hospital, N14 W5, Kita-Ku, Sapporo, Hokkaido, 060-8648, Japan
- Medical AI Research and Development Center, Hokkaido University Hospital, N14 W5, Kita-Ku, Sapporo, Hokkaido, 060-8648, Japan
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Zheng Q, Ni X, Yang R, Jiao P, Wu J, Yang S, Chen Z, Liu X, Wang L. UroAngel: a single-kidney function prediction system based on computed tomography urography using deep learning. World J Urol 2024; 42:238. [PMID: 38627315 DOI: 10.1007/s00345-024-04921-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Accepted: 01/16/2024] [Indexed: 04/19/2024] Open
Abstract
BACKGROUND Accurate estimation of the glomerular filtration rate (GFR) is clinically crucial for determining the status of obstruction, developing treatment strategies, and predicting prognosis in obstructive nephropathy (ON). We aimed to develop a deep learning-based system, named UroAngel, for non-invasive and convenient prediction of single-kidney function level. METHODS We retrospectively collected computed tomography urography (CTU) images and emission computed tomography diagnostic reports of 520 ON patients. A 3D U-Net model was used to segment the renal parenchyma, and a logistic regression multi-classification model was used to predict renal function level. We compared the predictive performance of UroAngel with the Modification of Diet in Renal Disease (MDRD), Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equations, and two expert radiologists in an additional 40 ON patients to validate clinical effectiveness. RESULTS UroAngel based on 3D U-Net convolutional neural network could segment the renal cortex accurately, with a Dice similarity coefficient of 0.861. Using the segmented renal cortex to predict renal function stage had high performance with an accuracy of 0.918, outperforming MDRD and CKD-EPI and two radiologists. CONCLUSIONS We proposed an automated 3D U-Net-based analysis system for direct prediction of single-kidney function stage from CTU images. UroAngel could accurately predict single-kidney function in ON patients, providing a novel, reliable, convenient, and non-invasive method.
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Affiliation(s)
- Qingyuan Zheng
- Department of Urology, Renmin Hospital of Wuhan University, 99 Zhang Zhi-Dong Road, Wuhan, 430060, Hubei, People's Republic of China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan, 430060, Hubei, People's Republic of China
| | - Xinmiao Ni
- Department of Urology, Renmin Hospital of Wuhan University, 99 Zhang Zhi-Dong Road, Wuhan, 430060, Hubei, People's Republic of China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan, 430060, Hubei, People's Republic of China
| | - Rui Yang
- Department of Urology, Renmin Hospital of Wuhan University, 99 Zhang Zhi-Dong Road, Wuhan, 430060, Hubei, People's Republic of China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan, 430060, Hubei, People's Republic of China
| | - Panpan Jiao
- Department of Urology, Renmin Hospital of Wuhan University, 99 Zhang Zhi-Dong Road, Wuhan, 430060, Hubei, People's Republic of China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan, 430060, Hubei, People's Republic of China
| | - Jiejun Wu
- Department of Urology, Renmin Hospital of Wuhan University, 99 Zhang Zhi-Dong Road, Wuhan, 430060, Hubei, People's Republic of China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan, 430060, Hubei, People's Republic of China
| | - Song Yang
- Department of Urology, Renmin Hospital of Wuhan University, 99 Zhang Zhi-Dong Road, Wuhan, 430060, Hubei, People's Republic of China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan, 430060, Hubei, People's Republic of China
| | - Zhiyuan Chen
- Department of Urology, Renmin Hospital of Wuhan University, 99 Zhang Zhi-Dong Road, Wuhan, 430060, Hubei, People's Republic of China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan, 430060, Hubei, People's Republic of China
| | - Xiuheng Liu
- Department of Urology, Renmin Hospital of Wuhan University, 99 Zhang Zhi-Dong Road, Wuhan, 430060, Hubei, People's Republic of China.
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan, 430060, Hubei, People's Republic of China.
| | - Lei Wang
- Department of Urology, Renmin Hospital of Wuhan University, 99 Zhang Zhi-Dong Road, Wuhan, 430060, Hubei, People's Republic of China.
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan, 430060, Hubei, People's Republic of China.
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Kim YR, Yoon YS, Cha JG. Opportunistic Screening for Acute Vertebral Fractures on a Routine Abdominal or Chest Computed Tomography Scans Using an Automated Deep Learning Model. Diagnostics (Basel) 2024; 14:781. [PMID: 38611694 PMCID: PMC11011775 DOI: 10.3390/diagnostics14070781] [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: 03/13/2024] [Revised: 03/31/2024] [Accepted: 04/04/2024] [Indexed: 04/14/2024] Open
Abstract
OBJECTIVES To develop an opportunistic screening model based on a deep learning algorithm to detect recent vertebral fractures in abdominal or chest CTs. MATERIALS AND METHODS A total of 1309 coronal reformatted images (504 with a recent fracture from 119 patients, and 805 without fracture from 115 patients), from torso CTs, performed from September 2018 to April 2022, on patients who also had a spine MRI within two months, were included. Two readers participated in image selection and manually labeled the fractured segment on each selected image with Neuro-T (version 2.3.3; Neurocle Inc.) software. We split the images randomly into the training and internal test set (labeled: unlabeled = 480:700) and the secondary interval validation set (24:105). For the observer study, three radiologists reviewed the CT images in the external test set with and without deep learning assistance and scored the likelihood of an acute fracture in each image independently. RESULTS For the training and internal test sets, the AI achieved a 99.86% test accuracy, 91.22% precision, and 89.18% F1 score for detection of recent fracture. Then, in the secondary internal validation set, it achieved 99.90%, 74.93%, and 78.30%, respectively. In the observer study, with the assistance of the deep learning algorithm, a significant improvement was observed in the radiology resident's accuracy, from 92.79% to 98.2% (p = 0.04). CONCLUSION The model showed a high level of accuracy in the test set and also the internal validation set. If this algorithm is applied opportunistically to daily torso CT evaluation, it will be helpful for the early detection of fractures that require treatment.
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Affiliation(s)
- Ye Rin Kim
- Department of Radiology, College of Medicine, Soonchunhyang University Bucheon Hospital, Soonchunhyang University, Bucheon 14584, Republic of Korea
| | - Yu Sung Yoon
- Department of Radiology, School of Medicine, Kyungpook National University Hospital, Kyungpook National University, Daegu 41944, Republic of Korea
| | - Jang Gyu Cha
- Department of Radiology, College of Medicine, Soonchunhyang University Bucheon Hospital, Soonchunhyang University, Bucheon 14584, Republic of Korea
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Foreman SC, Schinz D, El Husseini M, Goller SS, Weißinger J, Dietrich AS, Renz M, Metz MC, Feuerriegel GC, Wiestler B, Stahl R, Schwaiger BJ, Makowski MR, Kirschke JS, Gersing AS. Deep Learning to Differentiate Benign and Malignant Vertebral Fractures at Multidetector CT. Radiology 2024; 310:e231429. [PMID: 38530172 DOI: 10.1148/radiol.231429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/27/2024]
Abstract
Background Differentiating between benign and malignant vertebral fractures poses diagnostic challenges. Purpose To investigate the reliability of CT-based deep learning models to differentiate between benign and malignant vertebral fractures. Materials and Methods CT scans acquired in patients with benign or malignant vertebral fractures from June 2005 to December 2022 at two university hospitals were retrospectively identified based on a composite reference standard that included histopathologic and radiologic information. An internal test set was randomly selected, and an external test set was obtained from an additional hospital. Models used a three-dimensional U-Net encoder-classifier architecture and applied data augmentation during training. Performance was evaluated using the area under the receiver operating characteristic curve (AUC) and compared with that of two residents and one fellowship-trained radiologist using the DeLong test. Results The training set included 381 patients (mean age, 69.9 years ± 11.4 [SD]; 193 male) with 1307 vertebrae (378 benign fractures, 447 malignant fractures, 482 malignant lesions). Internal and external test sets included 86 (mean age, 66.9 years ± 12; 45 male) and 65 (mean age, 68.8 years ± 12.5; 39 female) patients, respectively. The better-performing model of two training approaches achieved AUCs of 0.85 (95% CI: 0.77, 0.92) in the internal and 0.75 (95% CI: 0.64, 0.85) in the external test sets. Including an uncertainty category further improved performance to AUCs of 0.91 (95% CI: 0.83, 0.97) in the internal test set and 0.76 (95% CI: 0.64, 0.88) in the external test set. The AUC values of residents were lower than that of the best-performing model in the internal test set (AUC, 0.69 [95% CI: 0.59, 0.78] and 0.71 [95% CI: 0.61, 0.80]) and external test set (AUC, 0.70 [95% CI: 0.58, 0.80] and 0.71 [95% CI: 0.60, 0.82]), with significant differences only for the internal test set (P < .001). The AUCs of the fellowship-trained radiologist were similar to those of the best-performing model (internal test set, 0.86 [95% CI: 0.78, 0.93; P = .39]; external test set, 0.71 [95% CI: 0.60, 0.82; P = .46]). Conclusion Developed models showed a high discriminatory power to differentiate between benign and malignant vertebral fractures, surpassing or matching the performance of radiology residents and matching that of a fellowship-trained radiologist. © RSNA, 2024 See also the editorial by Booz and D'Angelo in this issue.
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Affiliation(s)
- Sarah C Foreman
- From the Departments of Radiology (S.C.F., A.S.D., G.C.F., M.R.M.) and Neuroradiology (D.S., M.E.H., M.R., M.C.M., B.W., B.J.S., J.S.K.), Klinikum Rechts der Isar, Technische Universität München, Ismaninger Strasse 22, 81675 Munich, Germany; Departments of Radiology (S.S.G., J.W.) and Neuroradiology (R.S., A.S.G.), University Hospital Munich (LMU), Munich, Germany; and German Cancer Consortium (DKTK), Partner Site Munich, and German Cancer Research Center (DKFZ), Heidelberg, Germany (B.W.)
| | - David Schinz
- From the Departments of Radiology (S.C.F., A.S.D., G.C.F., M.R.M.) and Neuroradiology (D.S., M.E.H., M.R., M.C.M., B.W., B.J.S., J.S.K.), Klinikum Rechts der Isar, Technische Universität München, Ismaninger Strasse 22, 81675 Munich, Germany; Departments of Radiology (S.S.G., J.W.) and Neuroradiology (R.S., A.S.G.), University Hospital Munich (LMU), Munich, Germany; and German Cancer Consortium (DKTK), Partner Site Munich, and German Cancer Research Center (DKFZ), Heidelberg, Germany (B.W.)
| | - Malek El Husseini
- From the Departments of Radiology (S.C.F., A.S.D., G.C.F., M.R.M.) and Neuroradiology (D.S., M.E.H., M.R., M.C.M., B.W., B.J.S., J.S.K.), Klinikum Rechts der Isar, Technische Universität München, Ismaninger Strasse 22, 81675 Munich, Germany; Departments of Radiology (S.S.G., J.W.) and Neuroradiology (R.S., A.S.G.), University Hospital Munich (LMU), Munich, Germany; and German Cancer Consortium (DKTK), Partner Site Munich, and German Cancer Research Center (DKFZ), Heidelberg, Germany (B.W.)
| | - Sophia S Goller
- From the Departments of Radiology (S.C.F., A.S.D., G.C.F., M.R.M.) and Neuroradiology (D.S., M.E.H., M.R., M.C.M., B.W., B.J.S., J.S.K.), Klinikum Rechts der Isar, Technische Universität München, Ismaninger Strasse 22, 81675 Munich, Germany; Departments of Radiology (S.S.G., J.W.) and Neuroradiology (R.S., A.S.G.), University Hospital Munich (LMU), Munich, Germany; and German Cancer Consortium (DKTK), Partner Site Munich, and German Cancer Research Center (DKFZ), Heidelberg, Germany (B.W.)
| | - Jürgen Weißinger
- From the Departments of Radiology (S.C.F., A.S.D., G.C.F., M.R.M.) and Neuroradiology (D.S., M.E.H., M.R., M.C.M., B.W., B.J.S., J.S.K.), Klinikum Rechts der Isar, Technische Universität München, Ismaninger Strasse 22, 81675 Munich, Germany; Departments of Radiology (S.S.G., J.W.) and Neuroradiology (R.S., A.S.G.), University Hospital Munich (LMU), Munich, Germany; and German Cancer Consortium (DKTK), Partner Site Munich, and German Cancer Research Center (DKFZ), Heidelberg, Germany (B.W.)
| | - Anna-Sophia Dietrich
- From the Departments of Radiology (S.C.F., A.S.D., G.C.F., M.R.M.) and Neuroradiology (D.S., M.E.H., M.R., M.C.M., B.W., B.J.S., J.S.K.), Klinikum Rechts der Isar, Technische Universität München, Ismaninger Strasse 22, 81675 Munich, Germany; Departments of Radiology (S.S.G., J.W.) and Neuroradiology (R.S., A.S.G.), University Hospital Munich (LMU), Munich, Germany; and German Cancer Consortium (DKTK), Partner Site Munich, and German Cancer Research Center (DKFZ), Heidelberg, Germany (B.W.)
| | - Martin Renz
- From the Departments of Radiology (S.C.F., A.S.D., G.C.F., M.R.M.) and Neuroradiology (D.S., M.E.H., M.R., M.C.M., B.W., B.J.S., J.S.K.), Klinikum Rechts der Isar, Technische Universität München, Ismaninger Strasse 22, 81675 Munich, Germany; Departments of Radiology (S.S.G., J.W.) and Neuroradiology (R.S., A.S.G.), University Hospital Munich (LMU), Munich, Germany; and German Cancer Consortium (DKTK), Partner Site Munich, and German Cancer Research Center (DKFZ), Heidelberg, Germany (B.W.)
| | - Marie-Christin Metz
- From the Departments of Radiology (S.C.F., A.S.D., G.C.F., M.R.M.) and Neuroradiology (D.S., M.E.H., M.R., M.C.M., B.W., B.J.S., J.S.K.), Klinikum Rechts der Isar, Technische Universität München, Ismaninger Strasse 22, 81675 Munich, Germany; Departments of Radiology (S.S.G., J.W.) and Neuroradiology (R.S., A.S.G.), University Hospital Munich (LMU), Munich, Germany; and German Cancer Consortium (DKTK), Partner Site Munich, and German Cancer Research Center (DKFZ), Heidelberg, Germany (B.W.)
| | - Georg C Feuerriegel
- From the Departments of Radiology (S.C.F., A.S.D., G.C.F., M.R.M.) and Neuroradiology (D.S., M.E.H., M.R., M.C.M., B.W., B.J.S., J.S.K.), Klinikum Rechts der Isar, Technische Universität München, Ismaninger Strasse 22, 81675 Munich, Germany; Departments of Radiology (S.S.G., J.W.) and Neuroradiology (R.S., A.S.G.), University Hospital Munich (LMU), Munich, Germany; and German Cancer Consortium (DKTK), Partner Site Munich, and German Cancer Research Center (DKFZ), Heidelberg, Germany (B.W.)
| | - Benedikt Wiestler
- From the Departments of Radiology (S.C.F., A.S.D., G.C.F., M.R.M.) and Neuroradiology (D.S., M.E.H., M.R., M.C.M., B.W., B.J.S., J.S.K.), Klinikum Rechts der Isar, Technische Universität München, Ismaninger Strasse 22, 81675 Munich, Germany; Departments of Radiology (S.S.G., J.W.) and Neuroradiology (R.S., A.S.G.), University Hospital Munich (LMU), Munich, Germany; and German Cancer Consortium (DKTK), Partner Site Munich, and German Cancer Research Center (DKFZ), Heidelberg, Germany (B.W.)
| | - Robert Stahl
- From the Departments of Radiology (S.C.F., A.S.D., G.C.F., M.R.M.) and Neuroradiology (D.S., M.E.H., M.R., M.C.M., B.W., B.J.S., J.S.K.), Klinikum Rechts der Isar, Technische Universität München, Ismaninger Strasse 22, 81675 Munich, Germany; Departments of Radiology (S.S.G., J.W.) and Neuroradiology (R.S., A.S.G.), University Hospital Munich (LMU), Munich, Germany; and German Cancer Consortium (DKTK), Partner Site Munich, and German Cancer Research Center (DKFZ), Heidelberg, Germany (B.W.)
| | - Benedikt J Schwaiger
- From the Departments of Radiology (S.C.F., A.S.D., G.C.F., M.R.M.) and Neuroradiology (D.S., M.E.H., M.R., M.C.M., B.W., B.J.S., J.S.K.), Klinikum Rechts der Isar, Technische Universität München, Ismaninger Strasse 22, 81675 Munich, Germany; Departments of Radiology (S.S.G., J.W.) and Neuroradiology (R.S., A.S.G.), University Hospital Munich (LMU), Munich, Germany; and German Cancer Consortium (DKTK), Partner Site Munich, and German Cancer Research Center (DKFZ), Heidelberg, Germany (B.W.)
| | - Marcus R Makowski
- From the Departments of Radiology (S.C.F., A.S.D., G.C.F., M.R.M.) and Neuroradiology (D.S., M.E.H., M.R., M.C.M., B.W., B.J.S., J.S.K.), Klinikum Rechts der Isar, Technische Universität München, Ismaninger Strasse 22, 81675 Munich, Germany; Departments of Radiology (S.S.G., J.W.) and Neuroradiology (R.S., A.S.G.), University Hospital Munich (LMU), Munich, Germany; and German Cancer Consortium (DKTK), Partner Site Munich, and German Cancer Research Center (DKFZ), Heidelberg, Germany (B.W.)
| | - Jan S Kirschke
- From the Departments of Radiology (S.C.F., A.S.D., G.C.F., M.R.M.) and Neuroradiology (D.S., M.E.H., M.R., M.C.M., B.W., B.J.S., J.S.K.), Klinikum Rechts der Isar, Technische Universität München, Ismaninger Strasse 22, 81675 Munich, Germany; Departments of Radiology (S.S.G., J.W.) and Neuroradiology (R.S., A.S.G.), University Hospital Munich (LMU), Munich, Germany; and German Cancer Consortium (DKTK), Partner Site Munich, and German Cancer Research Center (DKFZ), Heidelberg, Germany (B.W.)
| | - Alexandra S Gersing
- From the Departments of Radiology (S.C.F., A.S.D., G.C.F., M.R.M.) and Neuroradiology (D.S., M.E.H., M.R., M.C.M., B.W., B.J.S., J.S.K.), Klinikum Rechts der Isar, Technische Universität München, Ismaninger Strasse 22, 81675 Munich, Germany; Departments of Radiology (S.S.G., J.W.) and Neuroradiology (R.S., A.S.G.), University Hospital Munich (LMU), Munich, Germany; and German Cancer Consortium (DKTK), Partner Site Munich, and German Cancer Research Center (DKFZ), Heidelberg, Germany (B.W.)
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Booz C, D'Angelo T. Improved Vertebral Fracture Assessment: The Game-Changing Potential of Deep Learning with Multidetector CT. Radiology 2024; 310:e240409. [PMID: 38530170 DOI: 10.1148/radiol.240409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/27/2024]
Affiliation(s)
- Christian Booz
- From the Department of Diagnostic and Interventional Radiology, Division of Experimental Imaging, University Hospital Frankfurt, Theodor-Stern-Kai 7, 60590 Frankfurt am Main, Germany (C.B., T.D.); Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt am Main, Germany (C.B.); Department of Dental and Morphological and Functional Imaging, Diagnostic and Interventional Radiology Unit, University Hospital Messina, Messina, Italy (T.D.); and Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands (T.D.)
| | - Tommaso D'Angelo
- From the Department of Diagnostic and Interventional Radiology, Division of Experimental Imaging, University Hospital Frankfurt, Theodor-Stern-Kai 7, 60590 Frankfurt am Main, Germany (C.B., T.D.); Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt am Main, Germany (C.B.); Department of Dental and Morphological and Functional Imaging, Diagnostic and Interventional Radiology Unit, University Hospital Messina, Messina, Italy (T.D.); and Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands (T.D.)
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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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Tong X, Wang S, Zhang J, Fan Y, Liu Y, Wei W. Automatic Osteoporosis Screening System Using Radiomics and Deep Learning from Low-Dose Chest CT Images. Bioengineering (Basel) 2024; 11:50. [PMID: 38247927 PMCID: PMC10813496 DOI: 10.3390/bioengineering11010050] [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: 11/28/2023] [Revised: 12/21/2023] [Accepted: 12/29/2023] [Indexed: 01/23/2024] Open
Abstract
OBJECTIVE Develop two fully automatic osteoporosis screening systems using deep learning (DL) and radiomics (Rad) techniques based on low-dose chest CT (LDCT) images and evaluate their diagnostic effectiveness. METHODS In total, 434 patients who underwent LDCT and bone mineral density (BMD) examination were retrospectively enrolled and divided into the development set (n = 333) and temporal validation set (n = 101). An automatic thoracic vertebra cancellous bone (TVCB) segmentation model was developed. The Dice similarity coefficient (DSC) was used to evaluate the segmentation performance. Furthermore, the three-class Rad and DL models were developed to distinguish osteoporosis, osteopenia, and normal bone mass. The diagnostic performance of these models was evaluated using the receiver operating characteristic (ROC) curve and decision curve analysis (DCA). RESULTS The automatic segmentation model achieved excellent segmentation performance, with a mean DSC of 0.96 ± 0.02 in the temporal validation set. The Rad model was used to identify osteoporosis, osteopenia, and normal BMD in the temporal validation set, with respective area under the receiver operating characteristic curve (AUC) values of 0.943, 0.801, and 0.932. The DL model achieved higher AUC values of 0.983, 0.906, and 0.969 for the same categories in the same validation set. The Delong test affirmed that both models performed similarly in BMD assessment. However, the accuracy of the DL model is 81.2%, which is better than the 73.3% accuracy of the Rad model in the temporal validation set. Additionally, DCA indicated that the DL model provided a greater net benefit compared to the Rad model across the majority of the reasonable threshold probabilities Conclusions: The automated segmentation framework we developed can accurately segment cancellous bone on low-dose chest CT images. These predictive models, which are based on deep learning and radiomics, provided comparable diagnostic performance in automatic BMD assessment. Nevertheless, it is important to highlight that the DL model demonstrates higher accuracy and precision than the Rad model.
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Affiliation(s)
| | | | | | | | | | - Wei Wei
- Department of Radiology, First Affiliated Hospital of Dalian Medical University, Dalian 116014, China (S.W.); (Y.F.)
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Jo SW, Khil EK, Lee KY, Choi I, Yoon YS, Cha JG, Lee JH, Kim H, Lee SY. Deep learning system for automated detection of posterior ligamentous complex injury in patients with thoracolumbar fracture on MRI. Sci Rep 2023; 13:19017. [PMID: 37923853 PMCID: PMC10624679 DOI: 10.1038/s41598-023-46208-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 10/29/2023] [Indexed: 11/06/2023] Open
Abstract
This study aimed to develop a deep learning (DL) algorithm for automated detection and localization of posterior ligamentous complex (PLC) injury in patients with acute thoracolumbar (TL) fracture on magnetic resonance imaging (MRI) and evaluate its diagnostic performance. In this retrospective multicenter study, using midline sagittal T2-weighted image with fracture (± PLC injury), a training dataset and internal and external validation sets of 300, 100, and 100 patients, were constructed with equal numbers of injured and normal PLCs. The DL algorithm was developed through two steps (Attention U-net and Inception-ResNet-V2). We evaluate the diagnostic performance for PLC injury between the DL algorithm and radiologists with different levels of experience. The area under the curves (AUCs) generated by the DL algorithm were 0.928, 0.916 for internal and external validations, and by two radiologists for observer performance test were 0.930, 0.830, respectively. Although no significant difference was found in diagnosing PLC injury between the DL algorithm and radiologists, the DL algorithm exhibited a trend of higher AUC than the radiology trainee. Notably, the radiology trainee's diagnostic performance significantly improved with DL algorithm assistance. Therefore, the DL algorithm exhibited high diagnostic performance in detecting PLC injuries in acute TL fractures.
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Affiliation(s)
- Sang Won Jo
- Department of Radiology, Hallym University Dongtan Sacred Heart Hospital, 7, Keunjaebong-gil, Hwaseong-si, Republic of Korea
| | - Eun Kyung Khil
- Department of Radiology, Hallym University Dongtan Sacred Heart Hospital, 7, Keunjaebong-gil, Hwaseong-si, Republic of Korea.
- Department of Radiology, Fastbone Orthopedic Hospital, Hwaseong-si, Republic of Korea.
| | - Kyoung Yeon Lee
- Department of Radiology, Hallym University Dongtan Sacred Heart Hospital, 7, Keunjaebong-gil, Hwaseong-si, Republic of Korea
| | - Il Choi
- Department of Neurologic Surgery, Hallym University Dongtan Sacred Heart Hospital, Hwaseong-si, Republic of Korea
| | - Yu Sung Yoon
- Department of Radiology, Soonchunhyang University Bucheon Hospital, Bucheon, Republic of Korea
- Department of Radiology, Kyungpook National University Hospital, Daegu, Republic of Korea
| | - Jang Gyu Cha
- Department of Radiology, Soonchunhyang University Bucheon Hospital, Bucheon, Republic of Korea
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Hallinan JTPD, Zhu L, Tan HWN, Hui SJ, Lim X, Ong BWL, Ong HY, Eide SE, Cheng AJL, Ge S, Kuah T, Lim SWD, Low XZ, Teo EC, Yap QV, Chan YH, Kumar N, Vellayappan BA, Ooi BC, Quek ST, Makmur A, Tan JH. A deep learning-based technique for the diagnosis of epidural spinal cord compression on thoracolumbar CT. EUROPEAN SPINE JOURNAL : OFFICIAL PUBLICATION OF THE EUROPEAN SPINE SOCIETY, THE EUROPEAN SPINAL DEFORMITY SOCIETY, AND THE EUROPEAN SECTION OF THE CERVICAL SPINE RESEARCH SOCIETY 2023; 32:3815-3824. [PMID: 37093263 DOI: 10.1007/s00586-023-07706-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 03/12/2023] [Accepted: 04/06/2023] [Indexed: 04/25/2023]
Abstract
PURPOSE To develop a deep learning (DL) model for epidural spinal cord compression (ESCC) on CT, which will aid earlier ESCC diagnosis for less experienced clinicians. METHODS We retrospectively collected CT and MRI data from adult patients with suspected ESCC at a tertiary referral institute from 2007 till 2020. A total of 183 patients were used for training/validation of the DL model. A separate test set of 40 patients was used for DL model evaluation and comprised 60 staging CT and matched MRI scans performed with an interval of up to 2 months. DL model performance was compared to eight readers: one musculoskeletal radiologist, two body radiologists, one spine surgeon, and four trainee spine surgeons. Diagnostic performance was evaluated using inter-rater agreement, sensitivity, specificity and AUC. RESULTS Overall, 3115 axial CT slices were assessed. The DL model showed high kappa of 0.872 for normal, low and high-grade ESCC (trichotomous), which was superior compared to a body radiologist (R4, κ = 0.667) and all four trainee spine surgeons (κ range = 0.625-0.838)(all p < 0.001). In addition, for dichotomous normal versus any grade of ESCC detection, the DL model showed high kappa (κ = 0.879), sensitivity (91.82), specificity (92.01) and AUC (0.919), with the latter AUC superior to all readers (AUC range = 0.732-0.859, all p < 0.001). CONCLUSION A deep learning model for the objective assessment of ESCC on CT had comparable or superior performance to radiologists and spine surgeons. Earlier diagnosis of ESCC on CT could reduce treatment delays, which are associated with poor outcomes, increased costs, and reduced survival.
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Affiliation(s)
- James Thomas Patrick Decourcy Hallinan
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore, 119074, Singapore.
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore, 117597, Singapore.
| | - Lei Zhu
- Department of Computer Science, School of Computing, National University of Singapore, 13 Computing Drive, Singapore, 117417, Singapore
| | - Hui Wen Natalie Tan
- Department of Orthopaedic Surgery, University Spine Centre, National University Health System, 1E, Lower Kent Ridge Road, Singapore, 119228, Singapore
| | - Si Jian Hui
- Department of Orthopaedic Surgery, University Spine Centre, National University Health System, 1E, Lower Kent Ridge Road, Singapore, 119228, Singapore
| | - Xinyi Lim
- Orthopaedic Centre, Alexandra Hospital, 378 Alexandra Road, Singapore, 159964, Singapore
| | - Bryan Wei Loong Ong
- Department of Orthopaedic Surgery, University Spine Centre, National University Health System, 1E, Lower Kent Ridge Road, Singapore, 119228, Singapore
| | - Han Yang Ong
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore, 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore, 117597, Singapore
| | - Sterling Ellis Eide
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore, 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore, 117597, Singapore
| | - Amanda J L Cheng
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore, 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore, 117597, Singapore
| | - Shuliang Ge
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore, 119074, Singapore
| | - Tricia Kuah
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore, 119074, Singapore
| | - Shi Wei Desmond Lim
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore, 119074, Singapore
| | - Xi Zhen Low
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore, 119074, Singapore
| | - Ee Chin Teo
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore, 119074, Singapore
| | - Qai Ven Yap
- Biostatistics Unit, Yong Loo Lin School of Medicine, 10 Medical Drive, Singapore, 117597, Singapore
| | - Yiong Huak Chan
- Biostatistics Unit, Yong Loo Lin School of Medicine, 10 Medical Drive, Singapore, 117597, Singapore
| | - Naresh Kumar
- Department of Orthopaedic Surgery, University Spine Centre, National University Health System, 1E, Lower Kent Ridge Road, Singapore, 119228, Singapore
| | - Balamurugan A Vellayappan
- Department of Radiation Oncology, National University Cancer Institute Singapore, National University Hospital, Singapore, Singapore
| | - Beng Chin Ooi
- Department of Computer Science, School of Computing, National University of Singapore, 13 Computing Drive, Singapore, 117417, Singapore
| | - Swee Tian Quek
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore, 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore, 117597, Singapore
| | - Andrew Makmur
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore, 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore, 117597, Singapore
| | - Jiong Hao Tan
- Department of Orthopaedic Surgery, University Spine Centre, National University Health System, 1E, Lower Kent Ridge Road, Singapore, 119228, Singapore
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Dai Q, Tao Y, Liu D, Zhao C, Sui D, Xu J, Shi T, Leng X, Lu M. Ultrasound radiomics models based on multimodal imaging feature fusion of papillary thyroid carcinoma for predicting central lymph node metastasis. Front Oncol 2023; 13:1261080. [PMID: 38023240 PMCID: PMC10643192 DOI: 10.3389/fonc.2023.1261080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 10/09/2023] [Indexed: 12/01/2023] Open
Abstract
Objective This retrospective study aimed to establish ultrasound radiomics models to predict central lymph node metastasis (CLNM) based on preoperative multimodal ultrasound imaging features fusion of primary papillary thyroid carcinoma (PTC). Methods In total, 498 cases of unifocal PTC were randomly divided into two sets which comprised 348 cases (training set) and 150 cases (validition set). In addition, the testing set contained 120 cases of PTC at different times. Post-operative histopathology was the gold standard for CLNM. The following steps were used to build models: the regions of interest were segmented in PTC ultrasound images, multimodal ultrasound image features were then extracted by the deep learning residual neural network with 50-layer network, followed by feature selection and fusion; subsequently, classification was performed using three classical classifiers-adaptive boosting (AB), linear discriminant analysis (LDA), and support vector machine (SVM). The performances of the unimodal models (Unimodal-AB, Unimodal-LDA, and Unimodal-SVM) and the multimodal models (Multimodal-AB, Multimodal-LDA, and Multimodal-SVM) were evaluated and compared. Results The Multimodal-SVM model achieved the best predictive performance than the other models (P < 0.05). For the Multimodal-SVM model validation and testing sets, the areas under the receiver operating characteristic curves (AUCs) were 0.910 (95% CI, 0.894-0.926) and 0.851 (95% CI, 0.833-0.869), respectively. The AUCs of the Multimodal-SVM model were 0.920 (95% CI, 0.881-0.959) in the cN0 subgroup-1 cases and 0.828 (95% CI, 0.769-0.887) in the cN0 subgroup-2 cases. Conclusion The ultrasound radiomics model only based on the PTC multimodal ultrasound image have high clinical value in predicting CLNM and can provide a reference for treatment decisions.
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Affiliation(s)
- Quan Dai
- Department of Ultrasound, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Medicine & Laboratory of Translational Research in Ultrasound Theranostics, Chengdu, China
| | - Yi Tao
- Department of Ultrasound, West China Hospital of Sichuan University, Chengdu, China
| | - Dongmei Liu
- Department of Ultrasound, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Chen Zhao
- Department of Ultrasound, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Dong Sui
- State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China
- School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing, China
| | - Jinshun Xu
- Department of Ultrasound, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Medicine & Laboratory of Translational Research in Ultrasound Theranostics, Chengdu, China
| | - Tiefeng Shi
- Department of General Surgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Xiaoping Leng
- Department of Ultrasound, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Man Lu
- Department of Ultrasound, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Medicine & Laboratory of Translational Research in Ultrasound Theranostics, Chengdu, China
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Wang X, Zhou D, Kong Y, Cheng N, Gao M, Zhang G, Ma J, Chen Y, Ge S. Value of 18F-FDG-PET/CT radiomics combined with clinical variables in the differential diagnosis of malignant and benign vertebral compression fractures. EJNMMI Res 2023; 13:89. [PMID: 37819414 PMCID: PMC10567613 DOI: 10.1186/s13550-023-01038-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 09/20/2023] [Indexed: 10/13/2023] Open
Abstract
BACKGROUND Vertebral compression fractures (VCFs) are common clinical problems that arise from various reasons. The differential diagnosis of benign and malignant VCFs is challenging. This study was designed to develop and validate a radiomics model to predict benign and malignant VCFs with 18F-fluorodeoxyglucose-positron emission tomography/computed tomography (18F-FDG-PET/CT). RESULTS Twenty-six features (9 PET features and 17 CT features) and eight clinical variables (age, SUVmax, SUVpeak, SULmax, SULpeak, osteolytic destruction, fracture line, and appendices/posterior vertebrae involvement) were ultimately selected. The area under the curve (AUCs) of the radiomics and clinical-radiomics models were significantly different from that of the clinical model in both the training group (0.986, 0.987 vs. 0.884, p < 0.05) and test group (0.962, 0.948 vs. 0.858, p < 0.05), while there was no significant difference between the radiomics model and clinical-radiomics model (p > 0.05). The accuracies of the radiomics and clinical-radiomics models were 94.0% and 95.0% in the training group and 93.2% and 93.2% in the test group, respectively. The three models all showed good calibration (Hosmer-Lemeshow test, p > 0.05). According to the decision curve analysis (DCA), the radiomics model and clinical-radiomics model exhibited higher overall net benefit than the clinical model. CONCLUSIONS The PET/CT-based radiomics and clinical-radiomics models showed good performance in distinguishing between malignant and benign VCFs. The radiomics method may be valuable for treatment decision-making.
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Affiliation(s)
- Xun Wang
- Department of Medical Imaging, Affiliated Hospital of Jining Medical University, Guhuai Road, Jining, Shandong, China
| | - Dandan Zhou
- Big Data and Artificial Intelligence, Jining Polytechnic, Jinyu Road, Jining, Shandong, China
| | - Yu Kong
- Department of Medical Imaging, Affiliated Hospital of Jining Medical University, Guhuai Road, Jining, Shandong, China
| | - Nan Cheng
- Department of Medical Imaging, Affiliated Hospital of Jining Medical University, Guhuai Road, Jining, Shandong, China
| | - Ming Gao
- Department of Medical Imaging, Affiliated Hospital of Jining Medical University, Guhuai Road, Jining, Shandong, China
| | - Guqing Zhang
- Department of Medical Imaging, Affiliated Hospital of Jining Medical University, Guhuai Road, Jining, Shandong, China
| | - Junli Ma
- Department of Radiation Oncology, Affiliated Hospital of Jining Medical University, Guhuai Road, Jining, Shandong, China
| | - Yueqin Chen
- Department of Medical Imaging, Affiliated Hospital of Jining Medical University, Guhuai Road, Jining, Shandong, China.
| | - Shuang Ge
- Department of Radiation Oncology, Affiliated Hospital of Jining Medical University, Guhuai Road, Jining, Shandong, China.
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Shen L, Gao C, Hu S, Kang D, Zhang Z, Xia D, Xu Y, Xiang S, Zhu Q, Xu G, Tang F, Yue H, Yu W, Zhang Z. Using Artificial Intelligence to Diagnose Osteoporotic Vertebral Fractures on Plain Radiographs. J Bone Miner Res 2023; 38:1278-1287. [PMID: 37449775 DOI: 10.1002/jbmr.4879] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 06/18/2023] [Accepted: 07/06/2023] [Indexed: 07/18/2023]
Abstract
Osteoporotic vertebral fracture (OVF) is a risk factor for morbidity and mortality in elderly population, and accurate diagnosis is important for improving treatment outcomes. OVF diagnosis suffers from high misdiagnosis and underdiagnosis rates, as well as high workload. Deep learning methods applied to plain radiographs, a simple, fast, and inexpensive examination, might solve this problem. We developed and validated a deep-learning-based vertebral fracture diagnostic system using area loss ratio, which assisted a multitasking network to perform skeletal position detection and segmentation and identify and grade vertebral fractures. As the training set and internal validation set, we used 11,397 plain radiographs from six community centers in Shanghai. For the external validation set, 1276 participants were recruited from the outpatient clinic of the Shanghai Sixth People's Hospital (1276 plain radiographs). Radiologists performed all X-ray images and used the Genant semiquantitative tool for fracture diagnosis and grading as the ground truth data. Accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were used to evaluate diagnostic performance. The AI_OVF_SH system demonstrated high accuracy and computational speed in skeletal position detection and segmentation. In the internal validation set, the accuracy, sensitivity, and specificity with the AI_OVF_SH model were 97.41%, 84.08%, and 97.25%, respectively, for all fractures. The sensitivity and specificity for moderate fractures were 88.55% and 99.74%, respectively, and for severe fractures, they were 92.30% and 99.92%. In the external validation set, the accuracy, sensitivity, and specificity for all fractures were 96.85%, 83.35%, and 94.70%, respectively. For moderate fractures, the sensitivity and specificity were 85.61% and 99.85%, respectively, and 93.46% and 99.92% for severe fractures. Therefore, the AI_OVF_SH system is an efficient tool to assist radiologists and clinicians to improve the diagnosing of vertebral fractures. © 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).
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Affiliation(s)
- Li Shen
- Department of Osteoporosis and Bone Disease, Shanghai Clinical Research Center of Bone Disease, Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Clinical Research Center, Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Chao Gao
- Department of Osteoporosis and Bone Disease, Shanghai Clinical Research Center of Bone Disease, Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shundong Hu
- Department of Radiology, Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Dan Kang
- Shanghai Jiyinghui Intelligent Technology Co, Shanghai, China
| | - Zhaogang Zhang
- Shanghai Jiyinghui Intelligent Technology Co, Shanghai, China
| | - Dongdong Xia
- Department of Orthopaedics, Ning Bo First Hospital, Zhejiang, China
| | - Yiren Xu
- Department of Radiology, Ning Bo First Hospital, Zhejiang, China
| | - Shoukui Xiang
- Department of Endocrinology and Metabolism, The First People's Hospital of Changzhou, Changzhou, China
| | - Qiong Zhu
- Kangjian Community Health Service Center, Shanghai, China
| | - GeWen Xu
- Kangjian Community Health Service Center, Shanghai, China
| | - Feng Tang
- Jinhui Community Health Service Center, Shanghai, China
| | - Hua Yue
- Department of Osteoporosis and Bone Disease, Shanghai Clinical Research Center of Bone Disease, Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wei Yu
- Department of Radiology, Peking Union Medical College Hospital, Beijing, China
| | - Zhenlin Zhang
- Department of Osteoporosis and Bone Disease, Shanghai Clinical Research Center of Bone Disease, Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Clinical Research Center, Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
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15
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Chen Y, Xue J, Yan X, Fang DG, Li F, Tian X, Yan P, Feng Z. Identification of crucial genes related to heart failure based on GEO database. BMC Cardiovasc Disord 2023; 23:376. [PMID: 37507655 PMCID: PMC10385922 DOI: 10.1186/s12872-023-03400-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: 03/21/2023] [Accepted: 07/15/2023] [Indexed: 07/30/2023] Open
Abstract
BACKGROUND The molecular biological mechanisms underlying heart failure (HF) remain poorly understood. Therefore, it is imperative to use innovative approaches, such as high-throughput sequencing and artificial intelligence, to investigate the pathogenesis, diagnosis, and potential treatment of HF. METHODS First, we initially screened Two data sets (GSE3586 and GSE5406) from the GEO database containing HF and control samples from the GEO database to establish the Train group, and selected another dataset (GSE57345) to construct the Test group for verification. Next, we identified the genes with significantly different expression levels in patients with or without HF and performed functional and pathway enrichment analyses. HF-specific genes were identified, and an artificial neural network was constructed by Random Forest. The ROC curve was used to evaluate the accuracy and reliability of the constructed model in the Train and Test groups. Finally, immune cell infiltration was analyzed to determine the role of the inflammatory response and the immunological microenvironment in the pathogenesis of HF. RESULTS In the Train group, 153 significant differentially expressed genes (DEGs) associated with HF were found to be abnormal, including 81 down-regulated genes and 72 up-regulated genes. GO and KEGG enrichment analyses revealed that the down-regulated genes were primarily enriched in organic anion transport, neutrophil activation, and the PI3K-Akt signaling pathway. The upregulated genes were mainly enriched in neutrophil activation and the calcium signaling. DEGs were identified using Random Forest, and finally, 16 HF-specific genes were obtained. In the ROC validation and evaluation, the area under the curve (AUC) of the Train and Test groups were 0.996 and 0.863, respectively. CONCLUSIONS Our research revealed the potential functions and pathways implicated in the progression of HF, and designed an RNA diagnostic model for HF tissues using machine learning and artificial neural networks. Sensitivity, specificity, and stability were confirmed by ROC curves in the two different cohorts.
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Affiliation(s)
- Yongliang Chen
- Department of Cardiac Surgery, Affiliated Hospital of Chengde Medical University, 36 Nanyingzi Street, Chengde, Hebei, 067000, China
| | - Jing Xue
- Experimental Center of Morphology, College of Basic Medicine, Chengde Medical University, Chengde, Hebei, China
| | - Xiaoli Yan
- Experimental Center of Morphology, College of Basic Medicine, Chengde Medical University, Chengde, Hebei, China
| | - Da-Guang Fang
- Department of Cardiac Surgery, Affiliated Hospital of Chengde Medical University, 36 Nanyingzi Street, Chengde, Hebei, 067000, China
| | - Fangliang Li
- Experimental Center of Morphology, College of Basic Medicine, Chengde Medical University, Chengde, Hebei, China
| | - Xuefei Tian
- Department of Cardiac Surgery, Affiliated Hospital of Chengde Medical University, 36 Nanyingzi Street, Chengde, Hebei, 067000, China
| | - Peng Yan
- Experimental Center of Morphology, College of Basic Medicine, Chengde Medical University, Chengde, Hebei, China
| | - Zengbin Feng
- Department of Cardiac Surgery, Affiliated Hospital of Chengde Medical University, 36 Nanyingzi Street, Chengde, Hebei, 067000, China.
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16
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Ye P, Li S, Wang Z, Tian S, Luo Y, Wu Z, Zhuang Y, Zhang Y, Grzegorzek M, Hou Z. Development and validation of a deep learning-based model to distinguish acetabular fractures on pelvic anteroposterior radiographs. Front Physiol 2023; 14:1146910. [PMID: 37187961 PMCID: PMC10176114 DOI: 10.3389/fphys.2023.1146910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 04/12/2023] [Indexed: 05/17/2023] Open
Abstract
Objective: To develop and test a deep learning (DL) model to distinguish acetabular fractures (AFs) on pelvic anteroposterior radiographs (PARs) and compare its performance to that of clinicians. Materials and methods: A total of 1,120 patients from a big level-I trauma center were enrolled and allocated at a 3:1 ratio for the DL model's development and internal test. Another 86 patients from two independent hospitals were collected for external validation. A DL model for identifying AFs was constructed based on DenseNet. AFs were classified into types A, B, and C according to the three-column classification theory. Ten clinicians were recruited for AF detection. A potential misdiagnosed case (PMC) was defined based on clinicians' detection results. The detection performance of the clinicians and DL model were evaluated and compared. The detection performance of different subtypes using DL was assessed using the area under the receiver operating characteristic curve (AUC). Results: The means of 10 clinicians' sensitivity, specificity, and accuracy to identify AFs were 0.750/0.735, 0.909/0.909, and 0.829/0.822, in the internal test/external validation set, respectively. The sensitivity, specificity, and accuracy of the DL detection model were 0.926/0.872, 0.978/0.988, and 0.952/0.930, respectively. The DL model identified type A fractures with an AUC of 0.963 [95% confidence interval (CI): 0.927-0.985]/0.950 (95% CI: 0.867-0.989); type B fractures with an AUC of 0.991 (95% CI: 0.967-0.999)/0.989 (95% CI: 0.930-1.000); and type C fractures with an AUC of 1.000 (95% CI: 0.975-1.000)/1.000 (95% CI: 0.897-1.000) in the test/validation set. The DL model correctly recognized 56.5% (26/46) of PMCs. Conclusion: A DL model for distinguishing AFs on PARs is feasible. In this study, the DL model achieved a diagnostic performance comparable to or even superior to that of clinicians.
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Affiliation(s)
- Pengyu Ye
- Third Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Sihe Li
- University of Lübeck, Lübeck, Schleswig-Holstein, Germany
| | - Zhongzheng Wang
- Third Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Siyu Tian
- Third Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Yi Luo
- Heidelberg University, Heidelberg, Baden-Württemberg, Germany
| | - Zhanyong Wu
- Orthopedic Hospital of Xingtai, Xingtai, China
| | - Yan Zhuang
- Xi’an Honghui Hospital, Xi’an, Shaanxi, China
| | - Yingze Zhang
- Third Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | | | - Zhiyong Hou
- Third Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
- *Correspondence: Zhiyong Hou,
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17
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Radiomics and Deep Learning for Disease Detection in Musculoskeletal Radiology: An Overview of Novel MRI- and CT-Based Approaches. Invest Radiol 2023; 58:3-13. [PMID: 36070548 DOI: 10.1097/rli.0000000000000907] [Citation(s) in RCA: 26] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
ABSTRACT Radiomics and machine learning-based methods offer exciting opportunities for improving diagnostic performance and efficiency in musculoskeletal radiology for various tasks, including acute injuries, chronic conditions, spinal abnormalities, and neoplasms. While early radiomics-based methods were often limited to a smaller number of higher-order image feature extractions, applying machine learning-based analytic models, multifactorial correlations, and classifiers now permits big data processing and testing thousands of features to identify relevant markers. A growing number of novel deep learning-based methods describe magnetic resonance imaging- and computed tomography-based algorithms for diagnosing anterior cruciate ligament tears, meniscus tears, articular cartilage defects, rotator cuff tears, fractures, metastatic skeletal disease, and soft tissue tumors. Initial radiomics and deep learning techniques have focused on binary detection tasks, such as determining the presence or absence of a single abnormality and differentiation of benign versus malignant. Newer-generation algorithms aim to include practically relevant multiclass characterization of detected abnormalities, such as typing and malignancy grading of neoplasms. So-called delta-radiomics assess tumor features before and after treatment, with temporal changes of radiomics features serving as surrogate markers for tumor responses to treatment. New approaches also predict treatment success rates, surgical resection completeness, and recurrence risk. Practice-relevant goals for the next generation of algorithms include diagnostic whole-organ and advanced classification capabilities. Important research objectives to fill current knowledge gaps include well-designed research studies to understand how diagnostic performances and suggested efficiency gains of isolated research settings translate into routine daily clinical practice. This article summarizes current radiomics- and machine learning-based magnetic resonance imaging and computed tomography approaches for musculoskeletal disease detection and offers a perspective on future goals and objectives.
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18
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Zhang J, Liu F, Xu J, Zhao Q, Huang C, Yu Y, Yuan H. Automated detection and classification of acute vertebral body fractures using a convolutional neural network on computed tomography. Front Endocrinol (Lausanne) 2023; 14:1132725. [PMID: 37051194 PMCID: PMC10083489 DOI: 10.3389/fendo.2023.1132725] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Accepted: 03/14/2023] [Indexed: 03/29/2023] Open
Abstract
BACKGROUND Acute vertebral fracture is usually caused by low-energy injury with osteoporosis and high-energy trauma. The AOSpine thoracolumbar spine injury classification system (AO classification) plays an important role in the diagnosis and treatment of the disease. The diagnosis and description of vertebral fractures according to the classification scheme requires a great deal of time and energy for radiologists. PURPOSE To design and validate a multistage deep learning system (multistage AO system) for the automatic detection, localization and classification of acute thoracolumbar vertebral body fractures according to AO classification on computed tomography. MATERIALS AND METHODS The CT images of 1,217 patients who came to our hospital from January 2015 to December 2019 were collected retrospectively. The fractures were marked and classified by 2 junior radiology residents according to the type A standard in the AO classification. Marked fracture sites included the upper endplate, lower endplate and posterior wall. When there were inconsistent opinions on classification labels, the final result was determined by a director radiologist. We integrated different networks into different stages of the overall framework. U-net and a graph convolutional neural network (U-GCN) are used to realize the location and classification of the thoracolumbar spine. Next, a classification network is used to detect whether the thoracolumbar spine has a fracture. In the third stage, we detect fractures in different parts of the thoracolumbar spine by using a multibranch output network and finally obtain the AO types. RESULTS The mean age of the patients was 61.87 years with a standard deviation of 17.04 years, consisting of 760 female patients and 457 male patients. On vertebrae level, sensitivity for fracture detection was 95.23% in test dataset, with an accuracy of 97.93% and a specificity of 98.35%. For the classification of vertebral body fractures, the balanced accuracy was 79.56%, with an AUC of 0.904 for type A1, 0.945 for type A2, 0.878 for type A3 and 0.942 for type A4. CONCLUSION The multistage AO system can automatically detect and classify acute vertebral body fractures in the thoracolumbar spine on CT images according to AO classification with high accuracy.
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Affiliation(s)
- Jianlun Zhang
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | | | | | - Qingqing Zhao
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | | | | | - Huishu Yuan
- Department of Radiology, Peking University Third Hospital, Beijing, China
- *Correspondence: Huishu Yuan,
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19
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A Classification Method for Thoracolumbar Vertebral Fractures due to Basketball Sports Injury Based on Deep Learning. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:8747487. [PMID: 36245837 PMCID: PMC9556197 DOI: 10.1155/2022/8747487] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/06/2022] [Revised: 08/30/2022] [Accepted: 09/15/2022] [Indexed: 11/17/2022]
Abstract
Objective There are more and more basketball competitions, to propose a classification method of thoracolumbar fractures to assist in the diagnosis of basketball injuries, to analyze the feasibility of its clinical application, and to improve the recovery rate. Methods From February 2015 to May 2022, 1130 CT images of thoracolumbar fractures admitted to our hospital and affiliated hospital units due to basketball injuries were collected, and the image labeling system uniformly labeled them. All CT images were classified according to the AO spine classification of thoracolumbar injuries. In the ABC-type classification, 935 CT images were used for training and validation to optimize the deep learning system, including 815 training sets and 120 validation sets; the remaining 198 CT images were used as test sets for comparing the deep learning system and clinician's diagnosis. In the classification of subtype A, a total of 523 CT scans can be performed for training and validation to optimize the deep learning system, including 500 training sets and 23 validation sets; the remaining 94 CT images are used as test sets for comparing depth learning systems and clinicians' diagnostic results. Results The deep learning system had a correct rate of ABC classification of fractures in 86.4%, with a kappa coefficient of 0.850 (P < 0.001); the correct rate of subtype A was 85.3%, with a kappa coefficient of 0.815 (P < 0.001). Conclusion The classification accuracy of thoracolumbar fractures based on deep learning is high. The method can assist in diagnosing CT images of thoracolumbar fractures and improve the current manual and complex diagnosis process.
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20
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Aparisi Gómez MP, Isaac A, Dalili D, Fotiadou A, Kariki EP, Kirschke JS, Krestan CR, Messina C, Oei EHG, Phan CM, Prakash M, Sabir N, Tagliafico A, Aparisi F, Baum T, Link TM, Guglielmi G, Bazzocchi A. Imaging of Metabolic Bone Diseases: The Spine View, Part II. Semin Musculoskelet Radiol 2022; 26:491-500. [PMID: 36103890 DOI: 10.1055/s-0042-1754341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
Abstract
Metabolic bone diseases comprise a wide spectrum. Osteoporosis, the most frequent, characteristically involves the spine, with a high impact on health care systems and on the morbidity of patients due to the occurrence of vertebral fractures (VFs).Part II of this review completes an overview of state-of-the-art techniques on the imaging of metabolic bone diseases of the spine, focusing on specific populations and future perspectives. We address the relevance of diagnosis and current status on VF assessment and quantification. We also analyze the diagnostic techniques in the pediatric population and then review the assessment of body composition around the spine and its potential application. We conclude with a discussion of the future of osteoporosis screening, through opportunistic diagnosis and the application of artificial intelligence.
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Affiliation(s)
- Maria Pilar Aparisi Gómez
- Department of Radiology, Auckland City Hospital, Auckland, New Zealand.,Department of Radiology, IMSKE, Valencia, Spain
| | - Amanda Isaac
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Danoob Dalili
- Academic Surgical Unit, South West London Elective Orthopaedic Centre (SWLEOC), Epsom, London, United Kingdom.,Department of Diagnostic and Interventional Radiology, Epsom and St. Helier University Hospitals NHS Trust, London, United Kingdom
| | - Anastasia Fotiadou
- Consultant Radiologist, Royal National Orthopaedic Hospital, Stanmore, United Kingdom
| | - Eleni P Kariki
- Manchester University NHS Foundation Trust, Manchester, United Kingdom.,Division of Informatics, Imaging & Data Sciences, School of Health Sciences, The University of Manchester, Manchester, United Kingdom
| | - Jan S Kirschke
- Interventional und Diagnostic Neuroradiology, School of Medicine, Technical University Munich, Munich, Germany
| | | | | | - Edwin H G Oei
- Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - Catherine M Phan
- Service de Radiologie Ostéo-Articulaire, APHP, Nord-Université de Paris, Hôpital Lariboisière, Paris, France
| | - Mahesh Prakash
- Department of Radiodiagnosis & Imaging, PGIMER, Chandigarh, India
| | - Nuran Sabir
- Department of Radiology, Pamukkale University School of Medicine, Denizli, Turkey
| | - Alberto Tagliafico
- DISSAL, University of Genova, Genova, Italy.,Ospedale Policlinico San Martino, Genova, Italy
| | - Francisco Aparisi
- Department of Radiology, Hospital Vithas Nueve de Octubre, Valencia, Spain
| | - Thomas Baum
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Thomas M Link
- Department of Radiology and Biomedical Imaging, University of California at San Francisco, San Francisco, California
| | | | - Alberto Bazzocchi
- Diagnostic and Interventional Radiology, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy
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21
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Sha G, Wu J, Yu B. The Shrank YoloV3-tiny for spinal fracture lesions detection. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-212255] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Purpose: at present, more and more deep learning algorithms are used to detect and segment lesions from spinal CT (Computed Tomography) images. But these algorithms usually require computers with high performance and occupy large resources, so they are not suitable for the clinical embedded and mobile devices, which only have limited computational resources and also expect a relative good performance in detecting and segmenting lesions. Methods: in this paper, we present a model based on Yolov3-tiny to detect three spinal fracture lesions, cfracture (cervical fracture), tfracture (thoracic fracture), and lfracture (lumbar fracture) with a small size model. We construct this novel model by replacing the traditional convolutional layers in YoloV3-tiny with fire modules from SqueezeNet, so as to reduce the parameters and model size, meanwhile get accurate lesions detection. Then we remove the batch normalization layers in the fire modules after the comparative experiments, though the overall performance of fire module without batch normalization layers is slightly improved, we can reduce computation complexity and low occupations of computer resources for fast lesions detection. Results: the experiments show that the shrank model only has a size of 13 MB (almost a third of Yolov3-tiny), while the mAP (mean Average Precsion) is 91.3%, and IOU (intersection over union) is 90.7. The detection time is 0.015 second per CT image, and BFLOP/s (Billion Floating Point Operations per Second) value is less than Yolov3-tiny. Conclusion: the model we presented can be deployed in clinical embedded and mobile devices, meanwhile has a relative accurate and rapid real-time lesions detection.
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Affiliation(s)
- Gang Sha
- School of Computer Science, Northwestern Polytechnical University, Xi’an, P. R. China
| | - Junsheng Wu
- School of Software & Microelectronics, Northwestern Polytechnical University, Xi’an, P. R. China
| | - Bin Yu
- School of Computer Science and Technology, Xidian University, Xi an 710071, P. R. China
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