|
1
|
Han XL, Shi XL, Li QY, Shao YJ, Gao CP. Paravertebral soft tissue swelling on magnetic resonance images helps in differentiation between osteoporotic and malignant vertebral fractures. World J Clin Cases 2025; 13:103627. [DOI: 10.12998/wjcc.v13.i20.103627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2024] [Revised: 02/23/2025] [Accepted: 03/08/2025] [Indexed: 04/09/2025] Open
Abstract
BACKGROUND Osteoporotic vertebral fracture (OVF) is one of the most common sequelae of osteoporosis. Differential diagnosis between OVF and malignant vertebral fracture (MVF) is a challenge in clinical practice. Findings on computed tomography and magnetic resonance images (MRI) may help to differentiate between these two types of fracture.
AIM To determine whether paravertebral soft tissue swelling is useful for differentiation between OVF and MVF.
METHODS We retrospectively reviewed the MRI for 165 patients diagnosed with a vertebral fracture between May 2021 and July 2022. Three radiologists evaluated the vertebral segments and thickness of soft tissue swelling on sagittal MRI by consensus. The morphology of the soft tissue swelling was also evaluated. The statistical analyses were performed using the χ2 test and analysis of variance.
RESULTS The study included 117 patients (153 vertebrae) with OVF and 48 patients (63 vertebrae) with MVF. Soft tissue swelling was observed beneath the anterior longitudinal ligament on sagittal MRI and rim-shaped in the paravertebral area on axial MRI in all 153 vertebrae with OVF (100%) and in 12 (19%) of the 63 vertebra with MVF; the difference was statistically significant (P < 0.001), 95%CI: 3.156–8.735. Soft tissue swelling beneath the anterior longitudinal ligament spanned significantly more vertebral segments in patients with OVF than in those with MVF (P < 0.001), 95%CI: 0.932-1.546. The mean thickness of the soft tissue swelling was significantly greater for OVF than for MVF (5.62 mm ± 2.50 mm vs 3.88 mm ± 1.73 mm, P < 0.05, 95%CI: 0.681–0.920). Post-contrast examination was performed in 13 patients; T1-weighted images confirmed OVF in 11 cases and MVF in 2 cases. Soft tissue swelling in OVF and MVF had a fusiform appearance or appeared as a thin line on sagittal MRI and was rim-shaped on axial MRI. The length and diameter of the soft tissue swelling in patients with OVF decreased during follow-up.
CONCLUSION Paravertebral soft tissue swelling is helpful for differentiating between OVF and MVF.
Collapse
Affiliation(s)
- Xiao-Lin Han
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao 266003, Shandong Province, China
| | - Xiang-Long Shi
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao 266003, Shandong Province, China
| | - Qi-Yuan Li
- Department of Radiology, The Cangzhou Central Hospital, Cangzhou 061001, Hebei Province, China
| | - Ya-Jing Shao
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao 266003, Shandong Province, China
| | - Chuan-Ping Gao
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao 266003, Shandong Province, China
| |
Collapse
|
|
2
|
Chen YS, Liu PC, Chang CC, Tu TH, Kuo CH. Clinical Oversight and Delayed Diagnosis of a Pathological Compression Fracture Causing Paraplegia. Cureus 2024; 16:e68296. [PMID: 39350874 PMCID: PMC11441844 DOI: 10.7759/cureus.68296] [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] [Accepted: 08/31/2024] [Indexed: 10/04/2024] Open
Abstract
While osteoporosis is the primary cause of vertebral compression fractures (VCFs), it's crucial to promptly recognize pathological fractures through comprehensive diagnostic tests, including vertebral biopsies, to determine the exact etiology. For instance, a 66-year-old male with osteoporosis experienced worsening lower limb weakness and back pain after an initial vertebroplasty for a T12 compression fracture. Subsequent MRI revealed severe circumferential extradural compression at T12, leading to further surgeries that eventually uncovered metastatic adenocarcinoma from a pancreatic tumor. This case highlights the importance of precise diagnosis through vertebral biopsy and the necessity of sufficient ventral decompression or corpectomy, coupled with extensive laminectomy, to address severe neurological impairments like paraplegia. Prompt and accurate interventions can significantly improve patient outcomes and quality of life.
Collapse
Affiliation(s)
- Yin-Sheng Chen
- Department of Neurosurgery, Taipei Veterans General Hospital, Taipei, TWN
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, TWN
| | - Ping-Chuan Liu
- Department of Neurosurgery, Taipei Veterans General Hospital, Taipei, TWN
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, TWN
| | - Chih-Chang Chang
- Department of Neurosurgery, Taipei Veterans General Hospital, Taipei, TWN
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, TWN
- Department of Biomedical Engineering, School of Biomedical Science and Engineering, National Yang Ming Chiao Tung University, Taipei, TWN
| | - Tsung-Hsi Tu
- Department of Neurosurgery, Taipei Veterans General Hospital, Taipei, TWN
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, TWN
| | - Chao-Hung Kuo
- Department of Neurosurgery, Taipei Veterans General Hospital, Taipei, TWN
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, TWN
- Department of Biomedical Engineering, School of Biomedical Science and Engineering, National Yang Ming Chiao Tung University, Taipei, TWN
- Ph.D. Program in Medical Neuroscience, College of Medical Science and Technology, Taipei Medical University, New Taipei City, TWN
| |
Collapse
|
|
3
|
Yildirim O, Peck KK, Saha A, Karimi S, Lis E. Dynamic Contrast Enhanced MR Perfusion and Diffusion-Weighted Imaging of Marrow-Replacing Disorders of the Spine: A Comprehensive Review. Radiol Clin North Am 2024; 62:287-302. [PMID: 38272621 DOI: 10.1016/j.rcl.2023.09.004] [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: 01/27/2024]
Abstract
Significant advancements in cancer treatment have led to improved survival rates for patients, particularly in the context of spinal metastases. However, early detection and monitoring of treatment response remain crucial for optimizing patient outcomes. Although conventional imaging methods such as bone scan, PET, MR imaging, and computed tomography are commonly used for diagnosing and monitoring treatment, they present challenges in differential diagnoses and treatment response monitoring. This review article provides a comprehensive overview of the principles, applications, and practical uses of dynamic contrast-enhanced MR imaging and diffusion-weighted imaging in the assessment and monitoring of marrow-replacing disorders of the spine.
Collapse
Affiliation(s)
- Onur Yildirim
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA.
| | | | - Atin Saha
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA
| | - Sasan Karimi
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA
| | - Eric Lis
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA
| |
Collapse
|
|
4
|
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: 3] [Impact Index Per Article: 3.0] [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.
Collapse
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.)
| |
Collapse
|
|
5
|
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.
Collapse
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.)
| |
Collapse
|
|
6
|
Fatima K, Naik S, Jain M, Bhoi SK, Padhi S, Bag ND, Panigrahi A, Mohakud S. Diffusion-Weighted Imaging and Chemical Shift Imaging to Differentiate Benign and Malignant Vertebral Lesion: A Hospital-Based Cross-Sectional Study. Indian J Radiol Imaging 2024; 34:76-84. [PMID: 38106853 PMCID: PMC10723945 DOI: 10.1055/s-0043-1772848] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2023] Open
Abstract
Objective The aim of this study was to evaluate the role of diffusion-weighted imaging (DWI) and chemical shift imaging (CSI) for the differentiation of benign and malignant vertebral lesions. Methods Patients with vertebral lesions underwent routine magnetic resonance imaging (MRI) along with DWI and CSI. Qualitative analysis of the morphological features was done by routine MRI. Quantitative analysis of apparent diffusion coefficient (ADC) from DWI and fat fraction (FF) from CSI was done and compared between benign and malignant vertebral lesions. Results Seventy-two patients were included. No significant difference was noted in signal intensities of benign and malignant lesions on conventional MRI sequences. Posterior element involvement, paravertebral soft-tissue lesion, and posterior vertebral bulge were common in malignant lesion, whereas epidural/paravertebral collection, absence of posterior vertebral bulge, and multiple compression fractures were common in benign vertebral lesion ( p < 0.001). The mean ADC value was 1.25 ± 0.27 mm 2 /s for benign lesions and 0.9 ± 0.19 mm 2 /s for malignant vertebral lesions ( p ≤ 0.001). The mean value of FF was 12.7 ± 7.49 for the benign group and 4.04 ± 2.6 for the malignant group ( p < 0.001). A receiver operating characteristic (ROC) curve analysis showed that an ADC cutoff of 1.05 × 10 -3 mm 2 /s and an FF cutoff of 6.9 can differentiate benign from malignant vertebral lesions, with the former having 86% sensitivity and 82.8% specificity and the latter having 93% sensitivity and 96.6% specificity. Conclusion The addition of DWI and CSI to routine MRI protocol in patients with vertebral lesions promises to be very helpful in differentiating benign from malignant vertebral lesions when difficulty in qualitative interpretation of conventional MR images arises.
Collapse
Affiliation(s)
- Kaneez Fatima
- Department of Radiodiagnosis, All India Institute of Medical Sciences, Bhubaneswar, Odisha, India
| | - Suprava Naik
- Department of Radiodiagnosis, All India Institute of Medical Sciences, Bhubaneswar, Odisha, India
| | - Mantu Jain
- Department of Orthopaedics, All India Institute of Medical Sciences, Bhubaneswar, Odisha, India
| | - Sanjeev Kumar Bhoi
- Department of Neurology, All India Institute of Medical Sciences, Bhubaneswar, Odisha, India
| | - Somnath Padhi
- Department of Pathology, All India Institute of Medical Sciences, Bhubaneswar, Odisha, India
| | - Nerbadyswari Deep Bag
- Department of Radiodiagnosis, All India Institute of Medical Sciences, Bhubaneswar, Odisha, India
| | - Ashutosh Panigrahi
- Department of Haematology, All India Institute of Medical Sciences, Bhubaneswar, Odisha, India
| | - Sudipta Mohakud
- Department of Radiodiagnosis, All India Institute of Medical Sciences, Bhubaneswar, Odisha, India
| |
Collapse
|
|
7
|
Goller SS, Foreman SC, Rischewski JF, Weißinger J, Dietrich AS, Schinz D, Stahl R, Luitjens J, Siller S, Schmidt VF, Erber B, Ricke J, Liebig T, Kirschke JS, Dieckmeyer M, Gersing AS. Differentiation of benign and malignant vertebral fractures using a convolutional neural network to extract CT-based texture features. 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; 32:4314-4320. [PMID: 37401945 DOI: 10.1007/s00586-023-07838-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 05/25/2023] [Accepted: 06/20/2023] [Indexed: 07/05/2023]
Abstract
PURPOSE To assess the diagnostic performance of three-dimensional (3D) CT-based texture features (TFs) using a convolutional neural network (CNN)-based framework to differentiate benign (osteoporotic) and malignant vertebral fractures (VFs). METHODS A total of 409 patients who underwent routine thoracolumbar spine CT at two institutions were included. VFs were categorized as benign or malignant using either biopsy or imaging follow-up of at least three months as standard of reference. Automated detection, labelling, and segmentation of the vertebrae were performed using a CNN-based framework ( https://anduin.bonescreen.de ). Eight TFs were extracted: Varianceglobal, Skewnessglobal, energy, entropy, short-run emphasis (SRE), long-run emphasis (LRE), run-length non-uniformity (RLN), and run percentage (RP). Multivariate regression models adjusted for age and sex were used to compare TFs between benign and malignant VFs. RESULTS Skewnessglobal showed a significant difference between the two groups when analyzing fractured vertebrae from T1 to L6 (benign fracture group: 0.70 [0.64-0.76]; malignant fracture group: 0.59 [0.56-0.63]; and p = 0.017), suggesting a higher skewness in benign VFs compared to malignant VFs. CONCLUSION Three-dimensional CT-based global TF skewness assessed using a CNN-based framework showed significant difference between benign and malignant thoracolumbar VFs and may therefore contribute to the clinical diagnostic work-up of patients with VFs.
Collapse
Affiliation(s)
- Sophia S Goller
- Department of Radiology, University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany.
| | - Sarah C Foreman
- Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Jon F Rischewski
- Institute of Neuroradiology, University Hospital, LMU Munich, Munich, Germany
| | - Jürgen Weißinger
- Institute of Neuroradiology, University Hospital, LMU Munich, Munich, Germany
| | - Anna-Sophia Dietrich
- Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - David Schinz
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Robert Stahl
- Institute of Neuroradiology, University Hospital, LMU Munich, Munich, Germany
| | - Johanna Luitjens
- Department of Radiology, University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA
| | - Sebastian Siller
- Department of Neurosurgery, University Hospital, LMU Munich, Munich, Germany
| | - Vanessa F Schmidt
- Department of Radiology, University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany
| | - Bernd Erber
- Department of Radiology, University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany
| | - Jens Ricke
- Department of Radiology, University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany
| | - Thomas Liebig
- Institute of Neuroradiology, University Hospital, LMU Munich, Munich, Germany
| | - Jan S Kirschke
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Michael Dieckmeyer
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, University Hospital, University of Bern, Bern, Switzerland
| | - Alexandra S Gersing
- Institute of Neuroradiology, University Hospital, LMU Munich, Munich, Germany
| |
Collapse
|
|
8
|
Shah AJ, BT P. Neoplastic and Non-Neoplastic Vertebral Marrow Pathologies: Can the Conventional and Advanced MRI Sequences Provide a Definitive Answer? Indian J Radiol Imaging 2023; 33:438-439. [PMID: 37811169 PMCID: PMC10556313 DOI: 10.1055/s-0043-1775571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/10/2023] Open
Affiliation(s)
- Ankur J. Shah
- Department of Radiology, Sadbhav Imaging Centre, Ahmedabad, Gujarat, India
| | - Pushpa BT
- Department of Radiology, Ganga Hospital, Coimbatore, India
| |
Collapse
|
|
9
|
Saha A, Peck KK, Karimi S, Lis E, Holodny AI. Dynamic Contrast-Enhanced MR Perfusion: Role in Diagnosis and Treatment Follow-Up in Patients with Vertebral Body Tumors. Neuroimaging Clin N Am 2023; 33:477-486. [PMID: 37356863 DOI: 10.1016/j.nic.2023.03.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/27/2023]
Abstract
Recent therapeutic advances have led to increased survival times for patients with metastatic disease. Key to survival is early diagnosis and subsequent treatment as well as early detection of treatment failure allowing for therapy modifications. Conventional MR imaging techniques of the spine can be at times suboptimal for identifying viable tumor, as structural changes and imaging characteristics may not differ pretreatment and posttreatment. Advanced imaging techniques such as DCE-MRI can allow earlier and more accurate noninvasive assessment of viable disease by characterizing physiologic changes and tumor microvasculature.
Collapse
Affiliation(s)
- Atin Saha
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA; Department of Radiology, Weill Cornell Medical College, 1300 York Avenue, New York, NY 10065, USA.
| | - Kyung K Peck
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA
| | - Sasan Karimi
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA; Department of Radiology, Weill Cornell Medical College, 1300 York Avenue, New York, NY 10065, USA
| | - Eric Lis
- Department of Radiology, Weill Cornell Medical College, 1300 York Avenue, New York, NY 10065, USA
| | - Andrei I Holodny
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA. https://twitter.com/AndreiHolodny
| |
Collapse
|
|
10
|
Zhang H, Yuan G, Wang C, Zhao H, Zhu K, Guo J, Chen M, Liu H, Yang G, Wang Y, Ma X. Differentiation of benign versus malignant indistinguishable vertebral compression fractures by different machine learning with MRI-based radiomic features. Eur Radiol 2023:10.1007/s00330-023-09678-x. [PMID: 37099176 DOI: 10.1007/s00330-023-09678-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 02/06/2023] [Accepted: 02/22/2023] [Indexed: 04/27/2023]
Abstract
OBJECTIVES To explore an optimal machine learning (ML) model trained on MRI-based radiomic features to differentiate benign from malignant indistinguishable vertebral compression fractures (VCFs). METHODS This retrospective study included patients within 6 weeks of back pain (non-traumatic) who underwent MRI and were diagnosed with benign and malignant indistinguishable VCFs. The two cohorts were retrospectively recruited from the Affiliated Hospital of Qingdao University (QUH) and Qinghai Red Cross Hospital (QRCH). Three hundred seventy-six participants from QUH were divided into the training (n = 263) and validation (n = 113) cohort based on the date of MRI examination. One hundred three participants from QRCH were used to evaluate the external generalizability of our prediction models. A total of 1045 radiomic features were extracted from each region of interest (ROI) and used to establish the models. The prediction models were established based on 7 different classifiers. RESULTS These models showed favorable efficacy in differentiating benign from malignant indistinguishable VCFs. However, our Gaussian naïve Bayes (GNB) model attained higher AUC and accuracy (0.86, 87.61%) than the other classifiers in validation cohort. It also remains the high accuracy and sensitivity for the external test cohort. CONCLUSIONS Our GNB model performed better than the other models in the present study, suggesting that it may be more useful for differentiating indistinguishable benign form malignant VCFs. KEY POINTS • The differential diagnosis of benign and malignant indistinguishable VCFs based on MRI is rather difficult for spine surgeons or radiologists. • Our ML models facilitate the differential diagnosis of benign and malignant indistinguishable VCFs with improved diagnostic efficacy. • Our GNB model had the high accuracy and sensitivity for clinical application.
Collapse
Affiliation(s)
- Hao Zhang
- Department of Spinal Surgery, The Affiliated Hospital of Qingdao University, No. 59 Haier Road, Qingdao, 266000, Shandong, China
| | - Genji Yuan
- College of Computer Science and Technology, Qingdao University, Qingdao, Shandong, China
| | - Chao Wang
- Department of Spinal Surgery, The Affiliated Hospital of Qingdao University, No. 59 Haier Road, Qingdao, 266000, Shandong, China
| | - Hongshun Zhao
- Department of Spinal Surgery, Qinghai Red Cross Hospital, Xining, Qinghai, China
| | - Kai Zhu
- Department of Spinal Surgery, The Affiliated Hospital of Qingdao University, No. 59 Haier Road, Qingdao, 266000, Shandong, China
| | - Jianwei Guo
- Department of Spinal Surgery, The Affiliated Hospital of Qingdao University, No. 59 Haier Road, Qingdao, 266000, Shandong, China
| | - Mingrui Chen
- Department of Spinal Surgery, The Affiliated Hospital of Qingdao University, No. 59 Haier Road, Qingdao, 266000, Shandong, China
| | - Houchen Liu
- Department of Spinal Surgery, The Affiliated Hospital of Qingdao University, No. 59 Haier Road, Qingdao, 266000, Shandong, China
| | - Guangjie Yang
- Department of Nuclear Medicine, The Affiliated Hospital of Qingdao University, No. 59 Haier Road, Qingdao, 266000, Shandong, China.
| | - Yan Wang
- Department of Spinal Surgery, The Affiliated Hospital of Qingdao University, No. 59 Haier Road, Qingdao, 266000, Shandong, China.
| | - Xuexiao Ma
- Department of Spinal Surgery, The Affiliated Hospital of Qingdao University, No. 59 Haier Road, Qingdao, 266000, Shandong, China.
| |
Collapse
|
|
11
|
Ong W, Zhu L, Tan YL, Teo EC, Tan JH, Kumar N, Vellayappan BA, Ooi BC, Quek ST, Makmur A, Hallinan JTPD. Application of Machine Learning for Differentiating Bone Malignancy on Imaging: A Systematic Review. Cancers (Basel) 2023; 15:cancers15061837. [PMID: 36980722 PMCID: PMC10047175 DOI: 10.3390/cancers15061837] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 03/07/2023] [Accepted: 03/16/2023] [Indexed: 03/22/2023] Open
Abstract
An accurate diagnosis of bone tumours on imaging is crucial for appropriate and successful treatment. The advent of Artificial intelligence (AI) and machine learning methods to characterize and assess bone tumours on various imaging modalities may assist in the diagnostic workflow. The purpose of this review article is to summarise the most recent evidence for AI techniques using imaging for differentiating benign from malignant lesions, the characterization of various malignant bone lesions, and their potential clinical application. A systematic search through electronic databases (PubMed, MEDLINE, Web of Science, and clinicaltrials.gov) was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 34 articles were retrieved from the databases and the key findings were compiled and summarised. A total of 34 articles reported the use of AI techniques to distinguish between benign vs. malignant bone lesions, of which 12 (35.3%) focused on radiographs, 12 (35.3%) on MRI, 5 (14.7%) on CT and 5 (14.7%) on PET/CT. The overall reported accuracy, sensitivity, and specificity of AI in distinguishing between benign vs. malignant bone lesions ranges from 0.44–0.99, 0.63–1.00, and 0.73–0.96, respectively, with AUCs of 0.73–0.96. In conclusion, the use of AI to discriminate bone lesions on imaging has achieved a relatively good performance in various imaging modalities, with high sensitivity, specificity, and accuracy for distinguishing between benign vs. malignant lesions in several cohort studies. However, further research is necessary to test the clinical performance of these algorithms before they can be facilitated and integrated into routine clinical practice.
Collapse
Affiliation(s)
- Wilson Ong
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
- Correspondence: ; Tel.: +65-67725207
| | - Lei Zhu
- Department of Computer Science, School of Computing, National University of Singapore, 13 Computing Drive, Singapore 117417, Singapore
| | - Yi Liang Tan
- 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
| | - Jiong Hao Tan
- University Spine Centre, Department of Orthopaedic Surgery, National University Health System, 1E, Lower Kent Ridge Road, Singapore 119228, Singapore
| | - Naresh Kumar
- University Spine Centre, Department of Orthopaedic Surgery, 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, 5 Lower Kent Ridge Road, Singapore 119074, 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
| | - 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
| |
Collapse
|
|
12
|
Kim AY, Yoon MA, Ham SJ, Cho YC, Ko Y, Park B, Kim S, Lee E, Lee RW, Chee CG, Lee MH, Lee SH, Chung HW. Prediction of the Acuity of Vertebral Compression Fractures on CT Using Radiologic and Radiomic Features. Acad Radiol 2022; 29:1512-1520. [PMID: 34998683 DOI: 10.1016/j.acra.2021.12.008] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 12/08/2021] [Accepted: 12/08/2021] [Indexed: 12/14/2022]
Abstract
RATIONALE AND OBJECTIVES To develop and validate prediction models to differentiate acute and chronic vertebral compression fractures based on radiologic and radiomic features on CT. MATERIALS AND METHODS This study included acute and chronic compression fractures in patients who underwent both spine CT and MRI examinations. For each fractured vertebra, three CT findings ([1] cortical disruption, [2] hypoattenuating cleft or sclerotic line, and [3] relative bone marrow attenuation) were assessed by two radiologists. A radiomic score was built from 280 radiomic features extracted from non-contrast-enhanced CT images. Weighted multivariable logistic regression analysis was performed to build a radiologic model based on CT findings and an integrated model combining the radiomic score and CT findings. Model performance was evaluated and compared. Models were externally validated using an independent test cohort. RESULTS A total to 238 fractures (159 acute and 79 chronic) in 122 patients and 58 fractures (39 acute and 19 chronic) in 32 patients were included in the training and test cohorts, respectively. The AUC of the radiomic score was 0.95 in the training and 0.93 in the test cohorts. The AUC of the radiologic model was 0.89 in the training and 0.83 in the test cohorts. The discriminatory performance of the integrated model was significantly higher than the radiologic model in both the training (AUC, 0.97; p<0.01) and the test (AUC, 0.95; p=0.01) cohorts. CONCLUSION Combining radiomics with radiologic findings significantly improved the performance of CT in determining the acuity of vertebral compression fractures.
Collapse
Affiliation(s)
- A Yeon Kim
- Department of Radiology and Research Institute of Radiology (A.Y.K., M.A.Y., S.J.H., C.G.C., M.H.L., S.H.L., H.W.C.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Korea; Biomedical Research Center (Y.C.C., Y.K.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Health Innovation Big Data Center (B.P.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Clinical Epidemiology and Biostatistics (S.K.), Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Radiology (E.L.), Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea; Department of Radiology (R.W.L.), Inha University Hospital, Jung-gu, Incheon, Korea
| | - Min A Yoon
- Department of Radiology and Research Institute of Radiology (A.Y.K., M.A.Y., S.J.H., C.G.C., M.H.L., S.H.L., H.W.C.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Korea; Biomedical Research Center (Y.C.C., Y.K.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Health Innovation Big Data Center (B.P.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Clinical Epidemiology and Biostatistics (S.K.), Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Radiology (E.L.), Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea; Department of Radiology (R.W.L.), Inha University Hospital, Jung-gu, Incheon, Korea.
| | - Su Jung Ham
- Department of Radiology and Research Institute of Radiology (A.Y.K., M.A.Y., S.J.H., C.G.C., M.H.L., S.H.L., H.W.C.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Korea; Biomedical Research Center (Y.C.C., Y.K.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Health Innovation Big Data Center (B.P.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Clinical Epidemiology and Biostatistics (S.K.), Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Radiology (E.L.), Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea; Department of Radiology (R.W.L.), Inha University Hospital, Jung-gu, Incheon, Korea
| | - Young Chul Cho
- Department of Radiology and Research Institute of Radiology (A.Y.K., M.A.Y., S.J.H., C.G.C., M.H.L., S.H.L., H.W.C.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Korea; Biomedical Research Center (Y.C.C., Y.K.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Health Innovation Big Data Center (B.P.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Clinical Epidemiology and Biostatistics (S.K.), Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Radiology (E.L.), Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea; Department of Radiology (R.W.L.), Inha University Hospital, Jung-gu, Incheon, Korea
| | - Yousun Ko
- Department of Radiology and Research Institute of Radiology (A.Y.K., M.A.Y., S.J.H., C.G.C., M.H.L., S.H.L., H.W.C.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Korea; Biomedical Research Center (Y.C.C., Y.K.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Health Innovation Big Data Center (B.P.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Clinical Epidemiology and Biostatistics (S.K.), Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Radiology (E.L.), Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea; Department of Radiology (R.W.L.), Inha University Hospital, Jung-gu, Incheon, Korea
| | - Bumwoo Park
- Department of Radiology and Research Institute of Radiology (A.Y.K., M.A.Y., S.J.H., C.G.C., M.H.L., S.H.L., H.W.C.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Korea; Biomedical Research Center (Y.C.C., Y.K.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Health Innovation Big Data Center (B.P.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Clinical Epidemiology and Biostatistics (S.K.), Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Radiology (E.L.), Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea; Department of Radiology (R.W.L.), Inha University Hospital, Jung-gu, Incheon, Korea
| | - Seonok Kim
- Department of Radiology and Research Institute of Radiology (A.Y.K., M.A.Y., S.J.H., C.G.C., M.H.L., S.H.L., H.W.C.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Korea; Biomedical Research Center (Y.C.C., Y.K.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Health Innovation Big Data Center (B.P.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Clinical Epidemiology and Biostatistics (S.K.), Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Radiology (E.L.), Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea; Department of Radiology (R.W.L.), Inha University Hospital, Jung-gu, Incheon, Korea
| | - Eugene Lee
- Department of Radiology and Research Institute of Radiology (A.Y.K., M.A.Y., S.J.H., C.G.C., M.H.L., S.H.L., H.W.C.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Korea; Biomedical Research Center (Y.C.C., Y.K.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Health Innovation Big Data Center (B.P.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Clinical Epidemiology and Biostatistics (S.K.), Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Radiology (E.L.), Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea; Department of Radiology (R.W.L.), Inha University Hospital, Jung-gu, Incheon, Korea
| | - Ro Woon Lee
- Department of Radiology and Research Institute of Radiology (A.Y.K., M.A.Y., S.J.H., C.G.C., M.H.L., S.H.L., H.W.C.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Korea; Biomedical Research Center (Y.C.C., Y.K.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Health Innovation Big Data Center (B.P.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Clinical Epidemiology and Biostatistics (S.K.), Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Radiology (E.L.), Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea; Department of Radiology (R.W.L.), Inha University Hospital, Jung-gu, Incheon, Korea
| | - Choong Guen Chee
- Department of Radiology and Research Institute of Radiology (A.Y.K., M.A.Y., S.J.H., C.G.C., M.H.L., S.H.L., H.W.C.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Korea; Biomedical Research Center (Y.C.C., Y.K.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Health Innovation Big Data Center (B.P.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Clinical Epidemiology and Biostatistics (S.K.), Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Radiology (E.L.), Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea; Department of Radiology (R.W.L.), Inha University Hospital, Jung-gu, Incheon, Korea
| | - Min Hee Lee
- Department of Radiology and Research Institute of Radiology (A.Y.K., M.A.Y., S.J.H., C.G.C., M.H.L., S.H.L., H.W.C.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Korea; Biomedical Research Center (Y.C.C., Y.K.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Health Innovation Big Data Center (B.P.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Clinical Epidemiology and Biostatistics (S.K.), Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Radiology (E.L.), Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea; Department of Radiology (R.W.L.), Inha University Hospital, Jung-gu, Incheon, Korea
| | - Sang Hoon Lee
- Department of Radiology and Research Institute of Radiology (A.Y.K., M.A.Y., S.J.H., C.G.C., M.H.L., S.H.L., H.W.C.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Korea; Biomedical Research Center (Y.C.C., Y.K.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Health Innovation Big Data Center (B.P.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Clinical Epidemiology and Biostatistics (S.K.), Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Radiology (E.L.), Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea; Department of Radiology (R.W.L.), Inha University Hospital, Jung-gu, Incheon, Korea
| | - Hye Won Chung
- Department of Radiology and Research Institute of Radiology (A.Y.K., M.A.Y., S.J.H., C.G.C., M.H.L., S.H.L., H.W.C.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Korea; Biomedical Research Center (Y.C.C., Y.K.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Health Innovation Big Data Center (B.P.), Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Clinical Epidemiology and Biostatistics (S.K.), Asan Medical Center, Songpa-gu, Seoul, Korea; Department of Radiology (E.L.), Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea; Department of Radiology (R.W.L.), Inha University Hospital, Jung-gu, Incheon, Korea
| |
Collapse
|
|
13
|
Weber MA, Bazzocchi A, Nöbauer-Huhmann IM. Tumors of the Spine: When Can Biopsy Be Avoided? Semin Musculoskelet Radiol 2022; 26:453-468. [PMID: 36103887 DOI: 10.1055/s-0042-1753506] [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
Regarding osseous tumors of the spine, characteristic morphology is encountered in hemangioma of the vertebral body, osteoid osteoma (OO), osteochondroma, Paget's disease, and bone islands. In these cases, radiologic imaging can make a specific diagnosis and thereby avoid biopsy, especially when the radiologist has chosen the correct imaging modality to establish the diagnosis, such as thin-slice computed tomography in suspected OO. A benign lesion is suggested by a high amount of fat within the lesion, the lack of uptake of the contrast agent, and a homogeneous aspect without solid parts in a cystic tumor. Suspicion of malignancy should be raised in spinal lesions with a heterogeneous disordered matrix, distinct signal decrease in T1-weighted magnetic resonance imaging, blurred border, perilesional edema, cortex erosion, and a large soft tissue component. Biopsy is mandatory in presumed malignancy, such as any Lodwick grade II or III osteolytic lesion in the vertebral column. The radiologist plays a crucial role in determining the clinical pathway by choosing the imaging approach wisely, by narrowing the differential diagnosis list, and, when characteristic morphology is encountered, by avoiding unnecessary biopsies.
Collapse
Affiliation(s)
- Marc-André Weber
- Institute of Diagnostic and Interventional Radiology, Pediatric Radiology and Neuroradiology, University Medical Center Rostock, Rostock, Germany
| | - Alberto Bazzocchi
- Diagnostic and Interventional Radiology, The Rizzoli Orthopedic Institute, Bologna, Italy
| | - Iris-M Nöbauer-Huhmann
- Department of Biomedical Imaging and Image Guided Therapy, Division of Neuroradiology and Musculoskeletal Radiology, Medical University of Vienna, Vienna, Austria
| |
Collapse
|
|
14
|
Kuah T, Vellayappan BA, Makmur A, Nair S, Song J, Tan JH, Kumar N, Quek ST, Hallinan JTPD. State-of-the-Art Imaging Techniques in Metastatic Spinal Cord Compression. Cancers (Basel) 2022; 14:3289. [PMID: 35805059 PMCID: PMC9265325 DOI: 10.3390/cancers14133289] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 06/24/2022] [Accepted: 06/28/2022] [Indexed: 12/23/2022] Open
Abstract
Metastatic Spinal Cord Compression (MSCC) is a debilitating complication in oncology patients. This narrative review discusses the strengths and limitations of various imaging modalities in diagnosing MSCC, the role of imaging in stereotactic body radiotherapy (SBRT) for MSCC treatment, and recent advances in deep learning (DL) tools for MSCC diagnosis. PubMed and Google Scholar databases were searched using targeted keywords. Studies were reviewed in consensus among the co-authors for their suitability before inclusion. MRI is the gold standard of imaging to diagnose MSCC with reported sensitivity and specificity of 93% and 97% respectively. CT Myelogram appears to have comparable sensitivity and specificity to contrast-enhanced MRI. Conventional CT has a lower diagnostic accuracy than MRI in MSCC diagnosis, but is helpful in emergent situations with limited access to MRI. Metal artifact reduction techniques for MRI and CT are continually being researched for patients with spinal implants. Imaging is crucial for SBRT treatment planning and three-dimensional positional verification of the treatment isocentre prior to SBRT delivery. Structural and functional MRI may be helpful in post-treatment surveillance. DL tools may improve detection of vertebral metastasis and reduce time to MSCC diagnosis. This enables earlier institution of definitive therapy for better outcomes.
Collapse
Affiliation(s)
- Tricia Kuah
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore; (A.M.); (S.N.); (J.S.); (S.T.Q.); (J.T.P.D.H.)
| | - Balamurugan A. Vellayappan
- Department of Radiation Oncology, National University Cancer Institute Singapore, National University Hospital, Singapore 119074, Singapore;
| | - Andrew Makmur
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore; (A.M.); (S.N.); (J.S.); (S.T.Q.); (J.T.P.D.H.)
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - Shalini Nair
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore; (A.M.); (S.N.); (J.S.); (S.T.Q.); (J.T.P.D.H.)
| | - Junda Song
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore; (A.M.); (S.N.); (J.S.); (S.T.Q.); (J.T.P.D.H.)
| | - Jiong Hao Tan
- University Spine Centre, Department of Orthopaedic Surgery, National University Health System, 1E Lower Kent Ridge Road, Singapore 119228, Singapore; (J.H.T.); (N.K.)
| | - Naresh Kumar
- University Spine Centre, Department of Orthopaedic Surgery, National University Health System, 1E Lower Kent Ridge Road, Singapore 119228, Singapore; (J.H.T.); (N.K.)
| | - Swee Tian Quek
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore; (A.M.); (S.N.); (J.S.); (S.T.Q.); (J.T.P.D.H.)
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - James Thomas Patrick Decourcy Hallinan
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore; (A.M.); (S.N.); (J.S.); (S.T.Q.); (J.T.P.D.H.)
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| |
Collapse
|
|
15
|
Ruiz Santiago F, Láinez Ramos-Bossini AJ, Wáng YXJ, Martínez Barbero JP, García Espinosa J, Martínez Martínez A. The value of magnetic resonance imaging and computed tomography in the study of spinal disorders. Quant Imaging Med Surg 2022; 12:3947-3986. [PMID: 35782254 PMCID: PMC9246762 DOI: 10.21037/qims-2022-04] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 04/13/2022] [Indexed: 08/15/2023]
Abstract
Computed tomography (CT) and magnetic resonance imaging (MRI) have replaced conventional radiography in the study of many spinal conditions, it is essential to know when these techniques are indicated instead of or as complementary tests to radiography, which findings can be expected in different clinical settings, and their significance in the diagnosis of different spinal conditions. Proper use of CT and MRI in spinal disorders may facilitate diagnosis and management of spinal conditions. An adequate clinical approach, a good understanding of the pathological manifestations demonstrated by these imaging techniques and a comprehensive report based on a universally accepted nomenclature represent the indispensable tools to improve the diagnostic approach and the decision-making process in patients with spinal pain. Several guidelines are available to assist clinicians in ordering appropriate imaging techniques to achieve an accurate diagnosis and to ensure appropriate medical care that meets the efficacy and safety needs of patients. This article reviews the clinical indications of CT and MRI in different pathologic conditions affecting the spine, including congenital, traumatic, degenerative, inflammatory, infectious and tumor disorders, as well as their main imaging features. It is intended to be a pictorial guide to clinicians involved in the diagnosis and treatment of spinal disorders.
Collapse
Affiliation(s)
| | | | - Yì Xiáng J. Wáng
- Department of Imaging and Interventional Radiology, the Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
| | - José Pablo Martínez Barbero
- Department of Radiology and Physical Medicine, Hospital Virgen de las Nieves, University of Granada, Granada, Spain
| | - Jade García Espinosa
- Department of Radiology and Physical Medicine, Hospital Virgen de las Nieves, University of Granada, Granada, Spain
| | - Alberto Martínez Martínez
- Department of Radiology and Physical Medicine, Hospital Virgen de las Nieves, University of Granada, Granada, Spain
| |
Collapse
|
|
16
|
Del Lama RS, Candido RM, Chiari-Correia NS, Nogueira-Barbosa MH, de Azevedo-Marques PM, Tinós R. Computer-Aided Diagnosis of Vertebral Compression Fractures Using Convolutional Neural Networks and Radiomics. J Digit Imaging 2022; 35:446-458. [PMID: 35132524 PMCID: PMC9156595 DOI: 10.1007/s10278-022-00586-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 12/28/2021] [Accepted: 12/30/2021] [Indexed: 12/15/2022] Open
Abstract
Vertebral Compression Fracture (VCF) occurs when the vertebral body partially collapses under the action of compressive forces. Non-traumatic VCFs can be secondary to osteoporosis fragility (benign VCFs) or tumors (malignant VCFs). The investigation of the etiology of non-traumatic VCFs is usually necessary, since treatment and prognosis are dependent on the VCF type. Currently, there has been great interest in using Convolutional Neural Networks (CNNs) for the classification of medical images because these networks allow the automatic extraction of useful features for the classification in a given problem. However, CNNs usually require large datasets that are often not available in medical applications. Besides, these networks generally do not use additional information that may be important for classification. A different approach is to classify the image based on a large number of predefined features, an approach known as radiomics. In this work, we propose a hybrid method for classifying VCFs that uses features from three different sources: i) intermediate layers of CNNs; ii) radiomics; iii) additional clinical and image histogram information. In the hybrid method proposed here, external features are inserted as additional inputs to the first dense layer of a CNN. A Genetic Algorithm is used to: i) select a subset of radiomic, clinical, and histogram features relevant to the classification of VCFs; ii) select hyper-parameters of the CNN. Experiments using different models indicate that combining information is interesting to improve the performance of the classifier. Besides, pre-trained CNNs presents better performance than CNNs trained from scratch on the classification of VCFs.
Collapse
Affiliation(s)
- Rafael Silva Del Lama
- Department of Computing and Mathematics, FFCLRP, University of São Paulo, Av. Bandeirantes, 3900, Ribeirão Preto, 14040-901, Brazil
| | - Raquel Mariana Candido
- Department of Computing and Mathematics, FFCLRP, University of São Paulo, Av. Bandeirantes, 3900, Ribeirão Preto, 14040-901, Brazil
| | - Natália Santana Chiari-Correia
- Medical Artificial Intelligence Laboratory, Department of Medical Imaging, Hematology and Clinical Oncology, Ribeirão Preto Medical School, University of São Paulo, Av. Bandeirantes, 3900, Ribeirão Preto, 14049-900, Brazil
| | - Marcello Henrique Nogueira-Barbosa
- Medical Artificial Intelligence Laboratory, Department of Medical Imaging, Hematology and Clinical Oncology, Ribeirão Preto Medical School, University of São Paulo, Av. Bandeirantes, 3900, Ribeirão Preto, 14049-900, Brazil
| | - Paulo Mazzoncini de Azevedo-Marques
- Medical Artificial Intelligence Laboratory, Department of Medical Imaging, Hematology and Clinical Oncology, Ribeirão Preto Medical School, University of São Paulo, Av. Bandeirantes, 3900, Ribeirão Preto, 14049-900, Brazil
| | - Renato Tinós
- Department of Computing and Mathematics, FFCLRP, University of São Paulo, Av. Bandeirantes, 3900, Ribeirão Preto, 14040-901, Brazil.
| |
Collapse
|
|
17
|
|
|
18
|
Automated segmentation of the fractured vertebrae on CT and its applicability in a radiomics model to predict fracture malignancy. Sci Rep 2022; 12:6735. [PMID: 35468985 PMCID: PMC9038736 DOI: 10.1038/s41598-022-10807-7] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Accepted: 04/13/2022] [Indexed: 11/08/2022] Open
Abstract
Although CT radiomics has shown promising results in the evaluation of vertebral fractures, the need for manual segmentation of fractured vertebrae limited the routine clinical implementation of radiomics. Therefore, automated segmentation of fractured vertebrae is needed for successful clinical use of radiomics. In this study, we aimed to develop and validate an automated algorithm for segmentation of fractured vertebral bodies on CT, and to evaluate the applicability of the algorithm in a radiomics prediction model to differentiate benign and malignant fractures. A convolutional neural network was trained to perform automated segmentation of fractured vertebral bodies using 341 vertebrae with benign or malignant fractures from 158 patients, and was validated on independent test sets (internal test, 86 vertebrae [59 patients]; external test, 102 vertebrae [59 patients]). Then, a radiomics model predicting fracture malignancy on CT was constructed, and the prediction performance was compared between automated and human expert segmentations. The algorithm achieved good agreement with human expert segmentation at testing (Dice similarity coefficient, 0.93-0.94; cross-sectional area error, 2.66-2.97%; average surface distance, 0.40-0.54 mm). The radiomics model demonstrated good performance in the training set (AUC, 0.93). In the test sets, automated and human expert segmentations showed comparable prediction performances (AUC, internal test, 0.80 vs 0.87, p = 0.044; external test, 0.83 vs 0.80, p = 0.37). In summary, we developed and validated an automated segmentation algorithm that showed comparable performance to human expert segmentation in a CT radiomics model to predict fracture malignancy, which may enable more practical clinical utilization of radiomics.
Collapse
|
|
19
|
Automated Differentiation Between Osteoporotic Vertebral Fracture and Malignant Vertebral Fracture on MRI Using a Deep Convolutional Neural Network. Spine (Phila Pa 1976) 2022; 47:E347-E352. [PMID: 34919075 DOI: 10.1097/brs.0000000000004307] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
STUDY DESIGN Retrospective study of magnetic resonance imaging (MRI). OBJECTIVES To assess the ability of a convolutional neural network (CNN) model to differentiate osteoporotic vertebral fractures (OVFs) and malignant vertebral compression fractures (MVFs) using short-TI inversion recovery (STIR) and T1-weighted images (T1WI) and to compare it to the performance of three spine surgeons. SUMMARY OF BACKGROUND DATA Differentiating between OVFs and MVFs is crucial for appropriate clinical staging and treatment planning. However, an accurate diagnosis is sometimes difficult. Recently, CNN modeling-an artificial intelligence technique-has gained popularity in the radiology field. METHODS We enrolled 50 patients with OVFs and 47 patients with MVFs who underwent thoracolumbar MRI. Sagittal STIR images and sagittal T1WI were used to train and validate the CNN models. To assess the performance of the CNN, the receiver operating characteristic curve was plotted and the area under the curve was calculated. We also compared the accuracy, sensitivity, and specificity of the diagnosis made by the CNN and three spine surgeons. RESULTS The area under the curve of receiver operating characteristic curves of the CNN based on STIR images and T1WI were 0.967 and 0.984, respectively. The CNN model based on STIR images showed a performance of 93.8% accuracy, 92.5% sensitivity, and 94.9% specificity. On the other hand, the CNN model based on T1WI showed a performance of 96.4% accuracy, 98.1% sensitivity, and 94.9% specificity. The accuracy and specificity of the CNN using both STIR and T1WI were statistically equal to or better than that of three spine surgeons. There were no significant differences in sensitivity based on both STIR images and T1WI between the CNN and spine surgeons. CONCLUSION We successfully differentiated OVFs and MVFs based on MRI with high accuracy using the CNN model, which was statistically equal or superior to that of the spine surgeons.Level of Evidence: 4.
Collapse
|
|
20
|
Diffusion-weighted MRI radiomics of spine bone tumors: feature stability and machine learning-based classification performance. Radiol Med 2022; 127:518-525. [PMID: 35320464 PMCID: PMC9098537 DOI: 10.1007/s11547-022-01468-7] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Accepted: 02/11/2022] [Indexed: 10/29/2022]
Abstract
PURPOSE To evaluate stability and machine learning-based classification performance of radiomic features of spine bone tumors using diffusion- and T2-weighted magnetic resonance imaging (MRI). MATERIAL AND METHODS This retrospective study included 101 patients with histology-proven spine bone tumor (22 benign; 38 primary malignant; 41 metastatic). All tumor volumes were manually segmented on morphologic T2-weighted sequences. The same region of interest (ROI) was used to perform radiomic analysis on ADC map. A total of 1702 radiomic features was considered. Feature stability was assessed through small geometrical transformations of the ROIs mimicking multiple manual delineations. Intraclass correlation coefficient (ICC) quantified feature stability. Feature selection consisted of stability-based (ICC > 0.75) and significance-based selections (ranking features by decreasing Mann-Whitney p-value). Class balancing was performed to oversample the minority (i.e., benign) class. Selected features were used to train and test a support vector machine (SVM) to discriminate benign from malignant spine tumors using tenfold cross-validation. RESULTS A total of 76.4% radiomic features were stable. The quality metrics for the SVM were evaluated as a function of the number of selected features. The radiomic model with the best performance and the lowest number of features for classifying tumor types included 8 features. The metrics were 78% sensitivity, 68% specificity, 76% accuracy and AUC 0.78. CONCLUSION SVM classifiers based on radiomic features extracted from T2- and diffusion-weighted imaging with ADC map are promising for classification of spine bone tumors. Radiomic features of spine bone tumors show good reproducibility rates.
Collapse
|
|
21
|
Key BM, Symanski J, Scheidt MJ, Tutton SM. Vertebroplasty, Kyphoplasty, and Implant-Based Mechanical Vertebral Augmentation. Semin Musculoskelet Radiol 2021; 25:785-794. [PMID: 34937118 DOI: 10.1055/s-0041-1739531] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Vertebral compression fractures are a global public health issue with a quantifiable negative impact on patient morbidity and mortality. The contemporary approach to the treatment of osteoporotic fragility fractures has moved beyond first-line nonsurgical management. An improved understanding of biomechanical forces, consequential morbidity and mortality, and the drive to reduce opioid use has resulted in multidisciplinary treatment algorithms and significant advances in augmentation techniques. This review will inform musculoskeletal radiologists, interventionalists, and minimally invasive spine surgeons on the proper work-up of patients, imaging features differentiating benign and malignant pathologic fractures, high-risk fracture morphologies, and new mechanical augmentation device options, and it describes the appropriate selection of devices, complications, outcomes, and future trends.
Collapse
Affiliation(s)
- Brandon M Key
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - John Symanski
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Matthew J Scheidt
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Sean M Tutton
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin.,Department of Orthopedic Surgery, and Palliative Medicine, Medical College of Wisconsin, Milwaukee, Wisconsin
| |
Collapse
|
|
22
|
Hutchins TA, Peckham M, Shah LM, Parsons MS, Agarwal V, Boulter DJ, Burns J, Cassidy RC, Davis MA, Holly LT, Hunt CH, Khan MA, Moritani T, Ortiz AO, O'Toole JE, Powers WJ, Promes SB, Reitman C, Shah VN, Singh S, Timpone VM, Corey AS. ACR Appropriateness Criteria® Low Back Pain: 2021 Update. J Am Coll Radiol 2021; 18:S361-S379. [PMID: 34794594 DOI: 10.1016/j.jacr.2021.08.002] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Accepted: 08/26/2021] [Indexed: 01/19/2023]
Abstract
In the United States, acute low back pain, with or without radiculopathy, is the leading cause of years lived with disability and the third ranking cause of disability-adjusted life-years. Uncomplicated acute low back pain and/or radiculopathy is a benign, self-limited condition that does not warrant any imaging studies. Imaging is considered in those patients who have had up to 6 weeks of medical management and physical therapy that resulted in little or no improvement in their back pain. It is also considered for those patients presenting with red flags, raising suspicion for a serious underlying condition, such as cauda equina syndrome, malignancy, fracture, or infection. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment.
Collapse
Affiliation(s)
- Troy A Hutchins
- Chief Value Officer, Department of Radiology, University of Utah Health, Salt Lake City, Utah.
| | - Miriam Peckham
- Research Author, University of Utah Medical Center, Salt Lake City, Utah
| | - Lubdha M Shah
- Panel Chair, University of Utah, Salt Lake City, Utah
| | - Matthew S Parsons
- Panel Vice-Chair, Mallinckrodt Institute of Radiology, Saint Louis, Missouri
| | - Vikas Agarwal
- Vice-Chair, Education, Chief, Neuroradiology, and Director, Spine Intervention, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
| | - Daniel J Boulter
- Clinical Director, MRI, The Ohio State University Wexner Medical Center, Columbus, Ohio
| | - Judah Burns
- Program Director, Diagnostic Radiology Residency Program, Montefiore Medical Center, Bronx, New York
| | - R Carter Cassidy
- UK Healthcare Spine and Total Joint Service, Lexington, Kentucky; Executive Board, Kentucky Orthopaedic Society; and American Academy of Orthopaedic Surgeons
| | - Melissa A Davis
- Director of Quality, Department of Radiology, Emory University, Atlanta, Georgia; and ACR YPS Communications Liaison
| | - Langston T Holly
- UCLA Medical Center, Los Angeles, California, Neurosurgery expert
| | | | | | | | - A Orlando Ortiz
- Chairman, Department of Radiology, Jacobi Medical Center, Bronx, New York
| | | | - William J Powers
- University of North Carolina School of Medicine, Chapel Hill, North Carolina; American Academy of Neurology; and Chair, Writing Group - American Heart Association/American Stroke Association Guidelines for the Early Management of Patients with Acute Ischemic Stroke, 2016-2019
| | - Susan B Promes
- Pennsylvania State University College of Medicine, Hershey, Pennsylvania; American College of Emergency Physicians; Editor-in-Chief, AEM Education & Training; and Board Member, Pennsylvania Psychiatric Hospital
| | - Charles Reitman
- Medical University of South Carolina, Charleston, South Carolina; North American Spine Society
| | - Vinil N Shah
- University of California San Francisco, San Francisco, California; Executive Committee, American Society of Spine Radiology; and Board of Directors, Spine Intervention Society
| | - Simranjit Singh
- Indiana University School of Medicine, Indianapolis, Indiana; American College of Physicians; Secretary, SHM, Indiana chapter; and Secretary, SGIM Midwest Region
| | - Vincent M Timpone
- Co-Director, Neuroradiology, Spine Intervention Service, and Director, Stroke and Vascular Imaging, Department of Radiology, University of Colorado School of Medicine, Anschutz Medical Campus, Aurora, Colorado
| | - Amanda S Corey
- Specialty Chair, Atlanta VA Health Care System and Emory University, Atlanta, Georgia
| |
Collapse
|
|
23
|
The role of chemical shift magnetic resonance imaging in differentiating osteoporotic benign and malignant vertebral marrow lesions. Pol J Radiol 2021; 86:e468-e473. [PMID: 34567292 PMCID: PMC8449562 DOI: 10.5114/pjr.2021.108541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2020] [Accepted: 08/04/2020] [Indexed: 11/17/2022] Open
Abstract
Purpose To evaluate the usefulness of chemical shift imaging (CSI) in differentiating benign osteoporotic and malignant vertebral marrow lesions. Material and methods Patients undergoing spinal magnetic resonance imaging (MRI) for back pain, which showed altered marrow signal intensity on conventional MRI sequences, were included in the study. Patients with acute traumatic vertebral fractures, infective spondylodiscitis, paravertebral collections, etc. were excluded. The patients underwent CSI. In-phase and opposed-phase images were taken to calculate the signal intensity ratio (SIR) of the abnormal vertebra. The SIR of the mean signal intensity measured on opposed-phase to mean signal intensity measured on in-phase images was measured and recorded. Results The studied population included 30 patients, in whom 58 vertebrae were accessed, which included 38 dorsal, 18 lumbar, 1 sacral, and 1 cervical. Out of 58 vertebrae, 46 (79%) were malignant and 12 (20%) were benign. The mean CSI/SIR of malignant lesions was 0.96 and the mean SIR of benign lesions was 0.76. Conclusions Conventional MRI sequences cannot always differentiate between benign and malignant lesions. So newer sequences like CSI have been developed. CSI SIR can be used as a new tool in differentiating benign osteoporotic and malignant vertebral marrow lesions.
Collapse
|
|
24
|
[Radiological aspects in the diagnostics of pathological fractures]. Unfallchirurg 2021; 124:695-703. [PMID: 34324034 DOI: 10.1007/s00113-021-01067-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/07/2021] [Indexed: 10/20/2022]
Abstract
Radiological diagnostics play a key role in the identification and assessment of pathological fractures. Conventional projection radiography is still the mainstay of imaging investigations. With knowledge of the patient history, the morphology and location of a fracture as well as concomitant findings, such as osteolysis or periosteal reactions can add valuable information on the origin of the fracture. Magnetic resonance imaging (MRI) is the imaging modality of choice for the local diagnostic work-up as it provides insights into the medullary cavity and visualizes potential extraosseous tumor tissue in the fracture zone. Computed tomography (CT) imaging provides valuable information on the morphological features of fractures and is useful for the planning of the surgical approach. Furthermore, it is the modality of choice for whole-body staging. In most cases of pathological fractures without a history of malignancy, a biopsy and histological work-up is recommended.
Collapse
|
|
25
|
Yıldız AE, Özbalcı AB, Ergen FB, Aydıngöz Ü. Pregnancy- and lactation-associated vertebral compression fractures: MRI prevalence and characteristics. Osteoporos Int 2021; 32:981-989. [PMID: 33236194 DOI: 10.1007/s00198-020-05754-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Accepted: 11/18/2020] [Indexed: 12/11/2022]
Abstract
UNLABELLED The frequency of pregnancy- and lactation-associated vertebral compression fractures (PLVCFs) is not known. This study showed that MRI prevalence of PLVCFs was approximately 0.5% in females ≥ 15 and < 40 years of age over a 48-month period. PLVCFs did not display MRI features distinguishing them from other vertebral insufficiency fractures. PURPOSE We aimed to investigate the MRI prevalence and characteristics of pregnancy- and lactation-associated vertebral compression fractures (PLVCFs). METHODS This retrospective cross-sectional observational study included all thoracic, lumbar, or thoracolumbar MRI examinations performed in our hospital (or at outside centers and referred to us for consultation) of females ≥ 15 and < 40 years of age during a 48-month period. Two radiologists independently reviewed all images for vertebral compression fractures and their disagreement was resolved by a third blinded senior radiologist with 24 years of dedicated musculoskeletal radiology experience. MRI features of PLVCFs (early/late stage, height loss, endplate involvement, retropulsion) were noted. RESULTS A total of 1484 MRI examinations-including 50 consultations from outside centers-of 1260 females (mean age, 27.7 years; range, 15-39) were included. Interobserver agreement of the two junior radiologists was substantial (κ = 0.607; 95% CI, 0.545-0.669). Vertebral compression fractures were identified in 177 of thoracic (n = 210), lumbar (n = 900), or thoracolumbar MRI (n = 374) examinations. Six women (7 MRI examinations; 4.0% of MRIs with vertebral fractures) had PLVCFs diagnosed on MRI (prevalence, 0.47%; mean age, 31 years; age range, 25-37). Number of fractured vertebrae in cases with PLVCF ranged between 1 and 11 (mean, 5.6). DEXA, available in all patients with PLVCFs, verified osteopenia/osteoporosis in four of six patients. CONCLUSION PLVCFs have an MRI prevalence of approximately 0.5% in the target population and do not display distinguishing features from other insufficiency type vertebral compression fractures.
Collapse
Affiliation(s)
- A E Yıldız
- Department of Radiology, Hacettepe University School of Medicine, Sihhiye, 06100, Ankara, Turkey.
| | - A B Özbalcı
- Department of Radiology, Ondokuz Mayıs University School of Medicine, Atakum, 55139, Samsun, Turkey
| | - F B Ergen
- Department of Radiology, Hacettepe University School of Medicine, Sihhiye, 06100, Ankara, Turkey
| | - Ü Aydıngöz
- Department of Radiology, Hacettepe University School of Medicine, Sihhiye, 06100, Ankara, Turkey
| |
Collapse
|
|
26
|
Husseini JS, Amorim BJ, Torrado-Carvajal A, Prabhu V, Groshar D, Umutlu L, Herrmann K, Cañamaque LG, Garzón JRG, Palmer WE, Heidari P, Shih TTF, Sosna J, Matushita C, Cerci J, Queiroz M, Muglia VF, Nogueira-Barbosa MH, Borra RJH, Kwee TC, Glaudemans AWJM, Evangelista L, Salvatore M, Cuocolo A, Soricelli A, Herold C, Laghi A, Mayerhoefer M, Mahmood U, Catana C, Daldrup-Link HE, Rosen B, Catalano OA. An international expert opinion statement on the utility of PET/MR for imaging of skeletal metastases. Eur J Nucl Med Mol Imaging 2021; 48:1522-1537. [PMID: 33619599 PMCID: PMC8240455 DOI: 10.1007/s00259-021-05198-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Accepted: 01/10/2021] [Indexed: 12/29/2022]
Abstract
BACKGROUND MR is an important imaging modality for evaluating musculoskeletal malignancies owing to its high soft tissue contrast and its ability to acquire multiparametric information. PET provides quantitative molecular and physiologic information and is a critical tool in the diagnosis and staging of several malignancies. PET/MR, which can take advantage of its constituent modalities, is uniquely suited for evaluating skeletal metastases. We reviewed the current evidence of PET/MR in assessing for skeletal metastases and provided recommendations for its use. METHODS We searched for the peer reviewed literature related to the usage of PET/MR in the settings of osseous metastases. In addition, expert opinions, practices, and protocols of major research institutions performing research on PET/MR of skeletal metastases were considered. RESULTS Peer-reviewed published literature was included. Nuclear medicine and radiology experts, including those from 13 major PET/MR centers, shared the gained expertise on PET/MR use for evaluating skeletal metastases and contributed to a consensus expert opinion statement. [18F]-FDG and non [18F]-FDG PET/MR may provide key advantages over PET/CT in the evaluation for osseous metastases in several primary malignancies. CONCLUSION PET/MR should be considered for staging of malignancies where there is a high likelihood of osseous metastatic disease based on the characteristics of the primary malignancy, hight clinical suspicious and in case, where the presence of osseous metastases will have an impact on patient management. Appropriate choice of tumor-specific radiopharmaceuticals, as well as stringent adherence to PET and MR protocols, should be employed.
Collapse
Affiliation(s)
- Jad S Husseini
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Bárbara Juarez Amorim
- Division of Nuclear Medicine, Department of Radiology, School of Medical Sciences,, State University of Campinas, Campinas, Brazil
| | - Angel Torrado-Carvajal
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
- Medical Image Analysis and Biometry Laboratory, Universidad Rey Juan Carlos, Madrid, Spain
| | - Vinay Prabhu
- Department of Radiology, NYU Langone Health, New York, NY, USA
| | - David Groshar
- Department of Nuclear Medicine, Assuta Medical Center, Tel Aviv, and Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Lale Umutlu
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Ken Herrmann
- Department of Nuclear Medicine, University Hospital Essen, Essen, Germany
| | - Lina García Cañamaque
- Department of Nuclear Medicine, Hospital Universitario Madrid Sanchinarro, Madrid, Spain
| | | | - William E Palmer
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Pedram Heidari
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Tiffany Ting-Fang Shih
- Department of Radiology and Medical Imaging, National Taiwan University College of Medicine and Hospital, Taipei City, Taiwan
| | - Jacob Sosna
- Department of Radiology, Hadassah Hebrew University Medical Center, Jerusalem, Israel
| | - Cristina Matushita
- Department of Nuclear Medicine, Hospital São Lucas of Pontifícia Universidade Católica do Rio Grande do Sul, Porto Alegre, Brazil
| | - Juliano Cerci
- Department of Nuclear Medicine, Quanta Diagnóstico Nuclear, Curitiba, Brazil
| | - Marcelo Queiroz
- Department of Radiology and Oncology, Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, Brazil
| | - Valdair Francisco Muglia
- Department of Medical Images, Radiation Therapy and Oncohematology, Ribeirao Preto Medical School, Hospital Clinicas, University of São Paulo, Ribeirão Prêto, Brazil
| | - Marcello H Nogueira-Barbosa
- Department of Medical Imaging, Hematology and Clinical Oncology, Ribeirão Preto Medical School. University of São Paulo (USP), Ribeirão Prêto, Brazil
| | - Ronald J H Borra
- Medical Imaging Center, University Medical Center Groningen, Groningen, The Netherlands
| | - Thomas C Kwee
- Medical Imaging Center, University Medical Center Groningen, Groningen, The Netherlands
| | - Andor W J M Glaudemans
- Medical Imaging Center, Department of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, Groningen, The Netherlands
| | - Laura Evangelista
- Department of Clinical and Experimental Medicine, University of Padova, Padua, Italy
| | - Marco Salvatore
- Department of Radiology and Nuclear Medicine, Università Suor Orsola Benincasa di Napoli, Naples, Italy
- Department of Radiology and Nuclear Medicine, Institute for Hospitalization and Healthcare (IRCCS) SDN, Istituto di Ricerca, Naples, Italy
| | - Alberto Cuocolo
- Department of Radiology and Nuclear Medicine, Institute for Hospitalization and Healthcare (IRCCS) SDN, Istituto di Ricerca, Naples, Italy
- Department of Advanced Biomedical Science, University of Naples Federico II, Naples, Italy
| | - Andrea Soricelli
- Department of Radiology and Nuclear Medicine, Institute for Hospitalization and Healthcare (IRCCS) SDN, Istituto di Ricerca, Naples, Italy
- Department of Movement and Wellness Sciences, Parthenope University of Naples, Naples, Italy
| | - Christian Herold
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna General Hospital, Vienna, Austria
| | - Andrea Laghi
- Department of Radiology, University of Rome "La Sapienza", Rome, Italy
| | - Marius Mayerhoefer
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Umar Mahmood
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Ciprian Catana
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | | | - Bruce Rosen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Onofrio A Catalano
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA.
| |
Collapse
|
|
27
|
Musa Aguiar P, Zarantonello P, Aparisi Gómez MP. Differentiation Between Osteoporotic And Neoplastic Vertebral Fractures: State Of The Art And Future Perspectives. Curr Med Imaging 2021; 18:187-207. [PMID: 33845727 DOI: 10.2174/1573405617666210412142758] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Revised: 02/25/2021] [Accepted: 02/26/2021] [Indexed: 11/22/2022]
Abstract
Vertebral fractures are a common condition, occurring in the context of osteoporosis and malignancy. These entities affect a group of patients in the same age range; clinical features may be indistinct and symptoms non-existing, and thus present challenges to diagnosis. In this article, we review the use and accuracy of different imaging modalities available to characterize vertebral fracture etiology, from well-established classical techniques, to the role of new and advanced imaging techniques, and the prospective use of artificial intelligence. We also address the role of imaging on treatment. In the context of osteoporosis, the importance of opportunistic diagnosis is highlighted. In the near future, the use of automated computer-aided diagnostic algorithms applied to different imaging techniques may be really useful to aid on diagnosis.
Collapse
Affiliation(s)
- Paula Musa Aguiar
- Serdil, Clinica de Radiologia e Diagnóstico por Imagem; R. São Luís, 96 - Santana, Porto Alegre - RS, 90620-170. Brazil
| | - Paola Zarantonello
- Department of paediatric orthopedics and traumatology, IRCCS Istituto Ortopedico Rizzoli; Via G. C. Pupilli 1, 40136 Bologna. Italy
| | | |
Collapse
|
|
28
|
Vertebral bone marrow edema in magnetic resonance imaging correlates with bone healing histomorphometry in (sub)acute osteoporotic vertebral compression fracture. 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 2021; 30:2708-2717. [PMID: 33743056 DOI: 10.1007/s00586-021-06814-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Revised: 01/20/2021] [Accepted: 03/07/2021] [Indexed: 10/21/2022]
Abstract
BACKGROUND BME on MRI has become the gold standard for the diagnosis of acute/subacute OVCF, but the correlation between the quantitative model of BME and histopathological manifestations of OVCF is rarely discussed in the literature. OBJECTIVES This study aimed to retrospectively investigate the relationship between bone marrow edema (BME) in magnetic resonance imaging (MRI) and bone healing histomorphometry in (sub)acute osteoporotic vertebral compression fracture. METHODS According to the period since fracture, 125 patients were divided into four stages: stage I (0 to 15 days), stage II (16 to 30 days), stage III (31 to 60 days) and stage IV (61 to 90 days). Bone marrow edema was evaluated by the signal changes and intensity patterns on MRI sagittal images. Decalcified biopsy specimens were obtained from the cancellous bone core in the fractured vertebral body. The histomorphometry study results were analyzed by light microscopy using grid analysis and defined using bone histomorphometry criteria. RESULTS There were 70 (56%) patients in stage I, 29 (23.2%) in stage II, 12 (9.6%) in stage III and 14 (11.2%) in stage IV. BME and histomorphometry characteristics differentiated from each other stage: The BME percentage had a significantly negative correlation with the ratio of osteoid volume/bone volume (r = - 0.539, p = 0.001) and the ratio of woven bone volume/tissue volume (r = - 0.584, p = 0.001). There was also a positive correlation between the BME percentage and the ratio of fibrous tissue volume/tissue volume (r = 0.488, p = 0.001). CONCLUSIONS Bone marrow edema significantly correlates with bone morphology parameters after vertebral fracture. The characteristics of histomorphological changes during fracture healing process can be preliminarily determined by observing the edema signal.
Collapse
|
|
29
|
Combined radiomics-clinical model to predict malignancy of vertebral compression fractures on CT. Eur Radiol 2021; 31:6825-6834. [PMID: 33742227 DOI: 10.1007/s00330-021-07832-x] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Accepted: 02/24/2021] [Indexed: 12/28/2022]
Abstract
OBJECTIVES To develop and validate a combined radiomics-clinical model to predict malignancy of vertebral compression fractures on CT. METHODS One hundred sixty-five patients with vertebral compression fractures were allocated to training (n = 110 [62 acute benign and 48 malignant fractures]) and validation (n = 55 [30 acute benign and 25 malignant fractures]) cohorts. Radiomics features (n = 144) were extracted from non-contrast-enhanced CT images. Radiomics score was constructed by applying least absolute shrinkage and selection operator regression to reproducible features. A combined radiomics-clinical model was constructed by integrating significant clinical parameters with radiomics score using multivariate logistic regression analysis. Model performance was quantified in terms of discrimination and calibration. The model was internally validated on the independent data set. RESULTS The combined radiomics-clinical model, composed of two significant clinical predictors (age and history of malignancy) and the radiomics score, showed good calibration (Hosmer-Lemeshow test, p > 0.05) and discrimination in both training (AUC, 0.970) and validation (AUC, 0.948) cohorts. Discrimination performance of the combined model was higher than that of either the radiomics score (AUC, 0.941 in training cohort and 0.852 in validation cohort) or the clinical predictor model (AUC, 0.924 in training cohort and 0.849 in validation cohort). The model stratified patients into groups with low and high risk of malignant fracture with an accuracy of 98.2% in the training cohort and 90.9% in the validation cohort. CONCLUSIONS The combined radiomics-clinical model integrating clinical parameters with radiomics score could predict malignancy in vertebral compression fractures on CT with high discriminatory ability. KEY POINTS • A combined radiomics-clinical model was constructed to predict malignancy of vertebral compression fractures on CT by combining clinical parameters and radiomics features. • The model showed good calibration and discrimination in both training and validation cohorts. • The model showed high accuracy in the stratification of patients into groups with low and high risk of malignant vertebral compression fractures.
Collapse
|
|
30
|
Donners R, Obmann MM, Boll D, Gutzeit A, Harder D. Dixon or DWI - Comparing the utility of fat fraction and apparent diffusion coefficient to distinguish between malignant and acute osteoporotic vertebral fractures. Eur J Radiol 2020; 132:109342. [PMID: 33068837 DOI: 10.1016/j.ejrad.2020.109342] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Revised: 09/02/2020] [Accepted: 10/05/2020] [Indexed: 02/05/2023]
Abstract
PURPOSE To compare fat fraction (FF) and apparent diffusion coefficient (ADC) as discriminators distinguishing malignant from acute/subacute osteoporotic vertebral fractures. METHOD 1.5 T MRIs of 42 malignant and 27 acute/subacute osteoporotic vertebral fractures (38 patients) were retrospectively reviewed. Two readers independently classified fractures as malignant or osteoporotic based on conventional imaging morphology. Diagnostic reader confidence was rated as confident or not confident. FF was derived from axial T1 gradient-echo 2-point Dixon MRI. ADC maps were calculated from axial b50 and b900 images. Both readers independently performed ROI measurements of mean FF and ADC of the same fractured vertebrae. FF and ADC values, corresponding ROC curves and optimized cut-off value performance were compared. Inter-reader agreement was analysed by calculation of intraclass correlation coefficients (ICCs). A p-value < 0.05 was deemed significant. RESULTS Mean FF and ADC were significantly lower in malignant (9.5 % and 1.05 × 10-3 mm²/s) compared to osteoporotic fractures (32 % and 1.34 × 10-3 mm²/s, all p < 0.001). The optimal cut-off FF was 11.5 %, detecting malignant fractures with 86 %/89 % sensitivity/specificity. The optimal ADC cut-off of 1.04 × 10-3 mm/s² yielded 62 %/96 % sensitivity/specificity. FF AUC (0.93) was significantly larger than ADC AUC (0.82, p = 0.03). In the subgroup of nine cases reported with low expert reader confidence, the optimized cut-off specificities of FF (83 %) and ADC (83 %) exceeded reader specificity (50 %). There was excellent inter-reader agreement for mean FF (ICC = 0.99) and good agreement for mean ADC (ICC = 0.86) measurements. CONCLUSION FF and ADC can improve reader specificity to distinguish between malignant and acute or subacute osteoporotic vertebral fractures. As single discriminator, FF was superior to ADC.
Collapse
Affiliation(s)
- Ricardo Donners
- Department of Radiology, University Hospital Basel, University of Basel, Petersgraben 4, CH-4031 Basel, Switzerland.
| | - Markus M Obmann
- Department of Radiology, University Hospital Basel, University of Basel, Petersgraben 4, CH-4031 Basel, Switzerland.
| | - Daniel Boll
- Department of Radiology, University Hospital Basel, University of Basel, Petersgraben 4, CH-4031 Basel, Switzerland.
| | - Andreas Gutzeit
- Institute of Radiology and Nuclear Medicine and Breast Center St. Anna, Hirslanden Klinik St. Anna, St. Anna-Strasse 32, 6006, Lucerne, Switzerland; Department of Chemistry and Applied Biosciences, Institute of Pharmaceutical Sciences, ETH Zurich, Vladimir-Prelog-Weg 1-5 / 10, 8093, Zurich, Switzerland; Department of Radiology, Paracelsus Medical University, Salzburg, Austria.
| | - Dorothee Harder
- Department of Radiology, University Hospital Basel, University of Basel, Petersgraben 4, CH-4031 Basel, Switzerland.
| |
Collapse
|
|
31
|
Bailey S, Hackney D, Vashishth D, Alkalay RN. The effects of metastatic lesion on the structural determinants of bone: Current clinical and experimental approaches. Bone 2020; 138:115159. [PMID: 31759204 PMCID: PMC7531290 DOI: 10.1016/j.bone.2019.115159] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/05/2019] [Revised: 10/31/2019] [Accepted: 11/18/2019] [Indexed: 01/30/2023]
Abstract
Metastatic bone disease is incurable with an associated increase in skeletal-related events, particularly a 17-50% risk of pathologic fractures. Current surgical and oncological treatments are palliative, do not reduce overall mortality, and therefore optimal management of adults at risk of pathologic fractures presents an unmet medical need. Plain radiography lacks specificity and may result in unnecessary prophylactic fixation. Radionuclide imaging techniques primarily supply information on the metabolic activity of the tumor or the bone itself. Magnetic resonance imaging and computed tomography provide excellent anatomical and structural information but do not quantitatively assess bone matrix. Research has now shifted to developing unbiased data-driven tools that can predict risk of impending fractures and guide individualized treatment decisions. This review discusses the state-of-the-art in clinical and experimental approaches for prediction of pathologic fractures with bone metastases. Alterations in bone matrix quality are associated with an age-related increase in skeletal fragility but the impact of metastases on the intrinsic material properties of bone is unclear. Engineering-based analyses are non-invasive with the capability to evaluate oncological treatments and predict failure due to the progression of metastasis. The combination of these approaches may improve our understanding of the underlying deterioration in mechanical performance.
Collapse
Affiliation(s)
- Stacyann Bailey
- Center for Biotechnology and Interdisciplinary Studies, Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY, United States of America
| | - David Hackney
- Department of Radiology, Beth Israel Deaconess Medical Center, Boston, MA 02215, United States of America
| | - Deepak Vashishth
- Center for Biotechnology and Interdisciplinary Studies, Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY, United States of America
| | - Ron N Alkalay
- Center for Advanced Orthopedic Studies, Department of Orthopedic Surgery, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, United States of America.
| |
Collapse
|
|
32
|
Yamamoto Y, Iwata E, Shigematsu H, Morita T, Tanaka M, Okuda A, Masuda K, Ikejiri M, Nakajima H, Koizumi M, Tanaka Y. Differential diagnosis between metastatic and osteoporotic vertebral fractures using sagittal T1-weighted magnetic resonance imaging. J Orthop Sci 2020; 25:763-769. [PMID: 31771804 DOI: 10.1016/j.jos.2019.10.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/01/2019] [Revised: 09/14/2019] [Accepted: 10/02/2019] [Indexed: 11/18/2022]
Abstract
BACKGROUND Magnetic resonance imaging (MRI) is the most helpful for determining the differential diagnosis between metastatic and osteoporotic vertebral fractures; especially whole spine MRI is effective if patients have multiple spinal metastases. However, it is time-consuming to obtain all planes for all metastatic vertebrae. If we can differentiate these metastatic and osteoporotic vertebral fractures based on only one section and signal intensity, it would save time and be effective for patients with pain. This study investigated the usefulness of sagittal T1-weighted MRI findings in differentiating metastatic and osteoporotic vertebral fractures. METHODS We retrospectively reviewed patients diagnosed with metastatic or osteoporotic vertebral fractures. Findings characteristic of metastatic fractures were considered: (a) pedicle or posterior element involvement; (b) convex posterior border of the vertebral body; (c) epidural infiltration; and (d) diffuse homogeneous low signal intensity; findings characteristic of osteoporotic compression fractures were also considered: (e) low-signal-intensity band and (f) posterior retropulsion. Chi-square test or Fisher's exact probability test was used to investigate the usefulness of each MRI finding. Intra- and inter-observer reliability analysis was performed. RESULTS This study comprised 43 patients with metastases (45 vertebrae) and 118 patients with osteoporotic fractures (156 vertebrae). All findings showed significant difference with each fracture (p-value: <0.01 to 0.03). Although each MRI finding exhibited high intra- and inter-observer reliability (κ: 0.66 to 1.00), finding (c) exhibited low reliability. Finding (a) showed high sensitivity (88.9%) and usefulness for screening, and findings (b), (d), (e), and (f) showed high specificity (90.4%-100%) and usefulness for definitive diagnosis. CONCLUSIONS Characteristic findings with sagittal T1-weighted MRI were useful in the differential diagnosis of metastatic and osteoporotic vertebral fractures. To prevent overlooking metastatic fractures with sagittal T1-weighted MRI, findings of the pedicle or posterior element involvement should be focused on because of its reliability and sensitivity.
Collapse
Affiliation(s)
- Yusuke Yamamoto
- Department of Orthopaedic Surgery, Nara Medical University, 840 Shijo-cho, Kashihara-shi, Nara 634-8522, Japan
| | - Eiichiro Iwata
- Department of Orthopaedic Surgery, Nara City Hospital, Nara, Japan.
| | - Hideki Shigematsu
- Department of Orthopaedic Surgery, Nara Medical University, 840 Shijo-cho, Kashihara-shi, Nara 634-8522, Japan
| | - Toshiya Morita
- Department of Orthopaedic Surgery, Kashiba Asahigaoka Hospital, Nara, Japan
| | - Masato Tanaka
- Department of Orthopaedic Surgery, Nara Medical University, 840 Shijo-cho, Kashihara-shi, Nara 634-8522, Japan
| | - Akinori Okuda
- Department of Orthopaedic Surgery, Nara Medical University, 840 Shijo-cho, Kashihara-shi, Nara 634-8522, Japan
| | - Keisuke Masuda
- Department of Orthopaedic Surgery, Nara Medical University, 840 Shijo-cho, Kashihara-shi, Nara 634-8522, Japan
| | - Masaki Ikejiri
- Department of Orthopaedic Surgery, Otemae Hospital, Osaka, Japan
| | - Hiroshi Nakajima
- Department of Orthopaedic Surgery, Otemae Hospital, Osaka, Japan
| | - Munehisa Koizumi
- Department of Spine Surgery, Nara Prefecture General Medical Center, Nara, Japan
| | - Yasuhito Tanaka
- Department of Orthopaedic Surgery, Nara Medical University, 840 Shijo-cho, Kashihara-shi, Nara 634-8522, Japan
| |
Collapse
|
|
33
|
Dormagen JB, Verma N, Fink KR. Imaging in Oncologic Emergencies. Semin Roentgenol 2020; 55:95-114. [PMID: 32438984 DOI: 10.1053/j.ro.2019.11.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
| | - Nupur Verma
- Department of Radiology, University of Florida, Gainesville, FL
| | | |
Collapse
|
|
34
|
What the Intensivists Need to Know About Critically Ill Myeloma Patients. ONCOLOGIC CRITICAL CARE 2020. [PMCID: PMC7121630 DOI: 10.1007/978-3-319-74588-6_98] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
Multiple myeloma (MM) is a hematological malignancy characterized by an increase in aberrant plasma cells in the bone marrow leading to rising monoclonal protein in serum and urine. With the introduction of novel therapies with manageable side effects, this incurable disease has evolved into a chronic disease with an acceptable quality of life for the majority of patients. Accordingly, management of acute complications is fundamental in reducing the morbidity and mortality in MM. MM emergencies include symptoms and signs related directly to the disease and/or to the treatment; many organs may be involved including, but not limited to, renal, cardiovascular, neurologic, hematologic, and infectious complications. This review will focus on the numerous approaches that are aimed at managing these complications.
Collapse
|
|
35
|
Yoon YC. Diffusion-Weighted Magnetic Resonance Imaging of Spine. JOURNAL OF THE KOREAN SOCIETY OF RADIOLOGY 2020; 81:58-69. [PMID: 36238128 PMCID: PMC9432087 DOI: 10.3348/jksr.2020.81.1.58] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Revised: 11/01/2019] [Accepted: 11/25/2019] [Indexed: 11/15/2022]
Abstract
척추영상에 사용되는 확산강조영상의 기술적 측면, 양성과 악성 척추체 압박골절의 구분, 다발성골수종이나 전이암의 발견과 감별진단, 그리고 항암화학요법이나 방사선치료 후 반응 평가에 대해 기술하고자 한다.
Collapse
Affiliation(s)
- Young Cheol Yoon
- School of Medicine, Sungkyunkwan University, Seoul, Korea
- Department of Radiology, Samsung Medical Center, Seoul, Korea
| |
Collapse
|
|
36
|
Tassemeier T, Haversath M, Brandenburger D, Schutzbach M, Serong S, Jäger M. [Atraumatic fractures of the spine : Current strategies for diagnosis and treatment]. DER ORTHOPADE 2019; 48:879-896. [PMID: 31511916 DOI: 10.1007/s00132-019-03804-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Atraumatic fractures of the spine are a common orthopedic disease condition that can be asymptomatic or associated with complaints of varying intensity and quality. The risk factors for such fracture forms are often metabolic and genetic diseases, which have a direct or indirect effect on bone metabolism and therefore secondarily affect the stability of the spinal vertebrae. Furthermore, benign and malignant tumors as well as infectious diseases can also be causative for atraumatic spinal fractures; however, those factors that are attributable to lifestyle habits should also not be underestimated. The treatment of affected patients is complex and nearly always interdisciplinary. In addition to purely symptom-oriented treatment concepts, orthoses in particular and when indicated surgical treatment procedures can be implemented. This article summarizes the important clinical, diagnostic and therapeutic aspects of atraumatic spinal fractures.
Collapse
Affiliation(s)
- T Tassemeier
- Universitätsklinik für Orthopädie und Unfallchirurgie, Universität Duisburg Essen, Essen, Deutschland.
| | - M Haversath
- Universitätsklinik für Orthopädie und Unfallchirurgie, Universität Duisburg Essen, Essen, Deutschland
| | - D Brandenburger
- Universitätsklinik für Orthopädie und Unfallchirurgie, Universität Duisburg Essen, Essen, Deutschland
| | - M Schutzbach
- Universitätsklinik für Orthopädie und Unfallchirurgie, Universität Duisburg Essen, Essen, Deutschland
| | - S Serong
- Klinik für Orthopädie und Orthopädische Chirurgie, Universitätsklinikum des Saarlandes, Homburg, Deutschland
| | - M Jäger
- Universitätsklinik für Orthopädie und Unfallchirurgie, Universität Duisburg Essen, Essen, Deutschland
| |
Collapse
|
|
37
|
Marshall RA, Mandell JC, Weaver MJ, Ferrone M, Sodickson A, Khurana B. Imaging Features and Management of Stress, Atypical, and Pathologic Fractures. Radiographics 2019; 38:2173-2192. [PMID: 30422769 DOI: 10.1148/rg.2018180073] [Citation(s) in RCA: 59] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Traumatic and atraumatic fractures are entities with distinct but often overlapping clinical manifestations, imaging findings, and management protocols. This article is a review of terminology, etiology, and key imaging features that affect management of atraumatic fractures including stress fractures, atypical femoral fractures, and pathologic fractures. The terminology of atraumatic fractures is reviewed, with an emphasis on the distinctions and similarities of stress, atypical, and pathologic fractures. The basic biomechanics of normal bone is described, with an emphasis on the bone remodeling pathway. This framework is used to better convey the shared etiologies, key differences, and important imaging findings of these types of fractures. Next, the characteristic imaging findings of this diverse family of fractures is discussed. For each type of fracture, the most clinically relevant imaging features that guide management by the multidisciplinary treatment team, including orthopedic surgeons, are reviewed. In addition, imaging features are reviewed to help discriminate stress fractures from pathologic fractures in patients with challenging cases. Finally, imaging criteria to risk stratify an impending pathologic fracture at the site of an osseous neoplasm are discussed. Special attention is paid to fractures occurring in the proximal femur because the osseous macrostructure and mix of trabecular and cortical bone of the proximal femur can function as a convenient framework to understanding atraumatic fractures throughout the skeleton. Atraumatic fractures elsewhere in the body also are used to illustrate key imaging features and treatment concepts. ©RSNA, 2018.
Collapse
Affiliation(s)
- Richard A Marshall
- From the Departments of Radiology (R.A.M., J.C.M., A.S., B.K.) and Orthopedic Surgery (M.J.W., M.F.), Brigham and Women's Hospital, 75 Francis St, Boston, MA 02115
| | - Jacob C Mandell
- From the Departments of Radiology (R.A.M., J.C.M., A.S., B.K.) and Orthopedic Surgery (M.J.W., M.F.), Brigham and Women's Hospital, 75 Francis St, Boston, MA 02115
| | - Michael J Weaver
- From the Departments of Radiology (R.A.M., J.C.M., A.S., B.K.) and Orthopedic Surgery (M.J.W., M.F.), Brigham and Women's Hospital, 75 Francis St, Boston, MA 02115
| | - Marco Ferrone
- From the Departments of Radiology (R.A.M., J.C.M., A.S., B.K.) and Orthopedic Surgery (M.J.W., M.F.), Brigham and Women's Hospital, 75 Francis St, Boston, MA 02115
| | - Aaron Sodickson
- From the Departments of Radiology (R.A.M., J.C.M., A.S., B.K.) and Orthopedic Surgery (M.J.W., M.F.), Brigham and Women's Hospital, 75 Francis St, Boston, MA 02115
| | - Bharti Khurana
- From the Departments of Radiology (R.A.M., J.C.M., A.S., B.K.) and Orthopedic Surgery (M.J.W., M.F.), Brigham and Women's Hospital, 75 Francis St, Boston, MA 02115
| |
Collapse
|
|
38
|
Tan H, Xu H, Luo F, Zhang Z, Yang Z, Yu N, Yu Y, Wang S, Fan Q, Li Y. Combined intravoxel incoherent motion diffusion-weighted MR imaging and magnetic resonance spectroscopy in differentiation between osteoporotic and metastatic vertebral compression fractures. J Orthop Surg Res 2019; 14:299. [PMID: 31488174 PMCID: PMC6727483 DOI: 10.1186/s13018-019-1350-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2019] [Accepted: 08/28/2019] [Indexed: 02/07/2023] Open
Abstract
Purpose Our purpose was to combine intravoxel incoherent motion diffusion-weighted MR imaging (IVIM-DWI) and magnetic resonance spectroscopy (MRS) to differentiate osteoporotic fractures from osteolytic metastatic vertebral compression fractures (VCFs). Methods A total of 70 patients with VCFs were included and divided into two groups, according to their causes of fractures based on pathological findings or clinical follow-up. All patients underwent conventional sagittal T1WI, T2WI, STIR, IVIM-DWI, and single-voxel MRS. The diffusion coefficient (D), pseudo diffusion (D*), and perfusion fraction (f) parameters from IVIM-DWI and the lipid water ratio (LWR) and fat fraction (FF) parameters from MRS were obtained and compared among groups. Furthermore, the diagnostic performance of MRS, IVIM-DWI, and IVIM-DWI combined with MRS for differentiation between osteoporotic and osteolytic metastatic VCFs was assessed by using receiver operating characteristic (ROC) curve analysis. Results Compared with the osteoporotic group, the metastatic group had significantly lower values for f, D, and FF, but higher D* (all P < 0.05). The area under the receiver operating characteristic (ROC) curve of MRS, IVIM-DWI, and IVIM-DWI combined with MRS were 0.73, 0.88, and 0.94, respectively. Among these, the IVIM-DWI combined with MRS showed the highest sensitivity, specificity, and accuracy, which are 90.63% (29/32), 97.37 % (37/38), and 94.29% (66/70), respectively. Conclusions IVIM-DWI combined with MRS can be more accurate and efficient for differentiation between osteoporotic and osteolytic metastatic VCFs than single MRS or IVIM-DWI.
Collapse
Affiliation(s)
- Hui Tan
- Institute of Medical Technology, Shaanxi University of Chinese Medicine, Xianyang, China
| | - Hui Xu
- Department of Medical Imaging, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.,Faculty of Dentistry, University of Toronto, Toronto, Ontario, Canada.,Centre for the Study of Pain, University of Toronto, Toronto, Ontario, Canada
| | - Feifei Luo
- Department of Medical Imaging, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.,The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Zhaoguo Zhang
- Institute of Medical Technology, Shaanxi University of Chinese Medicine, Xianyang, China.,Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang, China
| | - Zhen Yang
- Institute of Medical Technology, Shaanxi University of Chinese Medicine, Xianyang, China.,Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang, China
| | - Nan Yu
- Institute of Medical Technology, Shaanxi University of Chinese Medicine, Xianyang, China.,Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang, China
| | - Yong Yu
- Institute of Medical Technology, Shaanxi University of Chinese Medicine, Xianyang, China.,Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang, China
| | | | - Qiuju Fan
- Institute of Medical Technology, Shaanxi University of Chinese Medicine, Xianyang, China. .,Department of Radiology, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang, China.
| | - Yue Li
- Institute of Medical Technology, Shaanxi University of Chinese Medicine, Xianyang, China
| |
Collapse
|
|
39
|
Imai N, Endo N, Shobugawa Y, Oinuma T, Takahashi Y, Suzuki K, Ishikawa Y, Makino T, Suzuki H, Miyasaka D, Sakuma M. Incidence of four major types of osteoporotic fragility fractures among elderly individuals in Sado, Japan, in 2015. J Bone Miner Metab 2019; 37:484-490. [PMID: 29956020 DOI: 10.1007/s00774-018-0937-9] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/01/2017] [Accepted: 05/30/2018] [Indexed: 10/28/2022]
Abstract
The aim of this study was to survey the incidence of osteoporotic fragility fractures, which include vertebral, hip, distal radius, and proximal humerus fractures, in patients ≥ 50 years of age, from 2004 to 2015, in Sado City, Japan. We examined temporal changes in the incidence of these fractures from 2010 through 2015. The incidence of vertebral (p < 0.001) and radius fractures (p = 0.001) was lower in 2015 than in 2010, with only the incidence of hip fracture (p = 0.013) being lower in 2015 than in 2004. With regard to age-specific incidences, there was a sharp increase in vertebral and hip fractures among the segment of the population 70-89 years old, with no remarkable change in the incidence of radial and humeral fractures. Pre-existing vertebral fractures were identified in 69.6% of patients with a hip fracture, 35.6% of patients with a distal radius fracture, and 55% of patients with a humeral fracture. Among patients with pre-existing vertebral fractures, 42.5% had a single fracture, whereas 57.5% had 2 or more fractures. The proportion of patients on anti-osteoporotic agents before the occurrence of fractures increased to 14.5% in 2015, compared to 4% in 2004 and 7.6% in 2010. We speculate that the increase in the use of anti-osteoporotic agents is the main reason for the declining incidence of fractures. Therefore, considering the sharp increase in hip and vertebral fractures among individuals in their mid-1970s and older, judicious use of anti-osteoporotic agents among these individuals could be useful for lowering the occurrence of these fractures.
Collapse
Affiliation(s)
- Norio Imai
- Division of Comprehensive Geriatrics in Community, Niigata University Graduate School of Medical and Dental Sciences, 1-757 Asahimachidori, Niigata, Niigata, 9518510, Japan.
- Division of Orthopedic Surgery, Department of Regenerative and Transplant Medicine, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan.
| | - Naoto Endo
- Division of Orthopedic Surgery, Department of Regenerative and Transplant Medicine, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Yugo Shobugawa
- Division of International Health, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Takeo Oinuma
- Department of Orthopaedic Surgery, Sado General Hospital, Sado, Japan
| | | | - Kazuaki Suzuki
- Department of Orthopaedic Surgery, Sado General Hospital, Sado, Japan
| | - Yuya Ishikawa
- Department of Orthopaedic Surgery, Sado General Hospital, Sado, Japan
| | - Tatsuo Makino
- Department of Orthopaedic Surgery, Sado General Hospital, Sado, Japan
| | - Hayato Suzuki
- Division of Orthopedic Surgery, Department of Regenerative and Transplant Medicine, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
- Department of Orthopaedic Surgery, Sado General Hospital, Sado, Japan
| | - Dai Miyasaka
- Division of Orthopedic Surgery, Department of Regenerative and Transplant Medicine, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Mayumi Sakuma
- Department of Physical Therapy, Faculty of Medical Technology, Niigata University of Health and Welfare, Niigata, Japan
| |
Collapse
|
|
40
|
Reliability and Validity of Different MRI Sequences in Improving the Accuracy of Differential Diagnosis of Benign and Malignant Vertebral Fractures: A Meta-Analysis. AJR Am J Roentgenol 2019; 213:427-436. [PMID: 31039028 DOI: 10.2214/ajr.18.20560] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
OBJECTIVE. We aimed to systematically examine the reliability and validity of different MRI sequences in differentiating benign and malignant vertebral fractures, appropriately select the best MRI sequence to improve the diagnostic accuracy, and compare the diagnostic accuracy of MRI sequences in the context of different study designs or publication date. MATERIALS AND METHODS. Computer and manual retrieval were conducted on studies published between January 1, 2000, and September 31, 2016. Studies relevant to the differential diagnosis of benign and malignant vertebral fractures by MRI and reference standard (histopathologic diagnosis or clinical follow-up examination) were analyzed. RESULTS. Eighteen articles were included. Neither threshold (p = 0.86) nor nonthreshold (p = 0.06) effects were present. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were 89% (95% CI, 86-92%), 88% (95% CI, 85-91%), 6.54 (95% CI, 4.44-9.65), 0.14 (95% CI, 0.09-0.21), and 55.76 (95% CI, 37.06-83.89), respectively. The AUC was 0.95. The risk of publication bias was negligible (p = 0.33). CONCLUSION. MRI sequences could provide appreciable diagnostic performance in differentiating benign and malignant vertebral fractures. However, our pooled estimates do not support the superiority of one set of sequences over another, and there is not sufficient evidence to show that prospective or recent studies are obviously better than retrospective or older studies.
Collapse
|
|
41
|
Park SY, Lee MH, Jeon JY, Chung HW, Lee SH, Shin MJ. MRI Evaluation of Suspected Pathologic Fracture at the Extremities from Metastasis: Diagnostic Value of Added Diffusion-Weighted Imaging. Korean J Radiol 2019; 20:812-822. [PMID: 30993932 PMCID: PMC6470093 DOI: 10.3348/kjr.2018.0545] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2018] [Accepted: 12/04/2018] [Indexed: 12/17/2022] Open
Abstract
Objective To assess the diagnostic value of combining diffusion-weighted imaging (DWI) with conventional magnetic resonance imaging (MRI) for differentiating between pathologic and traumatic fractures at extremities from metastasis. Materials and Methods Institutional Review Board approved this retrospective study and informed consent was waived. This study included 49 patients each with pathologic and traumatic fractures at extremities. The patients underwent conventional MRI combined with DWI. For qualitative analysis, two radiologists (R1 and R2) independently reviewed three imaging sets with a crossover design using a 5-point scale and a 3-scale confidence level: DWI plus non-enhanced MRI (NEMR; DW set), NEMR plus contrast-enhanced fat-saturated T1-weighted imaging (CEFST1; CE set), and DWI plus NEMR plus CEFST1 (combined set). McNemar's test was used to compare the diagnostic performances among three sets and perform subgroup analyses (single vs. multiple bone abnormality, absence/presence of extra-osseous mass, and bone enhancement at fracture margin). Results Compared to the CE set, the combined set showed improved diagnostic accuracy (R1, 84.7 vs. 95.9%; R2, 91.8 vs. 95.9%, p < 0.05) and specificity (R1, 71.4% vs. 93.9%, p < 0.005; R2, 85.7% vs. 98%, p = 0.07), with no difference in sensitivities (p > 0.05). In cases of absent extra-osseous soft tissue mass and present fracture site enhancement, the combined set showed improved accuracy (R1, 82.9–84.4% vs. 95.6–96.3%, p < 0.05; R2, 90.2–91.1% vs. 95.1–95.6%, p < 0.05) and specificity (R1, 68.3–72.9% vs. 92.7–95.8%, p < 0.005; R2, 83.0–85.4% vs. 97.6–98.0%, p = 0.07). Conclusion Combining DWI with conventional MRI improved the diagnostic accuracy and specificity while retaining sensitivity for differentiating between pathologic and traumatic fractures from metastasis at extremities.
Collapse
Affiliation(s)
- Sun Young Park
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.,Department of Radiology, Hallym University Sacred Heart Hospital, Anyang, Korea
| | - Min Hee Lee
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.
| | - Ji Young Jeon
- Department of Radiology, Gil Medical Center, Gachon University College of Medicine, Incheon, Korea
| | - Hye Won Chung
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Sang Hoon Lee
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Myung Jin Shin
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| |
Collapse
|
|
42
|
Burian E, Rohrmeier A, Schlaeger S, Dieckmeyer M, Diefenbach MN, Syväri J, Klupp E, Weidlich D, Zimmer C, Rummeny EJ, Karampinos DC, Kirschke JS, Baum T. Lumbar muscle and vertebral bodies segmentation of chemical shift encoding-based water-fat MRI: the reference database MyoSegmenTUM spine. BMC Musculoskelet Disord 2019; 20:152. [PMID: 30961552 PMCID: PMC6454744 DOI: 10.1186/s12891-019-2528-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/14/2018] [Accepted: 03/24/2019] [Indexed: 12/17/2022] Open
Abstract
Background Magnetic resonance imaging (MRI) is the modality of choice for diagnosing and monitoring muscular tissue pathologies and bone marrow alterations in the context of lower back pain, neuromuscular diseases and osteoporosis. Chemical shift encoding-based water-fat MRI allows for reliable determination of proton density fat fraction (PDFF) of the muscle and bone marrow. Prior to quantitative data extraction, segmentation of the examined structures is needed. Performed manually, the segmentation process is time consuming and therefore limiting the clinical applicability. Thus, the development of automated segmentation algorithms is an ongoing research focus. Construction and content This database provides ground truth data which may help to develop and test automatic lumbar muscle and vertebra segmentation algorithms. Lumbar muscle groups and vertebral bodies (L1 to L5) were manually segmented in chemical shift encoding-based water-fat MRI and made publically available in the database MyoSegmenTUM. The database consists of water, fat and PDFF images with corresponding segmentation masks for lumbar muscle groups (right/left erector spinae and psoas muscles, respectively) and lumbar vertebral bodies 1–5 of 54 healthy Caucasian subjects. The database is freely accessible online at https://osf.io/3j54b/?view_only=f5089274d4a449cda2fef1d2df0ecc56. Conclusion A development and testing of segmentation algorithms based on this database may allow the use of quantitative MRI in clinical routine.
Collapse
Affiliation(s)
- Egon Burian
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technische Universität München, Munich, Germany.
| | - Alexander Rohrmeier
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Sarah Schlaeger
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technische Universität München, Munich, Germany.,Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Michael Dieckmeyer
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technische Universität München, Munich, Germany.,Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Maximilian N Diefenbach
- Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Jan Syväri
- Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Elisabeth Klupp
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Dominik Weidlich
- Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Claus Zimmer
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Ernst J Rummeny
- Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Dimitrios C Karampinos
- Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Jan S Kirschke
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Thomas Baum
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| |
Collapse
|
|
43
|
|
|
44
|
Abstract
BACKGROUND Radiological imaging is important in the preoperative diagnosis of many forms of spinal pathology and plays a fundamental role in the assessment of p.o. effects, which can be verified on the spinal column as well as on the surrounding soft tissues, depending on the imaging method used. AIM The article provides an overview of the current status and possibilities of radiological diagnostic methods for the verification of possibly recommended spine surgery in the context of degenerative, inflammatory-infectious, post-traumatic or p.o. pathologies and changes in the spine: X‑rays, computed tomography (CT), magnetic resonance imaging (MRI). The supplementary nuclear medicine procedures (scintigraphy, PET[-CT], SPECT, etc.) which may be required for special questions are not discussed. MATERIAL AND METHODS The merits and limitations of the techniques used in the investigation of advanced degenerative spinal pathologies and post-traumatic conditions are discussed, with multidetector CT being the focus of attention in spinal clearance for traumatic injuries. In most cases of spinal infection, MRI images, as a central diagnostic tool, show typical findings such as destruction of adjacent endplates, bone marrow and intervertebral disc abnormalities, and paravertebral or epidural abscesses. However, it is not always easy to diagnose a spinal infection, especially if atypical MR patterns of infectious spondylitis are present. Knowledge of them means misdiagnosis and improper treatment can be avoided. RESULTS It is shown that high-quality modern radiological examinations are essential for diagnosis and p.o. management, as these provide answers to the main questions in the treatment: Is the entity/injury stable or unstable, acute or old, benign or malign; is there a myelopathy or p.o. complication? DISCUSSION The main indications for p.o. diagnostic imaging, difficulties such as metal artefact formation, and potential pitfalls are analyzed. Entity-specific radiological image patterns, imaging algorithms and differential diagnostic peculiarities are presented and discussed based on current literature and selected case studies.
Collapse
Affiliation(s)
- Uwe H W Schütz
- Klinik für Diagnostische und Interventionelle Radiologie, Universitätsklinikum Ulm, Albert-Einstein-Allee 23, 89081, Ulm, Deutschland. .,Orthopädie und Schmerzmedizin am Grünen Turm, Grüner-Turm-Str. 4-10, 88212, Ravensburg, Deutschland.
| |
Collapse
|
|
45
|
Abstract
Acute low back pain, defined as less than 6 weeks in duration, does not require imaging in the absence of "red flags" that may indicate a cause, such as fracture, infection, or malignancy. When imaging is indicated, it is important to rule out a host of abnormalities that may be responsible for the pain and any associated symptoms. A common mnemonic VINDICATE can help ensure a thorough consideration of the possible causes.
Collapse
Affiliation(s)
- Scott M Johnson
- Department of Radiology and Imaging Sciences, University of Utah, 30 North 1900 East, Room 1A71, Salt Lake City, UT 84132, USA
| | - Lubdha M Shah
- Department of Radiology and Imaging Sciences, University of Utah, 30 North 1900 East, Room 1A71, Salt Lake City, UT 84132, USA.
| |
Collapse
|
|
46
|
Lavi ES, Pal A, Bleicher D, Kang K, Sidani C. MR Imaging of the Spine: Urgent and Emergent Indications. Semin Ultrasound CT MR 2018; 39:551-569. [DOI: 10.1053/j.sult.2018.10.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
|
|
47
|
Li Z, Guan M, Sun D, Xu Y, Li F, Xiong W. A novel MRI- and CT-based scoring system to differentiate malignant from osteoporotic vertebral fractures in Chinese patients. BMC Musculoskelet Disord 2018; 19:406. [PMID: 30458738 PMCID: PMC6247741 DOI: 10.1186/s12891-018-2331-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/18/2018] [Accepted: 10/30/2018] [Indexed: 11/16/2022] Open
Abstract
Background Various types of magnetic resonance imaging (MRI) and computed tomography (CT) findings are used to differentiate malignant vertebral fractures (MVFs) from osteoporotic vertebral fractures (OVFs). The distinguishing ability of any single finding is limited. This study developed a novel scoring system that integrates multiple MRI and CT signs for improved accuracy of differential diagnosis between MVFs and OVFs. Methods A total of 150 MVFs and 150 OVFs in thoracolumbar vertebrae were analyzed. MRI and CT images were obtained within 2 months of the probable time of fracture. The sensitivity and specificity of 15 MRI and CT image findings were evaluated. A stepwise discriminant analysis using these signs as variables was used to create a scoring system to differentiate MVFs from OVFs. Results All 15 image findings had strong specificity and moderate sensitivity. Seven MRI and three CT image findings were selected and assigned integral values in the final scoring system. A total score of 4 or greater points indicated MVF, whereas a total score of 3 or fewer points indicated OVF. The classification accuracy was 98.3% in the test set. Conclusions This novel scoring system using MRI and CT radiologic findings to differentiate MVFs from OVFs in Chinese patients was efficient with high accuracy and good applicability.
Collapse
Affiliation(s)
- Zi Li
- Department of orthopedics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095#, Jiefang Ave, Wuhan, Hubei, China.,Department of orthopedics, Taikang Tongji Hospital, Wuhan, Hubei, China
| | - Ming Guan
- Department of orthopedics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095#, Jiefang Ave, Wuhan, Hubei, China
| | - Dong Sun
- Radiology department, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095#, Jiefang Ave, Wuhan, Hubei, China
| | - Yong Xu
- Department of orthopedics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095#, Jiefang Ave, Wuhan, Hubei, China
| | - Feng Li
- Department of orthopedics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095#, Jiefang Ave, Wuhan, Hubei, China
| | - Wei Xiong
- Department of orthopedics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095#, Jiefang Ave, Wuhan, Hubei, China.
| |
Collapse
|
|
48
|
Besa P, Urrutia J, Campos M, Mobarec S, Cruz JP, Cikutovic P, Diaz G. The META score for differentiating metastatic from osteoporotic vertebral fractures: an independent agreement assessment. Spine J 2018; 18:2074-2080. [PMID: 29709548 DOI: 10.1016/j.spinee.2018.04.024] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/16/2018] [Revised: 04/18/2018] [Accepted: 04/20/2018] [Indexed: 02/03/2023]
Abstract
BACKGROUND CONTEXT Differentiating osteoporotic vertebral fractures (OVFs) from metastatic vertebral fractures (MVFs) is an important clinical challenge. A novel magnetic resonance imaging (MRI)-based score (the META score) was described, aiming to differentiate OVF from MVF. This score showed an almost perfect agreement by the group developing it, but an independent agreement evaluation is pending. PURPOSE We aimed to perform an independent inter- and intraobserver agreement evaluation of the META score and to test the score's capability of differentiating OVF from MVF. STUDY DESIGN This is an agreement study of the META score. METHODS Sixty-four patients with confirmed OVF or MVF were assessed by six independent evaluators (three spine surgeons and three fellowship-trained radiologists) using the META score. We used the intraclass correlation coefficient (ICC) to determine the overall inter-and intraobserver agreement, and the kappa statistic (κ) to express the agreement for each individual score criterion. The score accuracy was determined by calculating the area under the receiver operating characteristic curve. Finally, we used κ to evaluate the agreement among raters to determine whether the fracture was OVF or MVF. RESULTS The overall interobserver agreement was poor [ICC=0.10 (0.02-0.20)]; spine surgeons [ICC=0.75 (0.66-0.83)] had better agreement than radiologists did [ICC=0.05 (-0.08 to 0.21)]. The intraobserver agreement was poor [ICC=0.17 (0.01-0.32)]; both spine surgeons [ICC=0.21 (0.05-0.41)] and radiologists had a poor agreement [ICC=0.03 (-0.29 to 0.27)]. The agreement for each specific criterion varied from κ=0.24 to κ=0.60. The area under the receiver operating characteristic curve was 0.58 (0.64 for spine surgeons and 0.52 for radiologists, p<.01). CONCLUSIONS The interobserver agreement using the META score was adequate for spine surgeons but not for other potential users (radiologists); the intraobserver agreement was poor. Further studies are thus necessary before the use of this score is recommended.
Collapse
Affiliation(s)
- Pablo Besa
- Department of Orthopedic Surgery, School of Medicine, Pontificia Universidad Catolica de Chile, Diagonal Paraguay 362, Santiago, Chile 8330077
| | - Julio Urrutia
- Department of Orthopedic Surgery, School of Medicine, Pontificia Universidad Catolica de Chile, Diagonal Paraguay 362, Santiago, Chile 8330077.
| | - Mauricio Campos
- Department of Orthopedic Surgery, School of Medicine, Pontificia Universidad Catolica de Chile, Diagonal Paraguay 362, Santiago, Chile 8330077
| | - Sebastián Mobarec
- Department of Orthopedic Surgery, School of Medicine, Pontificia Universidad Catolica de Chile, Diagonal Paraguay 362, Santiago, Chile 8330077
| | - Juan Pablo Cruz
- Department of Radiology, School of Medicine, Pontificia Universidad Catolica de Chile, Marcoleta 352, Santiago, Chile 8330033
| | - Pablo Cikutovic
- Department of Radiology, School of Medicine, Pontificia Universidad Catolica de Chile, Marcoleta 352, Santiago, Chile 8330033
| | - Gonzalo Diaz
- Department of Radiology, School of Medicine, Pontificia Universidad Catolica de Chile, Marcoleta 352, Santiago, Chile 8330033
| |
Collapse
|
|
49
|
Diagnostic Performance of In-Phase and Opposed-Phase Chemical-Shift Imaging for Differentiating Benign and Malignant Vertebral Marrow Lesions: A Meta-Analysis. AJR Am J Roentgenol 2018; 211:W188-W197. [DOI: 10.2214/ajr.17.19306] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
|
|
50
|
Schmeel FC, Luetkens JA, Feißt A, Enkirch SJ, Endler CHJ, Wagenhäuser PJ, Schmeel LC, Träber F, Schild HH, Kukuk GM. Quantitative evaluation of T2* relaxation times for the differentiation of acute benign and malignant vertebral body fractures. Eur J Radiol 2018; 108:59-65. [PMID: 30396672 DOI: 10.1016/j.ejrad.2018.09.021] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2018] [Accepted: 09/17/2018] [Indexed: 12/27/2022]
Abstract
OBJECTIVES The aim of this prospective study was to evaluate the diagnostic performance of T2*-weighted magnetic resonance imaging (MRI) to differentiate between acute benign and neoplastic vertebral compression fractures (VCFs). MATERIALS AND METHODS Thirty-seven consecutive patients with a total of 52 VCFs were prospectively enrolled in this IRB approved study. All VCFs were categorized as either benign or malignant according to direct bone biopsy and histopathologic confirmation. In addition to routine clinical spine MRI including at least sagittal T1-weighted, T2-weighted and T2 spectral attenuated inversion recovery (SPAIR)-weighted sequences, all patients underwent an additional sagittal six-echo modified Dixon gradient-echo sequence of the spine at 3.0-T. Intravertebral T2* and T2*ratio (fracture T2*/normal vertebrae T2*) for acute benign and malignant VCFs were calculated using region-of-interest analysis and compared between both groups. Additional receiver operating characteristic analyses were performed. Five healthy subjects were scanned three times to determine the short-term reproducibility of vertebral T2* measurements. RESULTS There were 27 acute benign and 25 malignant VCFs. Both T2* and T2*ratio of malignant VCFs were significantly higher compared to acute benign VCFs (T2*, 30 ± 11 vs. 19 ± 11 ms [p = 0.001]; T2*ratio, 2.9 ± 1.6 vs. 1.2 ± 0.7 [p < 0.001]). The areas under the curve were 0.77 for T2* and 0.88 for T2*ratio, yielding an accuracy of 73% and 89% for distinguishing acute benign from malignant VCFs. The root mean square absolute precision error was 0.44 ms as a measure for the T2* short-term reproducibility. CONCLUSION Quantitative assessment of vertebral bone marrow T2* relaxation times provides good diagnostic accuracy for the differentiation of acute benign and malignant VCFs.
Collapse
Affiliation(s)
- Frederic Carsten Schmeel
- Department of Radiology and Radiation Oncology, University Hospital Bonn, Rheinische-Friedrich-Wilhelms-Universität Bonn, Bonn, Germany.
| | - Julian Alexander Luetkens
- Department of Radiology and Radiation Oncology, University Hospital Bonn, Rheinische-Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Andreas Feißt
- Department of Radiology and Radiation Oncology, University Hospital Bonn, Rheinische-Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Simon Jonas Enkirch
- Department of Radiology and Radiation Oncology, University Hospital Bonn, Rheinische-Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Christoph Hans-Jürgen Endler
- Department of Radiology and Radiation Oncology, University Hospital Bonn, Rheinische-Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Peter Johannes Wagenhäuser
- Department of Radiology and Radiation Oncology, University Hospital Bonn, Rheinische-Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Leonard Christopher Schmeel
- Department of Radiology and Radiation Oncology, University Hospital Bonn, Rheinische-Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Frank Träber
- Department of Radiology and Radiation Oncology, University Hospital Bonn, Rheinische-Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Hans Heinz Schild
- Department of Radiology and Radiation Oncology, University Hospital Bonn, Rheinische-Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Guido Matthias Kukuk
- Department of Radiology and Radiation Oncology, University Hospital Bonn, Rheinische-Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| |
Collapse
|