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Praveen AD, Sollmann N, Baum T, Ferguson SJ, Benedikt H. CT image-based biomarkers for opportunistic screening of osteoporotic fractures: a systematic review and meta-analysis. Osteoporos Int 2024; 35:971-996. [PMID: 38353706 PMCID: PMC11136833 DOI: 10.1007/s00198-024-07029-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/17/2023] [Accepted: 01/19/2024] [Indexed: 05/30/2024]
Abstract
The use of opportunistic computed tomography (CT) image-based biomarkers may be a low-cost strategy for screening older individuals at high risk for osteoporotic fractures and populations that are not sufficiently targeted. This review aimed to assess the discriminative ability of image-based biomarkers derived from existing clinical routine CT scans for hip, vertebral, and major osteoporotic fracture prediction. A systematic search in PubMed MEDLINE, Embase, Cochrane, and Web of Science was conducted from the earliest indexing date until July 2023. The evaluation of study quality was carried out using a modified Quality Assessment Tool for Diagnostic Accuracy Studies (QUADAS-2) checklist. The primary outcome of interest was the area under the curve (AUC) and its corresponding 95% confidence intervals (CIs) obtained for four main categories of biomarkers: areal bone mineral density (BMD), image attenuation, volumetric BMD, and finite element (FE)-derived biomarkers. The meta-analyses were performed using random effects models. Sixty-one studies were included in this review, among which 35 were synthesized in a meta-analysis and the remaining articles were qualitatively synthesized. In comparison to the pooled AUC of areal BMD (0.73 [95% CI 0.71-0.75]), the pooled AUC values for predicting osteoporotic fractures for FE-derived parameters (0.77 [95% CI 0.72-0.81]; p < 0.01) and volumetric BMD (0.76 [95% CI 0.71-0.81]; p < 0.01) were significantly higher, but there was no significant difference with the pooled AUC for image attenuation (0.73 [95% CI 0.66-0.79]; p = 0.93). Compared to areal BMD, volumetric BMD and FE-derived parameters may provide a significant improvement in the discrimination of osteoporotic fractures using opportunistic CT assessments.
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Affiliation(s)
- Anitha D Praveen
- Early Detection of Health Risks and Prevention, Future Health Technologies, Singapore-ETH Centre (SEC), Campus for Research Excellence and Technological Enterprise (CREATE), 1 Create Way, CREATE Tower, #06-01, Singapore, 138602, Singapore.
| | - Nico Sollmann
- Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Ulm, Germany
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Thomas Baum
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Stephen J Ferguson
- Early Detection of Health Risks and Prevention, Future Health Technologies, Singapore-ETH Centre (SEC), Campus for Research Excellence and Technological Enterprise (CREATE), 1 Create Way, CREATE Tower, #06-01, Singapore, 138602, Singapore
- Institute for Biomechanics, ETH-Zurich, Zurich, Switzerland
| | - Helgason Benedikt
- Early Detection of Health Risks and Prevention, Future Health Technologies, Singapore-ETH Centre (SEC), Campus for Research Excellence and Technological Enterprise (CREATE), 1 Create Way, CREATE Tower, #06-01, Singapore, 138602, Singapore
- Institute for Biomechanics, ETH-Zurich, Zurich, Switzerland
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2
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Giorgini F, Di Dalmazi G, Diciotti S. Artificial intelligence in endocrinology: a comprehensive review. J Endocrinol Invest 2024; 47:1067-1082. [PMID: 37971630 PMCID: PMC11035463 DOI: 10.1007/s40618-023-02235-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Accepted: 10/24/2023] [Indexed: 11/19/2023]
Abstract
BACKGROUND AND AIM Artificial intelligence (AI) has emerged as a promising technology in the field of endocrinology, offering significant potential to revolutionize the diagnosis, treatment, and management of endocrine disorders. This comprehensive review aims to provide a concise overview of the current landscape of AI applications in endocrinology and metabolism, focusing on the fundamental concepts of AI, including machine learning algorithms and deep learning models. METHODS The review explores various areas of endocrinology where AI has demonstrated its value, encompassing screening and diagnosis, risk prediction, translational research, and "pre-emptive medicine". Within each domain, relevant studies are discussed, offering insights into the methodology and main findings of AI in the treatment of different pathologies, such as diabetes mellitus and related disorders, thyroid disorders, adrenal tumors, and bone and mineral disorders. RESULTS Collectively, these studies show the valuable contributions of AI in optimizing healthcare outcomes and unveiling new understandings of the intricate mechanisms underlying endocrine disorders. Furthermore, AI-driven approaches facilitate the development of precision medicine strategies, enabling tailored interventions for patients based on their individual characteristics and needs. CONCLUSIONS By embracing AI in endocrinology, a future can be envisioned where medical professionals and AI systems synergistically collaborate, ultimately enhancing the lives of individuals affected by endocrine disorders.
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Affiliation(s)
- F Giorgini
- Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, Cesena, Italy
- Division of Endocrinology and Diabetes Prevention and Care, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - G Di Dalmazi
- Division of Endocrinology and Diabetes Prevention and Care, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
- Department of Medical and Surgical Sciences (DIMEC), Alma Mater Studiorum University of Bologna, Bologna, Italy
| | - S Diciotti
- Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, Cesena, Italy.
- Alma Mater Research Institute for Human-Centered Artificial Intelligence, University of Bologna, Bologna, Italy.
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Liu J, Wang H, Shan X, Zhang L, Cui S, Shi Z, Liu Y, Zhang Y, Wang L. Hybrid transformer convolutional neural network-based radiomics models for osteoporosis screening in routine CT. BMC Med Imaging 2024; 24:62. [PMID: 38486185 PMCID: PMC10938662 DOI: 10.1186/s12880-024-01240-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 03/06/2024] [Indexed: 03/18/2024] Open
Abstract
OBJECTIVE Early diagnosis of osteoporosis is crucial to prevent osteoporotic vertebral fracture and complications of spine surgery. We aimed to conduct a hybrid transformer convolutional neural network (HTCNN)-based radiomics model for osteoporosis screening in routine CT. METHODS To investigate the HTCNN algorithm for vertebrae and trabecular segmentation, 92 training subjects and 45 test subjects were employed. Furthermore, we included 283 vertebral bodies and randomly divided them into the training cohort (n = 204) and test cohort (n = 79) for radiomics analysis. Area receiver operating characteristic curves (AUCs) and decision curve analysis (DCA) were applied to compare the performance and clinical value between radiomics models and Hounsfield Unit (HU) values to detect dual-energy X-ray absorptiometry (DXA) based osteoporosis. RESULTS HTCNN algorithm revealed high precision for the segmentation of the vertebral body and trabecular compartment. In test sets, the mean dice scores reach 0.968 and 0.961. 12 features from the trabecular compartment and 15 features from the entire vertebral body were used to calculate the radiomics score (rad score). Compared with HU values and trabecular rad-score, the vertebrae rad-score suggested the best efficacy for osteoporosis and non-osteoporosis discrimination (training group: AUC = 0.95, 95%CI 0.91-0.99; test group: AUC = 0.97, 95%CI 0.93-1.00) and the differences were significant in test group according to the DeLong test (p < 0.05). CONCLUSIONS This retrospective study demonstrated the superiority of the HTCNN-based vertebrae radiomics model for osteoporosis discrimination in routine CT.
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Affiliation(s)
- Jiachen Liu
- Department of Orthopedics, Shengjing Hospital of China Medical University, 110004, Shenyang, People's Republic of China
| | - Huan Wang
- Department of Orthopedics, Shengjing Hospital of China Medical University, 110004, Shenyang, People's Republic of China
| | - Xiuqi Shan
- Department of Orthopedics, Shengjing Hospital of China Medical University, 110004, Shenyang, People's Republic of China
| | - Lei Zhang
- Department of Orthopedics, Shengjing Hospital of China Medical University, 110004, Shenyang, People's Republic of China
| | - Shaoqian Cui
- Department of Orthopedics, Shengjing Hospital of China Medical University, 110004, Shenyang, People's Republic of China
| | - Zelin Shi
- Shenyang Institute of Automation, Chinese Academy of Sciences, 110016, Shenyang, People's Republic of China
| | - Yunpeng Liu
- Shenyang Institute of Automation, Chinese Academy of Sciences, 110016, Shenyang, People's Republic of China
| | - Yingdi Zhang
- Shenyang Institute of Automation, Chinese Academy of Sciences, 110016, Shenyang, People's Republic of China
| | - Lanbo Wang
- Department of Radiology, Shengjing Hospital of China Medical University, 110004, Shenyang, People's Republic of China.
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Maki S, Furuya T, Inoue M, Shiga Y, Inage K, Eguchi Y, Orita S, Ohtori S. Machine Learning and Deep Learning in Spinal Injury: A Narrative Review of Algorithms in Diagnosis and Prognosis. J Clin Med 2024; 13:705. [PMID: 38337399 PMCID: PMC10856760 DOI: 10.3390/jcm13030705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 01/14/2024] [Accepted: 01/18/2024] [Indexed: 02/12/2024] Open
Abstract
Spinal injuries, including cervical and thoracolumbar fractures, continue to be a major public health concern. Recent advancements in machine learning and deep learning technologies offer exciting prospects for improving both diagnostic and prognostic approaches in spinal injury care. This narrative review systematically explores the practical utility of these computational methods, with a focus on their application in imaging techniques such as computed tomography (CT) and magnetic resonance imaging (MRI), as well as in structured clinical data. Of the 39 studies included, 34 were focused on diagnostic applications, chiefly using deep learning to carry out tasks like vertebral fracture identification, differentiation between benign and malignant fractures, and AO fracture classification. The remaining five were prognostic, using machine learning to analyze parameters for predicting outcomes such as vertebral collapse and future fracture risk. This review highlights the potential benefit of machine learning and deep learning in spinal injury care, especially their roles in enhancing diagnostic capabilities, detailed fracture characterization, risk assessments, and individualized treatment planning.
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Affiliation(s)
- Satoshi Maki
- Department of Orthopaedic Surgery, Graduate School of Medicine, Chiba University, Chiba 260-8670, Japan
- Center for Frontier Medical Engineering, Chiba University, Chiba 263-8522, Japan
| | - Takeo Furuya
- Department of Orthopaedic Surgery, Graduate School of Medicine, Chiba University, Chiba 260-8670, Japan
| | - Masahiro Inoue
- Department of Orthopaedic Surgery, Graduate School of Medicine, Chiba University, Chiba 260-8670, Japan
| | - Yasuhiro Shiga
- Department of Orthopaedic Surgery, Graduate School of Medicine, Chiba University, Chiba 260-8670, Japan
| | - Kazuhide Inage
- Department of Orthopaedic Surgery, Graduate School of Medicine, Chiba University, Chiba 260-8670, Japan
| | - Yawara Eguchi
- Department of Orthopaedic Surgery, Graduate School of Medicine, Chiba University, Chiba 260-8670, Japan
| | - Sumihisa Orita
- Department of Orthopaedic Surgery, Graduate School of Medicine, Chiba University, Chiba 260-8670, Japan
- Center for Frontier Medical Engineering, Chiba University, Chiba 263-8522, Japan
| | - Seiji Ohtori
- Department of Orthopaedic Surgery, Graduate School of Medicine, Chiba University, Chiba 260-8670, Japan
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Dhanagopal R, Menaka R, Suresh Kumar R, Vasanth Raj PT, Debrah EL, Pradeep K. Channel-Boosted and Transfer Learning Convolutional Neural Network-Based Osteoporosis Detection from CT Scan, Dual X-Ray, and X-Ray Images. JOURNAL OF HEALTHCARE ENGINEERING 2024; 2024:3733705. [PMID: 38223259 PMCID: PMC10783982 DOI: 10.1155/2024/3733705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 04/06/2022] [Accepted: 04/15/2022] [Indexed: 01/16/2024]
Abstract
Osteoporosis is a word used to describe a condition in which bone density has been diminished as a result of inadequate bone tissue development to counteract the elimination of old bone tissue. Osteoporosis diagnosis is made possible by the use of medical imaging technologies such as CT scans, dual X-ray, and X-ray images. In practice, there are various osteoporosis diagnostic methods that may be performed with a single imaging modality to aid in the diagnosis of the disease. The proposed study is to develop a framework, that is, to aid in the diagnosis of osteoporosis which agrees to all of these CT scans, X-ray, and dual X-ray imaging modalities. The framework will be implemented in the near future. The proposed work, CBTCNNOD, is the integration of 3 functional modules. The functional modules are a bilinear filter, grey-level zone length matrix, and CB-CNN. It is constructed in a manner that can provide crisp osteoporosis diagnostic reports based on the images that are fed into the system. All 3 modules work together to improve the performance of the proposed approach, CBTCNNOD, in terms of accuracy by 10.38%, 10.16%, 7.86%, and 14.32%; precision by 11.09%, 9.08%, 10.01%, and 16.51%; sensitivity by 9.77%, 10.74%, 6.20%, and 12.78%; and specificity by 11.01%, 9.52%, 9.5%, and 15.84%, while requiring less processing time of 33.52%, 17.79%, 23.34%, and 10.86%, when compared to the existing techniques of RCETA, BMCOFA, BACBCT, and XSFCV, respectively.
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Affiliation(s)
- R. Dhanagopal
- Centre for System Design, Chennai Institute of Technology, Chennai, Tamil Nadu, India
| | - R. Menaka
- Centre for System Design, Chennai Institute of Technology, Chennai, Tamil Nadu, India
| | - R. Suresh Kumar
- Centre for System Design, Chennai Institute of Technology, Chennai, Tamil Nadu, India
| | - P. T. Vasanth Raj
- Centre for System Design, Chennai Institute of Technology, Chennai, Tamil Nadu, India
| | - E. L. Debrah
- Biomedical Engineering Technology, Koforidua Technical University, Koforidua, Eastern Region, Ghana
| | - K. Pradeep
- Department of Biomedical Engineering, Chennai Institute of Technology, Chennai, Tamil Nadu, India
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Nicolaes J, Liu Y, Zhao Y, Huang P, Wang L, Yu A, Dunkel J, Libanati C, Cheng X. External validation of a convolutional neural network algorithm for opportunistically detecting vertebral fractures in routine CT scans. Osteoporos Int 2024; 35:143-152. [PMID: 37674097 PMCID: PMC10786735 DOI: 10.1007/s00198-023-06903-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 08/29/2023] [Indexed: 09/08/2023]
Abstract
The Convolutional Neural Network algorithm achieved a sensitivity of 94% and specificity of 93% in identifying scans with vertebral fractures (VFs). The external validation results suggest that the algorithm provides an opportunity to aid radiologists with the early identification of VFs in routine CT scans of abdomen and chest. PURPOSE To evaluate the performance of a previously trained Convolutional Neural Network (CNN) model to automatically detect vertebral fractures (VFs) in CT scans in an external validation cohort. METHODS Two Chinese studies and clinical data were used to retrospectively select CT scans of the chest, abdomen and thoracolumbar spine in men and women aged ≥50 years. The CT scans were assessed using the semiquantitative (SQ) Genant classification for prevalent VFs in a process blinded to clinical information. The performance of the CNN model was evaluated against reference standard readings by the area under the receiver operating characteristics curve (AUROC), accuracy, Cohen's kappa, sensitivity, and specificity. RESULTS A total of 4,810 subjects were included, with a median age of 62 years (IQR 56-67), of which 2,654 (55.2%) were females. The scans were acquired between January 2013 and January 2019 on 16 different CT scanners from three different manufacturers. 2,773 (57.7%) were abdominal CTs. A total of 628 scans (13.1%) had ≥1 VF (grade 2-3), representing 899 fractured vertebrae out of a total of 48,584 (1.9%) visualized vertebral bodies. The CNN's performance in identifying scans with ≥1 moderate or severe fractures achieved an AUROC of 0.94 (95% CI: 0.93-0.95), accuracy of 93% (95% CI: 93%-94%), kappa of 0.75 (95% CI: 0.72-0.77), a sensitivity of 94% (95% CI: 92-96%) and a specificity of 93% (95% CI: 93-94%). CONCLUSION The algorithm demonstrated excellent performance in the identification of vertebral fractures in a cohort of chest and abdominal CT scans of Chinese patients ≥50 years.
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Affiliation(s)
- Joeri Nicolaes
- Department of Electrical Engineering (ESAT), Center for Processing Speech and Images, KU Leuven, Leuven, Belgium.
- UCB Pharma, Brussels, Belgium.
| | - Yandong Liu
- Department of Radiology, Beijing Jishuitan Hospital, Beijing, 100035, China
| | - Yue Zhao
- Department of Radiology, Qingdao Fuwaicardiovascular Hospital, Qingdao, 26600, China
| | - Pengju Huang
- Department of Radiology, Beijing Anding Hospital, Beijing, 100120, China
| | - Ling Wang
- Department of Radiology, Beijing Jishuitan Hospital, Beijing, 100035, China
| | - Aihong Yu
- Department of Radiology, Beijing Anding Hospital, Beijing, 100120, China
| | | | | | - Xiaoguang Cheng
- Department of Radiology, Beijing Jishuitan Hospital, Beijing, 100035, China
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7
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Nicolaes J, Skjødt MK, Raeymaeckers S, Smith CD, Abrahamsen B, Fuerst T, Debois M, Vandermeulen D, Libanati C. Towards Improved Identification of Vertebral Fractures in Routine Computed Tomography (CT) Scans: Development and External Validation of a Machine Learning Algorithm. J Bone Miner Res 2023; 38:1856-1866. [PMID: 37747147 DOI: 10.1002/jbmr.4916] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 09/06/2023] [Accepted: 09/17/2023] [Indexed: 09/26/2023]
Abstract
Vertebral fractures (VFs) are the hallmark of osteoporosis, being one of the most frequent types of fragility fracture and an early sign of the disease. They are associated with significant morbidity and mortality. VFs are incidentally found in one out of five imaging studies, however, more than half of the VFs are not identified nor reported in patient computed tomography (CT) scans. Our study aimed to develop a machine learning algorithm to identify VFs in abdominal/chest CT scans and evaluate its performance. We acquired two independent data sets of routine abdominal/chest CT scans of patients aged 50 years or older: a training set of 1011 scans from a non-interventional, prospective proof-of-concept study at the Universitair Ziekenhuis (UZ) Brussel and a validation set of 2000 subjects from an observational cohort study at the Hospital of Holbaek. Both data sets were externally reevaluated to identify reference standard VF readings using the Genant semiquantitative (SQ) grading. Four independent models have been trained in a cross-validation experiment using the training set and an ensemble of four models has been applied to the external validation set. The validation set contained 15.3% scans with one or more VF (SQ2-3), whereas 663 of 24,930 evaluable vertebrae (2.7%) were fractured (SQ2-3) as per reference standard readings. Comparison of the ensemble model with the reference standard readings in identifying subjects with one or more moderate or severe VF resulted in an area under the receiver operating characteristic curve (AUROC) of 0.88 (95% confidence interval [CI], 0.85-0.90), accuracy of 0.92 (95% CI, 0.91-0.93), kappa of 0.72 (95% CI, 0.67-0.76), sensitivity of 0.81 (95% CI, 0.76-0.85), and specificity of 0.95 (95% CI, 0.93-0.96). We demonstrated that a machine learning algorithm trained for VF detection achieved strong performance on an external validation set. It has the potential to support healthcare professionals with the early identification of VFs and prevention of future fragility fractures. © 2023 UCB S.A. and The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).
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Affiliation(s)
- Joeri Nicolaes
- Department of Electrical Engineering (ESAT), Center for Processing Speech and Images, KU Leuven, Leuven, Belgium
- UCB Pharma, Brussels, Belgium
| | - Michael Kriegbaum Skjødt
- Department of Medicine, Hospital of Holbaek, Holbaek, Denmark
- OPEN-Open Patient Data Explorative Network, Department of Clinical Research, University of Southern Denmark and Odense University Hospital, Odense, Denmark
| | | | - Christopher Dyer Smith
- OPEN-Open Patient Data Explorative Network, Department of Clinical Research, University of Southern Denmark and Odense University Hospital, Odense, Denmark
| | - Bo Abrahamsen
- Department of Medicine, Hospital of Holbaek, Holbaek, Denmark
- OPEN-Open Patient Data Explorative Network, Department of Clinical Research, University of Southern Denmark and Odense University Hospital, Odense, Denmark
- NDORMS, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Oxford University Hospitals, Oxford, UK
| | | | | | - Dirk Vandermeulen
- Department of Electrical Engineering (ESAT), Center for Processing Speech and Images, KU Leuven, Leuven, Belgium
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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: 0] [Impact Index Per Article: 0] [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.
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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
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9
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Ong W, Liu RW, Makmur A, Low XZ, Sng WJ, Tan JH, Kumar N, Hallinan JTPD. Artificial Intelligence Applications for Osteoporosis Classification Using Computed Tomography. Bioengineering (Basel) 2023; 10:1364. [PMID: 38135954 PMCID: PMC10741220 DOI: 10.3390/bioengineering10121364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 11/21/2023] [Accepted: 11/23/2023] [Indexed: 12/24/2023] Open
Abstract
Osteoporosis, marked by low bone mineral density (BMD) and a high fracture risk, is a major health issue. Recent progress in medical imaging, especially CT scans, offers new ways of diagnosing and assessing osteoporosis. This review examines the use of AI analysis of CT scans to stratify BMD and diagnose osteoporosis. By summarizing the relevant studies, we aimed to assess the effectiveness, constraints, and potential impact of AI-based osteoporosis classification (severity) via CT. A systematic search of electronic databases (PubMed, MEDLINE, Web of Science, ClinicalTrials.gov) was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 39 articles were retrieved from the databases, and the key findings were compiled and summarized, including the regions analyzed, the type of CT imaging, and their efficacy in predicting BMD compared with conventional DXA studies. Important considerations and limitations are also discussed. The overall reported accuracy, sensitivity, and specificity of AI in classifying osteoporosis using CT images ranged from 61.8% to 99.4%, 41.0% to 100.0%, and 31.0% to 100.0% respectively, with areas under the curve (AUCs) ranging from 0.582 to 0.994. While additional research is necessary to validate the clinical efficacy and reproducibility of these AI tools before incorporating them into routine clinical practice, these studies demonstrate the promising potential of using CT to opportunistically predict and classify osteoporosis without the need for DEXA.
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Affiliation(s)
- Wilson Ong
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore (A.M.); (X.Z.L.); (W.J.S.); (J.T.P.D.H.)
| | - Ren Wei Liu
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore (A.M.); (X.Z.L.); (W.J.S.); (J.T.P.D.H.)
| | - Andrew Makmur
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore (A.M.); (X.Z.L.); (W.J.S.); (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
| | - Xi Zhen Low
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore (A.M.); (X.Z.L.); (W.J.S.); (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
| | - Weizhong Jonathan Sng
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore (A.M.); (X.Z.L.); (W.J.S.); (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
| | - 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.)
| | - James Thomas Patrick Decourcy Hallinan
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore (A.M.); (X.Z.L.); (W.J.S.); (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
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10
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Cheng L, Cai F, Xu M, Liu P, Liao J, Zong S. A diagnostic approach integrated multimodal radiomics with machine learning models based on lumbar spine CT and X-ray for osteoporosis. J Bone Miner Metab 2023; 41:877-889. [PMID: 37898574 DOI: 10.1007/s00774-023-01469-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 09/16/2023] [Indexed: 10/30/2023]
Abstract
INTRODUCTION The aim of this analysis is to construct a combined model that integrates radiomics, clinical risk factors, and machine learning algorithms to diagnose osteoporosis in patients and explore its potential in clinical applications. MATERIALS AND METHODS A retrospective analysis was conducted on 616 lumbar spine. Radiomics features were extracted from the computed tomography (CT) scans and anteroposterior and lateral X-ray images of the lumbar spine. Logistic regression (LR), support vector machine (SVM), and random forest (RF) algorithms were used to construct radiomics models. The receiver operating characteristic curve (ROC) was employed to select the best-performing model. Clinical risk factors were identified through univariate logistic regression analysis (ULRA) and multivariate logistic regression analysis (MLRA) and utilized to develop a clinical model. A combined model was then created by merging radiomics and clinical risk factors. The performance of the models was evaluated using ROC curve analysis, and the clinical value of the models was assessed using decision curve analysis (DCA). RESULTS A total of 4858 radiomics features were extracted. Among the radiomics models, the SVM model demonstrated the optimal diagnostic capabilities and accuracy, with an area under the curve (AUC) of 0.958 (0.9405-0.9762) in the training cohort and 0.907 (0.8648-0.9492) in the test cohort. Furthermore, the combined model exhibited an AUC of 0.959 (0.9412-0.9763) in the training cohort and 0.910 (0.8690-0.9506) in the test cohort. CONCLUSION The combined model displayed outstanding ability in diagnosing osteoporosis, providing a safe and efficient method for clinical decision-making.
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Affiliation(s)
- Liwei Cheng
- Department of Spine Osteopathia, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Nanning, 530021, Guangxi, People's Republic of China
| | - Fangqi Cai
- Department of Respiratory and Critical Care Medicine, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, 530021, People's Republic of China
| | - Mingzhi Xu
- Department of Spine Osteopathia, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Nanning, 530021, Guangxi, People's Republic of China
| | - Pan Liu
- Department of Spine Osteopathia, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Nanning, 530021, Guangxi, People's Republic of China
- Department of Orthopaedics, The Third Affiliated Hospital of Xinxiang Medical University, Xinxiang, 453000, People's Republic of China
| | - Jun Liao
- Department of Spine Osteopathia, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Nanning, 530021, Guangxi, People's Republic of China.
| | - Shaohui Zong
- Department of Spine Osteopathia, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Nanning, 530021, Guangxi, People's Republic of China.
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11
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Bodden J, Dieckmeyer M, Sollmann N, Rühling S, Prucker P, Löffler MT, Burian E, Subburaj K, Zimmer C, Kirschke JS, Baum T. Long-term reproducibility of opportunistically assessed vertebral bone mineral density and texture features in routine clinical multi-detector computed tomography using an automated segmentation framework. Quant Imaging Med Surg 2023; 13:5472-5482. [PMID: 37711780 PMCID: PMC10498219 DOI: 10.21037/qims-23-19] [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: 01/04/2023] [Accepted: 06/08/2023] [Indexed: 09/16/2023]
Abstract
Background To investigate reproducibility of texture features and volumetric bone mineral density (vBMD) extracted from trabecular bone in the thoracolumbar spine in routine clinical multi-detector computed tomography (MDCT) data in a single scanner environment. Methods Patients who underwent two routine clinical thoraco-abdominal MDCT exams at a single scanner with a time interval of 6 to 26 months (n=203, 131 males; time interval mean, 13 months; median, 12 months) were included in this observational study. Exclusion criteria were metabolic and hematological disorders, bone metastases, use of bone-active medications, and history of osteoporotic vertebral fractures (VFs) or prior diagnosis of osteoporosis. A convolutional neural network (CNN)-based framework was used for automated spine labeling and segmentation (T5-L5), asynchronous Hounsfield unit (HU)-to-BMD calibration, and correction for the intravenous contrast medium phase. Vertebral vBMD and six texture features [varianceglobal, entropy, short-run emphasis (SRE), long-run emphasis (LRE), run-length non-uniformity (RLN), and run percentage (RP)] were extracted for mid- (T5-T8) and lower thoracic (T9-T12), and lumbar vertebrae (L1-L5), respectively. Relative annual changes were calculated in texture features and vBMD for each vertebral level and sorted by sex, and changes were checked for statistical significance (P<0.05) using paired t-tests. Root mean square coefficient of variation (RMSCV) and root mean square error (RMSE) were calculated as measures of variability. Results SRE, LRE, RLN, and RP exhibited substantial reproducibility with RMSCV-values below 2%, for both sexes and at all spine levels, while vBMD was less reproducible (RMSCV =11.9-16.2%). Entropy showed highest variability (RMSCV =4.34-7.69%) due to statistically significant increases [range, mean ± standard deviation: (4.40±5.78)% to (8.36±8.66)%, P<0.001]. RMSCV of varianceglobal ranged from 1.60% to 3.03%. Conclusions Opportunistic assessment of texture features in a single scanner environment using the presented CNN-based framework yields substantial reproducibility, outperforming vBMD reproducibility. Lowest scan-rescan variability was found for higher-order texture features. Further studies are warranted to determine, whether microarchitectural changes to the trabecular bone may be assessed through texture features.
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Affiliation(s)
- Jannis Bodden
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Michael Dieckmeyer
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Nico Sollmann
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Ulm, Germany
| | - Sebastian Rühling
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Philipp Prucker
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Maximilian T. Löffler
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Freiburg im Breisgau, Germany
| | - Egon Burian
- Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Karupppasamy Subburaj
- Department of Mechanical and Production Engineering, Aarhus University, Aarhus, Denmark
| | - Claus Zimmer
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Jan S. Kirschke
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Thomas Baum
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
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12
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Guenoun D, Champsaur P. Opportunistic Computed Tomography Screening for Osteoporosis and Fracture. Semin Musculoskelet Radiol 2023; 27:451-456. [PMID: 37748468 DOI: 10.1055/s-0043-1771037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/27/2023]
Abstract
Osteoporosis is underdiagnosed and undertreated, leading to loss of treatment for the patient and high costs for the health care system. Routine thoracic and/or abdominal computed tomography (CT) performed for other indications can screen opportunistically for osteoporosis with no extra cost, time, or irradiation. Various methods can quantify fracture risk on opportunistic clinical CT: vertebral Hounsfield unit bone mineral density (BMD), usually of L1; BMD measurement with asynchronous or internal calibration; quantitative CT; bone texture assessment; and finite element analysis. Screening for osteoporosis and vertebral fractures on opportunistic CT is a promising approach, providing automated fracture risk scores by means of artificial intelligence, thus enabling earlier management.
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Affiliation(s)
- Daphne Guenoun
- APHM, Sainte-Marguerite Hospital, Institute for Locomotion, Department of Radiology, Marseille, France
- Aix-Marseille University, CNRS, Institut des Sciences du Mouvement, Marseille, France
| | - Pierre Champsaur
- APHM, Sainte-Marguerite Hospital, Institute for Locomotion, Department of Radiology, Marseille, France
- Aix-Marseille University, CNRS, Institut des Sciences du Mouvement, Marseille, France
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13
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Niu X, Huang Y, Li X, Yan W, Lu X, Jia X, Li J, Hu J, Sun T, Jing W, Guo J. Development and validation of a fully automated system using deep learning for opportunistic osteoporosis screening using low-dose computed tomography scans. Quant Imaging Med Surg 2023; 13:5294-5305. [PMID: 37581046 PMCID: PMC10423368 DOI: 10.21037/qims-22-1438] [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: 12/27/2022] [Accepted: 06/09/2023] [Indexed: 08/16/2023]
Abstract
Background Bone density measurement is an important examination for the diagnosis and screening of osteoporosis. The aim of this study was to develop a deep learning (DL) system for automatic measurement of bone mineral density (BMD) for osteoporosis screening using low-dose computed tomography (LDCT) images. Methods This retrospective study included 500 individuals who underwent LDCT scanning from April 2018 to July 2021. All images were manually annotated by a radiologist for the cancellous bone of target vertebrae and post-processed using quantitative computed tomography (QCT) software to identify osteoporosis. Patients were divided into the training, validation, and testing sets in a ratio of 6:2:2 using a 4-fold cross validation method. A localization model using faster region-based convolutional neural network (R-CNN) was trained to identify and locate the target vertebrae (T12-L2), then a 3-dimensional (3D) AnatomyNet was trained to finely segment the cancellous bone of target vertebrae in the localized image. A 3D DenseNet was applied for calculating BMD. The Dice coefficient was used to evaluate segmentation performance. Linear regression and Bland-Altman (BA) analyses were performed to compare the calculated BMD values using the proposed system with QCT. The diagnostic performance of the system for osteoporosis and osteopenia was evaluated with receiver operating characteristic (ROC) curve analysis. Results Our segmentation model achieved a mean Dice coefficient of 0.95, with Dice coefficients greater than 0.9 accounting for 96.6%. The correlation coefficient (R2) and mean errors between the proposed system and QCT in the testing set were 0.967 and 2.21 mg/cm3, respectively. The area under the curve (AUC) of the ROC was 0.984 for detecting osteoporosis and 0.993 for distinguishing abnormal BMD (osteopenia and osteoporosis). Conclusions The fully automated DL-based system is able to perform automatic BMD calculation for opportunistic osteoporosis screening with high accuracy using LDCT scans.
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Affiliation(s)
- Xinyi Niu
- Department of Radiology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Yilin Huang
- School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, China
| | - Xinyu Li
- Department of Radiology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Wenming Yan
- School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, China
| | - Xuanyu Lu
- Department of Radiology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Xiaoqian Jia
- Department of Radiology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Jianying Li
- GE HealthCare China, Computed Tomography Research Center, Beijing, China
| | - Jieliang Hu
- Department of Radiology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Tianze Sun
- Department of Radiology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Wenfeng Jing
- School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, China
| | - Jianxin Guo
- Department of Radiology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
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14
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Levi R, Garoli F, Battaglia M, Rizzo DAA, Mollura M, Savini G, Riva M, Tomei M, Ortolina A, Fornari M, Rohatgi S, Angelotti G, Savevski V, Mazziotti G, Barbieri R, Grimaldi M, Politi LS. CT-based radiomics can identify physiological modifications of bone structure related to subjects' age and sex. LA RADIOLOGIA MEDICA 2023:10.1007/s11547-023-01641-6. [PMID: 37147473 DOI: 10.1007/s11547-023-01641-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 04/26/2023] [Indexed: 05/07/2023]
Abstract
PURPOSE Radiomics of vertebral bone structure is a promising technique for identification of osteoporosis. We aimed at assessing the accuracy of machine learning in identifying physiological changes related to subjects' sex and age through analysis of radiomics features from CT images of lumbar vertebrae, and define its generalizability across different scanners. MATERIALS AND METHODS We annotated spherical volumes-of-interest (VOIs) in the center of the vertebral body for each lumbar vertebra in 233 subjects who had undergone lumbar CT for back pain on 3 different scanners, and we evaluated radiomics features from each VOI. Subjects with history of bone metabolism disorders, cancer, and vertebral fractures were excluded. We performed machine learning classification and regression models to identify subjects' sex and age respectively, and we computed a voting model which combined predictions. RESULTS The model was trained on 173 subjects and tested on an internal validation dataset of 60. Radiomics was able to identify subjects' sex within single CT scanner (ROC AUC: up to 0.9714), with lower performance on the combined dataset of the 3 scanners (ROC AUC: 0.5545). Higher consistency among different scanners was found in identification of subjects' age (R2 0.568 on all scanners, MAD 7.232 years), with highest results on a single CT scanner (R2 0.667, MAD 3.296 years). CONCLUSION Radiomics features are able to extract biometric data from lumbar trabecular bone, and determine bone modifications related to subjects' sex and age with great accuracy. However, acquisition from different CT scanners reduces the accuracy of the analysis.
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Affiliation(s)
- Riccardo Levi
- Department of Biomedical Sciences, Humanitas University, Via R. Levi Montalcini 4, 20072, Pieve Emanuele, Milan, Italy
- Department of Neuroradiology, IRCCS Humanitas Research Hospital, 20090, Rozzano, Italy
| | - Federico Garoli
- Department of Biomedical Sciences, Humanitas University, Via R. Levi Montalcini 4, 20072, Pieve Emanuele, Milan, Italy
- Department of Neuroradiology, IRCCS Humanitas Research Hospital, 20090, Rozzano, Italy
| | - Massimiliano Battaglia
- Department of Biomedical Sciences, Humanitas University, Via R. Levi Montalcini 4, 20072, Pieve Emanuele, Milan, Italy
- Department of Neuroradiology, IRCCS Humanitas Research Hospital, 20090, Rozzano, Italy
| | - Dario A A Rizzo
- Department of Biomedical Sciences, Humanitas University, Via R. Levi Montalcini 4, 20072, Pieve Emanuele, Milan, Italy
- Department of Neuroradiology, IRCCS Humanitas Research Hospital, 20090, Rozzano, Italy
| | - Maximilliano Mollura
- Department of Electronics, Information and Bioengineering, Politecnico Di Milano, 20133, Milan, Italy
| | - Giovanni Savini
- Department of Neuroradiology, IRCCS Humanitas Research Hospital, 20090, Rozzano, Italy
| | - Marco Riva
- Department of Biomedical Sciences, Humanitas University, Via R. Levi Montalcini 4, 20072, Pieve Emanuele, Milan, Italy
- Department of Neurosurgery, IRCCS Humanitas Research Hospital, 20090, Rozzano, Italy
| | - Massimo Tomei
- Department of Neurosurgery, IRCCS Humanitas Research Hospital, 20090, Rozzano, Italy
| | - Alessandro Ortolina
- Department of Neurosurgery, IRCCS Humanitas Research Hospital, 20090, Rozzano, Italy
| | - Maurizio Fornari
- Department of Neurosurgery, IRCCS Humanitas Research Hospital, 20090, Rozzano, Italy
| | - Saurabh Rohatgi
- Department of Neuroradiology, Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Giovanni Angelotti
- Artificial Intelligence Center, IRCCS Humanitas Research Hospital, 20090, Rozzano, Italy
| | - Victor Savevski
- Artificial Intelligence Center, IRCCS Humanitas Research Hospital, 20090, Rozzano, Italy
| | - Gherardo Mazziotti
- Department of Biomedical Sciences, Humanitas University, Via R. Levi Montalcini 4, 20072, Pieve Emanuele, Milan, Italy
- Metabolic Bone Diseases and Osteoporosis Section, Endocrinology, Diabetology and Medical Andrology Unit, IRCCS, Humanitas Research Hospital, 20090, Rozzano, Italy
| | - Riccardo Barbieri
- Department of Electronics, Information and Bioengineering, Politecnico Di Milano, 20133, Milan, Italy
| | - Marco Grimaldi
- Department of Neuroradiology, IRCCS Humanitas Research Hospital, 20090, Rozzano, Italy
| | - Letterio S Politi
- Department of Biomedical Sciences, Humanitas University, Via R. Levi Montalcini 4, 20072, Pieve Emanuele, Milan, Italy.
- Department of Neuroradiology, IRCCS Humanitas Research Hospital, 20090, Rozzano, Italy.
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15
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Zhang J, Liu J, Liang Z, Xia L, Zhang W, Xing Y, Zhang X, Tang G. Differentiation of acute and chronic vertebral compression fractures using conventional CT based on deep transfer learning features and hand-crafted radiomics features. BMC Musculoskelet Disord 2023; 24:165. [PMID: 36879285 PMCID: PMC9987077 DOI: 10.1186/s12891-023-06281-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Accepted: 02/28/2023] [Indexed: 03/08/2023] Open
Abstract
BACKGROUND We evaluated the diagnostic efficacy of deep learning radiomics (DLR) and hand-crafted radiomics (HCR) features in differentiating acute and chronic vertebral compression fractures (VCFs). METHODS A total of 365 patients with VCFs were retrospectively analysed based on their computed tomography (CT) scan data. All patients completed MRI examination within 2 weeks. There were 315 acute VCFs and 205 chronic VCFs. Deep transfer learning (DTL) features and HCR features were extracted from CT images of patients with VCFs using DLR and traditional radiomics, respectively, and feature fusion was performed to establish the least absolute shrinkage and selection operator. The MRI display of vertebral bone marrow oedema was used as the gold standard for acute VCF, and the model performance was evaluated using the receiver operating characteristic (ROC).To separately evaluate the effectiveness of DLR, traditional radiomics and feature fusion in the differential diagnosis of acute and chronic VCFs, we constructed a nomogram based on the clinical baseline data to visualize the classification evaluation. The predictive power of each model was compared using the Delong test, and the clinical value of the nomogram was evaluated using decision curve analysis (DCA). RESULTS Fifty DTL features were obtained from DLR, 41 HCR features were obtained from traditional radiomics, and 77 features fusion were obtained after feature screening and fusion of the two. The area under the curve (AUC) of the DLR model in the training cohort and test cohort were 0.992 (95% confidence interval (CI), 0.983-0.999) and 0.871 (95% CI, 0.805-0.938), respectively. While the AUCs of the conventional radiomics model in the training cohort and test cohort were 0.973 (95% CI, 0.955-0.990) and 0.854 (95% CI, 0.773-0.934), respectively. The AUCs of the features fusion model in the training cohort and test cohort were 0.997 (95% CI, 0.994-0.999) and 0.915 (95% CI, 0.855-0.974), respectively. The AUCs of nomogram constructed by the features fusion in combination with clinical baseline data were 0.998 (95% CI, 0.996-0.999) and 0.946 (95% CI, 0.906-0.987) in the training cohort and test cohort, respectively. The Delong test showed that the differences between the features fusion model and the nomogram in the training cohort and the test cohort were not statistically significant (P values were 0.794 and 0.668, respectively), and the differences in the other prediction models in the training cohort and the test cohort were statistically significant (P < 0.05). DCA showed that the nomogram had high clinical value. CONCLUSION The features fusion model can be used for the differential diagnosis of acute and chronic VCFs, and its differential diagnosis ability is improved when compared with that when either radiomics is used alone. At the same time, the nomogram has a high predictive value for acute and chronic VCFs and can be a potential decision-making tool to assist clinicians, especially when a patient is unable to undergo spinal MRI examination.
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Affiliation(s)
- Jun Zhang
- Department of Radiology, Clinical Medical College of Shanghai Tenth People's Hospital of Nanjing Medical University, 301 Middle Yanchang Road, Shanghai, 200072, P.R. China.,Department of Radiology, Sir RunRun Hospital affiliated to Nanjing Medical University, 109 Longmian Road, Nanjing, Jiangsu, 211002, P.R. China
| | - Jiayi Liu
- Department of Radiology, Sir RunRun Hospital affiliated to Nanjing Medical University, 109 Longmian Road, Nanjing, Jiangsu, 211002, P.R. China
| | - Zhipeng Liang
- Department of Radiology, Sir RunRun Hospital affiliated to Nanjing Medical University, 109 Longmian Road, Nanjing, Jiangsu, 211002, P.R. China
| | - Liang Xia
- Department of Radiology, Sir RunRun Hospital affiliated to Nanjing Medical University, 109 Longmian Road, Nanjing, Jiangsu, 211002, P.R. China
| | - Weixiao Zhang
- Department of Radiology, Sir RunRun Hospital affiliated to Nanjing Medical University, 109 Longmian Road, Nanjing, Jiangsu, 211002, P.R. China
| | - Yanfen Xing
- Department of Radiology, Sir RunRun Hospital affiliated to Nanjing Medical University, 109 Longmian Road, Nanjing, Jiangsu, 211002, P.R. China
| | - Xueli Zhang
- Department of Radiology, Shanghai TenthPeople's Hospital, Tongji University School of Medicine, 301 Middle Yanchang Road, Shanghai, 200072, P.R. China
| | - Guangyu Tang
- Department of Radiology, Clinical Medical College of Shanghai Tenth People's Hospital of Nanjing Medical University, 301 Middle Yanchang Road, Shanghai, 200072, P.R. China. .,Department of Radiology, Shanghai TenthPeople's Hospital, Tongji University School of Medicine, 301 Middle Yanchang Road, Shanghai, 200072, P.R. China.
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16
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Lu S, Fuggle NR, Westbury LD, Ó Breasail M, Bevilacqua G, Ward KA, Dennison EM, Mahmoodi S, Niranjan M, Cooper C. Machine learning applied to HR-pQCT images improves fracture discrimination provided by DXA and clinical risk factors. Bone 2023; 168:116653. [PMID: 36581259 DOI: 10.1016/j.bone.2022.116653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 12/16/2022] [Accepted: 12/21/2022] [Indexed: 12/28/2022]
Abstract
BACKGROUND Traditional analysis of High Resolution peripheral Quantitative Computed Tomography (HR-pQCT) images results in a multitude of cortical and trabecular parameters which would be potentially cumbersome to interpret for clinicians compared to user-friendly tools utilising clinical parameters. A computer vision approach (by which the entire scan is 'read' by a computer algorithm) to ascertain fracture risk, would be far simpler. We therefore investigated whether a computer vision and machine learning technique could improve upon selected clinical parameters in assessing fracture risk. METHODS Participants of the Hertfordshire Cohort Study (HCS) attended research visits at which height and weight were measured; fracture history was determined via self-report and vertebral fracture assessment. Bone microarchitecture was assessed via HR-pQCT scans of the non-dominant distal tibia (Scanco XtremeCT), and bone mineral density measurement and lateral vertebral assessment were performed using dual-energy X-ray absorptiometry (DXA) (Lunar Prodigy Advanced). Images were cropped, pre-processed and texture analysis was performed using a three-dimensional local binary pattern method. These image data, together with age, sex, height, weight, BMI, dietary calcium and femoral neck BMD, were used in a random-forest classification algorithm. Receiver operating characteristic (ROC) analysis was used to compare fracture risk identification methods. RESULTS Overall, 180 males and 165 females were included in this study with a mean age of approximately 76 years and 97 (28 %) participants had sustained a previous fracture. Using clinical risk factors alone resulted in an area under the curve (AUC) of 0.70 (95 % CI: 0.56-0.84), which improved to 0.71 (0.57-0.85) with the addition of DXA-measured BMD. The addition of HR-pQCT image data to the machine learning classifier with clinical risk factors and DXA-measured BMD as inputs led to an improved AUC of 0.90 (0.83-0.96) with a sensitivity of 0.83 and specificity of 0.74. CONCLUSION These results suggest that using a three-dimensional computer vision method to HR-pQCT scanning may enhance the identification of those at risk of fracture beyond that afforded by clinical risk factors and DXA-measured BMD. This approach has the potential to make the information offered by HR-pQCT more accessible (and therefore) applicable to healthcare professionals in the clinic if the technology becomes more widely available.
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Affiliation(s)
- Shengyu Lu
- Faculty of Engineering and Physical Sciences, Electronics and Computer Science, University of Southampton, UK.
| | - Nicholas R Fuggle
- MRC Lifecourse Epidemiology Centre, University of Southampton, Southampton, UK; The Alan Turing Institute, London, UK.
| | - Leo D Westbury
- MRC Lifecourse Epidemiology Centre, University of Southampton, Southampton, UK.
| | - Mícheál Ó Breasail
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
| | - Gregorio Bevilacqua
- MRC Lifecourse Epidemiology Centre, University of Southampton, Southampton, UK.
| | - Kate A Ward
- MRC Lifecourse Epidemiology Centre, University of Southampton, Southampton, UK; NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, UK.
| | - Elaine M Dennison
- MRC Lifecourse Epidemiology Centre, University of Southampton, Southampton, UK; Victoria University of Wellington, Wellington, New Zealand.
| | - Sasan Mahmoodi
- Faculty of Engineering and Physical Sciences, Electronics and Computer Science, University of Southampton, UK.
| | - Mahesan Niranjan
- Faculty of Engineering and Physical Sciences, Electronics and Computer Science, University of Southampton, UK.
| | - Cyrus Cooper
- MRC Lifecourse Epidemiology Centre, University of Southampton, Southampton, UK; NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, UK; NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK.
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17
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Wang J, Zhou S, Chen S, He Y, Gao H, Yan L, Hu X, Li P, Shen H, Luo M, You T, Li J, Zhong Z, Zhang K. Prediction of osteoporosis using radiomics analysis derived from single source dual energy CT. BMC Musculoskelet Disord 2023; 24:100. [PMID: 36750927 PMCID: PMC9903590 DOI: 10.1186/s12891-022-06096-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Accepted: 12/15/2022] [Indexed: 02/09/2023] Open
Abstract
BACKGROUND With the aging population of society, the incidence rate of osteoporosis is increasing year by year. Early diagnosis of osteoporosis plays a significant role in the progress of disease prevention. As newly developed technology, computed tomography (CT) radiomics could discover radiomic features difficult to recognize visually, providing convenient, comprehensive and accurate osteoporosis diagnosis. This study aimed to develop and validate a clinical-radiomics model based on the monochromatic imaging of single source dual-energy CT for osteoporosis prediction. METHODS One hundred sixty-four participants who underwent both single source dual-energy CT and quantitative computed tomography (QCT) lumbar-spine examination were enrolled in a study cohort including training datasets (n = 114 [30 osteoporosis and 84 non-osteoporosis]) and validation datasets (n = 50 [12 osteoporosis and 38 non-osteoporosis]). One hundred seven radiomics features were extracted from 70-keV monochromatic CT images. With QCT as the reference standard, a radiomics signature was built by using least absolute shrinkage and selection operator (LASSO) regression on the basis of reproducible features. A clinical-radiomics model was constructed by incorporating the radiomics signature and a significant clinical predictor (age) using multivariate logistic regression analysis. Model performance was assessed by its calibration, discrimination and clinical usefulness. RESULTS The radiomics signature comprised 14 selected features and showed good calibration and discrimination in both training and validation cohorts. The clinical-radiomics model, which incorporated the radiomics signature and a significant clinical predictor (age), also showed good discrimination, with an area under the receiver operating characteristic curve (AUC) of 0.938 (95% confidence interval, 0.903-0.952) in the training cohort and an AUC of 0.988 (95% confidence interval, 0.967-0.998) in the validation cohort, and good calibration. The clinical-radiomics model stratified participants into groups with osteoporosis and non-osteoporosis with an accuracy of 94.0% in the validation cohort. Decision curve analysis (DCA) demonstrated that the radiomics signature and the clinical-radiomics model were clinically useful. CONCLUSIONS The clinical-radiomics model incorporating the radiomics signature and a clinical parameter had a good ability to predict osteoporosis based on dual-energy CT monoenergetic imaging.
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Affiliation(s)
- Jinling Wang
- grid.488482.a0000 0004 1765 5169Department of Radiology, The First Hospital of Hunan University of Chinese Medicine, 95 Shaoshan Middle Road, Yuhua District, Changsha, 410007 People’s Republic of China ,grid.488482.a0000 0004 1765 5169College of Integrated Traditional Chinese and Western Medicine, Hunan University of Chinese Medicine, 300 Xueshi Road, Yuelu District, Changsha, 410208 People’s Republic of China
| | - Shuwei Zhou
- grid.488482.a0000 0004 1765 5169Department of Radiology, The First Hospital of Hunan University of Chinese Medicine, 95 Shaoshan Middle Road, Yuhua District, Changsha, 410007 People’s Republic of China ,grid.488482.a0000 0004 1765 5169College of Integrated Traditional Chinese and Western Medicine, Hunan University of Chinese Medicine, 300 Xueshi Road, Yuelu District, Changsha, 410208 People’s Republic of China
| | - Suping Chen
- GE Healthcare (Shanghai) Co., Ltd., Shanghai, 201203 People’s Republic of China
| | - Yewen He
- grid.488482.a0000 0004 1765 5169Department of Radiology, The First Hospital of Hunan University of Chinese Medicine, 95 Shaoshan Middle Road, Yuhua District, Changsha, 410007 People’s Republic of China
| | - Hui Gao
- grid.488482.a0000 0004 1765 5169Department of Radiology, The First Hospital of Hunan University of Chinese Medicine, 95 Shaoshan Middle Road, Yuhua District, Changsha, 410007 People’s Republic of China
| | - Luyou Yan
- grid.488482.a0000 0004 1765 5169Department of Radiology, The First Hospital of Hunan University of Chinese Medicine, 95 Shaoshan Middle Road, Yuhua District, Changsha, 410007 People’s Republic of China
| | - Xiaoli Hu
- grid.488482.a0000 0004 1765 5169Department of Radiology, The First Hospital of Hunan University of Chinese Medicine, 95 Shaoshan Middle Road, Yuhua District, Changsha, 410007 People’s Republic of China
| | - Ping Li
- grid.488482.a0000 0004 1765 5169Department of Radiology, The First Hospital of Hunan University of Chinese Medicine, 95 Shaoshan Middle Road, Yuhua District, Changsha, 410007 People’s Republic of China
| | - Hongrong Shen
- grid.488482.a0000 0004 1765 5169Department of Radiology, The First Hospital of Hunan University of Chinese Medicine, 95 Shaoshan Middle Road, Yuhua District, Changsha, 410007 People’s Republic of China
| | - Muqing Luo
- grid.488482.a0000 0004 1765 5169Department of Radiology, The First Hospital of Hunan University of Chinese Medicine, 95 Shaoshan Middle Road, Yuhua District, Changsha, 410007 People’s Republic of China
| | - Tian You
- grid.488482.a0000 0004 1765 5169Department of Radiology, The First Hospital of Hunan University of Chinese Medicine, 95 Shaoshan Middle Road, Yuhua District, Changsha, 410007 People’s Republic of China
| | - Jianyu Li
- grid.488482.a0000 0004 1765 5169Department of Radiology, The First Hospital of Hunan University of Chinese Medicine, 95 Shaoshan Middle Road, Yuhua District, Changsha, 410007 People’s Republic of China
| | - Zeya Zhong
- grid.488482.a0000 0004 1765 5169Department of Radiology, The First Hospital of Hunan University of Chinese Medicine, 95 Shaoshan Middle Road, Yuhua District, Changsha, 410007 People’s Republic of China
| | - Kun Zhang
- Department of Radiology, The First Hospital of Hunan University of Chinese Medicine, 95 Shaoshan Middle Road, Yuhua District, Changsha, 410007, People's Republic of China. .,College of Integrated Traditional Chinese and Western Medicine, Hunan University of Chinese Medicine, 300 Xueshi Road, Yuelu District, Changsha, 410208, People's Republic of China.
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18
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Abstract
PURPOSE OF REVIEW Opportunistic screening is a combination of techniques to identify subjects of high risk for osteoporotic fracture using routine clinical CT scans prescribed for diagnoses unrelated to osteoporosis. The two main components are automated detection of vertebral fractures and measurement of bone mineral density (BMD) in CT scans, in which a phantom for calibration of CT to BMD values is not used. This review describes the particular challenges of opportunistic screening and provides an overview and comparison of current techniques used for opportunistic screening. The review further outlines the performance of opportunistic screening. RECENT FINDINGS A wide range of technologies for the automatic detection of vertebral fractures have been developed and successfully validated. Most of them are based on artificial intelligence algorithms. The automated differentiation of osteoporotic from traumatic fractures and vertebral deformities unrelated to osteoporosis, the grading of vertebral fracture severity, and the detection of mild vertebral fractures is still problematic. The accuracy of automated fracture detection compared to classical radiological semi-quantitative Genant scoring is about 80%. Accuracy errors of alternative BMD calibration methods compared to simultaneous phantom-based calibration used in standard quantitative CT (QCT) range from below 5% to about 10%. The impact of contrast agents, frequently administered in clinical CT on the determination of BMD and on fracture risk determination is still controversial. Opportunistic screening, the identification of vertebral fracture and the measurement of BMD using clinical routine CT scans, is feasible but corresponding techniques still need to be integrated into the clinical workflow and further validated with respect to the prediction of fracture risk.
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Affiliation(s)
- Klaus Engelke
- Department of Medicine III, Friedrich-Alexander University of Erlangen-Nürnberg, University Hospital Erlangen, Ulmenweg 18, 91054, Erlangen, Germany.
- Institute of Medical Physics (IMP), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Henkestr. 91, 91052, Erlangen, Germany.
| | - Oliver Chaudry
- Department of Medicine III, Friedrich-Alexander University of Erlangen-Nürnberg, University Hospital Erlangen, Ulmenweg 18, 91054, Erlangen, Germany
- Institute of Medical Physics (IMP), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Henkestr. 91, 91052, Erlangen, Germany
| | - Stefan Bartenschlager
- Department of Medicine III, Friedrich-Alexander University of Erlangen-Nürnberg, University Hospital Erlangen, Ulmenweg 18, 91054, Erlangen, Germany
- Institute of Medical Physics (IMP), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Henkestr. 91, 91052, Erlangen, Germany
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19
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Vertebral trabecular bone texture analysis in opportunistic MRI and CT scan can distinguish patients with and without osteoporotic vertebral fracture: A preliminary study. Eur J Radiol 2023; 158:110642. [PMID: 36527774 DOI: 10.1016/j.ejrad.2022.110642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 11/29/2022] [Accepted: 11/30/2022] [Indexed: 12/12/2022]
Abstract
PURPOSE To investigate the potential of texture parameters from opportunistic MRI and CT for the detection of patients with vertebral fragility fracture, to design a decision tree and to compute a Random Forest analysis for the prediction of fracture risk. METHODS One hundred and eighty vertebrae of sixty patients with at least one (30) or without (30) a fragility fracture were retrospectively assessed. Patients had a DXA, an MRI and a CT scan from the three first lumbar vertebrae. Vertebrae texture analysis was performed in routine abdominal or lumbar CT and lumbar MRI using 1st and 2nd order texture parameters. Hounsfield Unit Bone density (HU BD) was also measured on CT-scan images. RESULTS Twelve texture parameters, Z-score and HU BD were significantly different between the two groups whereas T score and BMD were not. The inter observer reproducibility was good to excellent. Decision tree showed that age and HU BD were the most relevant factors to predict the fracture risk with a 93 % sensitivity and 56 % specificity. AUC was 0.91 in MRI and 0.92 in CT-scan using the Random Forest analysis. The corresponding sensitivity and specificity were 72 % and 93 % in MRI and 83 and 89 % in CT. CONCLUSIONS This study is the first to compare texture indices computed from opportunistic CT and MR images. Age and HU-BD together with selected texture parameters could be used to assess risk fracture. Machine learning algorithm can detect fracture risk in opportunistic CT and MR imaging and might be of high interest for the diagnosis of osteoporosis.
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20
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Zakharov A, Pisov M, Bukharaev A, Petraikin A, Morozov S, Gombolevskiy V, Belyaev M. Interpretable vertebral fracture quantification via anchor-free landmarks localization. Med Image Anal 2023; 83:102646. [PMID: 36279768 DOI: 10.1016/j.media.2022.102646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 07/20/2022] [Accepted: 09/27/2022] [Indexed: 12/12/2022]
Abstract
Vertebral body compression fractures are early signs of osteoporosis. Though these fractures are visible on Computed Tomography (CT) images, they are frequently missed by radiologists in clinical settings. Prior research on automatic methods of vertebral fracture classification proves its reliable quality; however, existing methods provide hard-to-interpret outputs and sometimes fail to process cases with severe abnormalities such as highly pathological vertebrae or scoliosis. We propose a new two-step algorithm to localize the vertebral column in 3D CT images and then detect individual vertebrae and quantify fractures in 2D simultaneously. We train neural networks for both steps using a simple 6-keypoints based annotation scheme, which corresponds precisely to the current clinical recommendation. Our algorithm has no exclusion criteria, processes 3D CT in 2 seconds on a single GPU, and provides an interpretable and verifiable output. The method approaches expert-level performance and demonstrates state-of-the-art results in vertebrae 3D localization (the average error is 1mm), vertebrae 2D detection (precision and recall are 0.99), and fracture identification (ROC AUC at the patient level is up to 0.96). Our anchor-free vertebra detection network shows excellent generalizability on a new domain by achieving ROC AUC 0.95, sensitivity 0.85, specificity 0.9 on a challenging VerSe dataset with many unseen vertebra types.
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Affiliation(s)
- Alexey Zakharov
- IRA Labs Ltd, Moscow, Russia; Skolkovo Institute of Science and Technology, Moscow, Russia
| | - Maxim Pisov
- IRA Labs Ltd, Moscow, Russia; Skolkovo Institute of Science and Technology, Moscow, Russia; Kharkevich Institute for Information Transmission Problems, Moscow, Russia
| | | | - Alexey Petraikin
- Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department, Russia
| | | | | | - Mikhail Belyaev
- IRA Labs Ltd, Moscow, Russia; Skolkovo Institute of Science and Technology, Moscow, Russia.
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21
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Wang M, Chen X, Cui W, Wang X, Hu N, Tang H, Zhang C, Shen J, Xie C, Chen X. A computed tomography-based radiomics nomogram for predicting osteoporotic vertebral fractures: A longitudinal study. J Clin Endocrinol Metab 2022; 108:e283-e294. [PMID: 36494103 DOI: 10.1210/clinem/dgac722] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Revised: 11/09/2022] [Accepted: 12/07/2022] [Indexed: 12/14/2022]
Abstract
CONTEXT Fractures are serious consequence of osteoporosis in old adults. However, few longitudinal studies showed the role of computed tomography (CT)-based radiomics in predicting osteoporotic fractures. OBJECTIVE We evaluated the performance of CT radiomics-based model for osteoporotic vertebral fractures (OVF) in a longitudinal study. METHODS 7906 subjects without OVF who were aged over 50 years, and underwent CT scans between 2016 and 2019 were enrolled and followed up until 2021. Seventy-two cases of new OVF were identified. One hundred and forty-four people without OVF during follow-up were selected as control. Radiomics features were extracted from baseline CT images. CT values of trabecular bone, and area and density of erector spinae were determined. Cox regression analysis was used to identify the independent associated factors. The predictive performance of the nomogram was assessed using the receiver operating characteristic (ROC) curve, calibration curve and decision curve. RESULTS CT value of vertebra (adjusted hazard ratio (aHR) = 2.04, 95% confidence interval (CI): 1.07, 3.89), radiomics score (aHR = 6.56, 95%CI:3.47, 12.38) and area of erector spinae (aHR = 1.68, 95%CI: 1.02, 2.78) were independently associated with OVF. Radscore was associated with severe OVF (aHR = 6.00, 95% CI:2.78-12.93). The nomogram showed good discrimination with a C-index of 0.82 (95%CI: 0.77, 0.87). The area under the curve of nomogram and radscore were both higher than osteoporosis + muscle area for 3-year and 4-year risk of fractures (p < 0.05). Decision curve also demonstrated that the radiomics nomogram was useful. CONCLUSIONS Bone radiomics is associated with OVF and the nomogram based on radiomics signature and muscle provides a tool for the prediction of OVF.
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Affiliation(s)
- Miaomiao Wang
- Department of Radiology, the Second Affiliated Hospital of Soochow University, 1055 Sanxiang road, Suzhou 215008, China
- Department of Radiology, the Affiliated Hospital of Nanjing University of Chinese Medicine, 155 Hanzhong road, Nanjing 210029, China
| | - Xin Chen
- Department of Radiology, Shanghai Sixth People's Hospital, Shanghai 200233, China
| | - Wenjing Cui
- Department of Radiology, the Affiliated Hospital of Nanjing University of Chinese Medicine, 155 Hanzhong road, Nanjing 210029, China
| | - Xinru Wang
- Department of Radiology, the Affiliated Hospital of Nanjing University of Chinese Medicine, 155 Hanzhong road, Nanjing 210029, China
| | - Nandong Hu
- Department of Radiology, the Affiliated Hospital of Nanjing University of Chinese Medicine, 155 Hanzhong road, Nanjing 210029, China
| | - Hongye Tang
- Department of Radiology, the Affiliated Hospital of Nanjing University of Chinese Medicine, 155 Hanzhong road, Nanjing 210029, China
| | - Chao Zhang
- Department of Orthopaedics, the Affiliated Hospital of Nanjing University of Chinese Medicine, 155 Hanzhong road, Nanjing 210029, China
| | - Jirong Shen
- Department of Orthopaedics, the Affiliated Hospital of Nanjing University of Chinese Medicine, 155 Hanzhong road, Nanjing 210029, China
| | - Chao Xie
- Department of Orthopaedics, University of Rochester School of Medicine, NY 14642, USA
| | - Xiao Chen
- Department of Radiology, the Second Affiliated Hospital of Soochow University, 1055 Sanxiang road, Suzhou 215008, China
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22
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Ren G, Yu K, Xie Z, Wang P, Zhang W, Huang Y, Wang Y, Wu X. Current Applications of Machine Learning in Spine: From Clinical View. Global Spine J 2022; 12:1827-1840. [PMID: 34628966 PMCID: PMC9609532 DOI: 10.1177/21925682211035363] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
STUDY DESIGN Narrative review. OBJECTIVES This review aims to present current applications of machine learning (ML) in spine domain to clinicians. METHODS We conducted a comprehensive PubMed search of peer-reviewed articles that were published between 2006 and 2020 using terms (spine, spinal, lumbar, cervical, thoracic, machine learning) to examine ML in spine. Then exclude research of other domain, case report, review or meta-analysis, and which without available abstract or full text. RESULTS Total 1738 articles were retrieved from database, and 292 studies were finally included. Key findings of current applications were compiled and summarized in this review. Main clinical applications of those techniques including image processing, diagnosis, decision supporting, operative assistance, rehabilitation, surgery outcomes, complications, hospitalization and cost. CONCLUSIONS ML had achieved excellent performance and hold immense potential in spine. ML could help clinical staff to improve medical level, enhance work efficiency, and reduce adverse events. However more randomized controlled trials and improvement of interpretability are essential to clinicians accepting models' assistance in real work.
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Affiliation(s)
- GuanRui Ren
- Southeast University Medical College,
Nanjing, Jiangsu, China
| | - Kun Yu
- Nanjing Jiangbei Hospital, Nanjing,
Jiangsu, China
| | - ZhiYang Xie
- Department of Spine Surgery, Zhongda
Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, China
| | - PeiYang Wang
- Southeast University Medical College,
Nanjing, Jiangsu, China
| | - Wei Zhang
- Southeast University Medical College,
Nanjing, Jiangsu, China
| | - Yong Huang
- Southeast University Medical College,
Nanjing, Jiangsu, China
| | - YunTao Wang
- Department of Spine Surgery, Zhongda
Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, China,YunTao Wang, Department of Spine Surgery,
Zhongda Hospital, School of Medicine, Southeast University, No. 87, Dingjiaqiao
Road, Nanjing, Jiangsu 210009, China.
| | - XiaoTao Wu
- Department of Spine Surgery, Zhongda
Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, China,XiaoTao Wu, Department of Spine Surgery,
Zhongda Hospital, School of Medicine, Southeast University, No. 87, Dingjiaqiao
Road, Nanjing, Jiangsu 210009, China.
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23
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Biamonte E, Levi R, Carrone F, Vena W, Brunetti A, Battaglia M, Garoli F, Savini G, Riva M, Ortolina A, Tomei M, Angelotti G, Laino ME, Savevski V, Mollura M, Fornari M, Barbieri R, Lania AG, Grimaldi M, Politi LS, Mazziotti G. Artificial intelligence-based radiomics on computed tomography of lumbar spine in subjects with fragility vertebral fractures. J Endocrinol Invest 2022; 45:2007-2017. [PMID: 35751803 DOI: 10.1007/s40618-022-01837-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 06/06/2022] [Indexed: 10/17/2022]
Abstract
PURPOSE There is emerging evidence that radiomics analyses can improve detection of skeletal fragility. In this cross-sectional study, we evaluated radiomics features (RFs) on computed tomography (CT) images of the lumbar spine in subjects with or without fragility vertebral fractures (VFs). METHODS Two-hundred-forty consecutive individuals (mean age 60.4 ± 15.4, 130 males) were evaluated by radiomics analyses on opportunistic lumbar spine CT. VFs were diagnosed in 58 subjects by morphometric approach on CT or XR-ray spine (D4-L4) images. DXA measurement of bone mineral density (BMD) was performed on 17 subjects with VFs. RESULTS Twenty RFs were used to develop the machine learning model reaching 0.839 and 0.789 of AUROC in the train and test datasets, respectively. After correction for age, VFs were significantly associated with RFs obtained from non-fractured vertebrae indicating altered trabecular microarchitecture, such as low-gray level zone emphasis (LGLZE) [odds ratio (OR) 1.675, 95% confidence interval (CI) 1.215-2.310], gray level non-uniformity (GLN) (OR 1.403, 95% CI 1.023-1.924) and neighboring gray-tone difference matrix (NGTDM) contrast (OR 0.692, 95% CI 0.493-0.971). Noteworthy, no significant differences in LGLZE (p = 0.94), GLN (p = 0.40) and NGDTM contrast (p = 0.54) were found between fractured subjects with BMD T score < - 2.5 SD and those in whom VFs developed in absence of densitometric diagnosis of osteoporosis. CONCLUSIONS Artificial intelligence-based analyses on spine CT images identified RFs associated with fragility VFs. Future studies are needed to test the predictive value of RFs on opportunistic CT scans in identifying subjects with primary and secondary osteoporosis at high risk of fracture.
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Affiliation(s)
- E Biamonte
- Endocrinology, Diabetology and Medical Andrology Unit, IRCCS, Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089, Milan, Italy
| | - R Levi
- Department of Biomedical Sciences, Humanitas University, Via R. Levi Montalcini 4, Pieve Emanuele, 20090, Milan, Italy
- Neuroradiology Unit, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089, Milan, Italy
| | - F Carrone
- Endocrinology, Diabetology and Medical Andrology Unit, IRCCS, Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089, Milan, Italy
| | - W Vena
- Endocrinology, Diabetology and Medical Andrology Unit, IRCCS, Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089, Milan, Italy
| | - A Brunetti
- Endocrinology, Diabetology and Medical Andrology Unit, IRCCS, Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089, Milan, Italy
| | - M Battaglia
- Department of Biomedical Sciences, Humanitas University, Via R. Levi Montalcini 4, Pieve Emanuele, 20090, Milan, Italy
- Neuroradiology Unit, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089, Milan, Italy
| | - F Garoli
- Department of Biomedical Sciences, Humanitas University, Via R. Levi Montalcini 4, Pieve Emanuele, 20090, Milan, Italy
- Neuroradiology Unit, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089, Milan, Italy
| | - G Savini
- Neuroradiology Unit, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089, Milan, Italy
| | - M Riva
- Department of Biomedical Sciences, Humanitas University, Via R. Levi Montalcini 4, Pieve Emanuele, 20090, Milan, Italy
- Neurosurgery Unit, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089, Milan, Italy
| | - A Ortolina
- Neurosurgery Unit, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089, Milan, Italy
| | - M Tomei
- Neurosurgery Unit, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089, Milan, Italy
| | - G Angelotti
- Artificial Intelligence Center, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089, Milan, Italy
| | - M E Laino
- Artificial Intelligence Center, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089, Milan, Italy
| | - V Savevski
- Artificial Intelligence Center, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089, Milan, Italy
| | - M Mollura
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - M Fornari
- Neurosurgery Unit, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089, Milan, Italy
| | - R Barbieri
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - A G Lania
- Department of Biomedical Sciences, Humanitas University, Via R. Levi Montalcini 4, Pieve Emanuele, 20090, Milan, Italy
- Endocrinology, Diabetology and Medical Andrology Unit, IRCCS, Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089, Milan, Italy
| | - M Grimaldi
- Neuroradiology Unit, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089, Milan, Italy
| | - L S Politi
- Department of Biomedical Sciences, Humanitas University, Via R. Levi Montalcini 4, Pieve Emanuele, 20090, Milan, Italy.
- Neuroradiology Unit, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089, Milan, Italy.
| | - G Mazziotti
- Department of Biomedical Sciences, Humanitas University, Via R. Levi Montalcini 4, Pieve Emanuele, 20090, Milan, Italy
- Endocrinology, Diabetology and Medical Andrology Unit, IRCCS, Humanitas Research Hospital, Via Manzoni 56, Rozzano, 20089, Milan, Italy
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24
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Hornung AL, Hornung CM, Mallow GM, Barajas JN, Rush A, Sayari AJ, Galbusera F, Wilke HJ, Colman M, Phillips FM, An HS, Samartzis D. Artificial intelligence in spine care: current applications and future utility. 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 2022; 31:2057-2081. [PMID: 35347425 DOI: 10.1007/s00586-022-07176-0] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 01/18/2022] [Accepted: 03/08/2022] [Indexed: 01/20/2023]
Abstract
PURPOSE The field of artificial intelligence is ever growing and the applications of machine learning in spine care are continuously advancing. Given the advent of the intelligence-based spine care model, understanding the evolution of computation as it applies to diagnosis, treatment, and adverse event prediction is of great importance. Therefore, the current review sought to synthesize findings from the literature at the interface of artificial intelligence and spine research. METHODS A narrative review was performed based on the literature of three databases (MEDLINE, CINAHL, and Scopus) from January 2015 to March 2021 that examined historical and recent advancements in the understanding of artificial intelligence and machine learning in spine research. Studies were appraised for their role in, or description of, advancements within image recognition and predictive modeling for spinal research. Only English articles that fulfilled inclusion criteria were ultimately incorporated in this review. RESULTS This review briefly summarizes the history and applications of artificial intelligence and machine learning in spine. Three basic machine learning training paradigms: supervised learning, unsupervised learning, and reinforced learning are also discussed. Artificial intelligence and machine learning have been utilized in almost every facet of spine ranging from localization and segmentation techniques in spinal imaging to pathology specific algorithms which include but not limited to; preoperative risk assessment of postoperative complications, screening algorithms for patients at risk of osteoporosis and clustering analysis to identify subgroups within adolescent idiopathic scoliosis. The future of artificial intelligence and machine learning in spine surgery is also discussed with focusing on novel algorithms, data collection techniques and increased utilization of automated systems. CONCLUSION Improvements to modern-day computing and accessibility to various imaging modalities allow for innovative discoveries that may arise, for example, from management. Given the imminent future of AI in spine surgery, it is of great importance that practitioners continue to inform themselves regarding AI, its goals, use, and progression. In the future, it will be critical for the spine specialist to be able to discern the utility of novel AI research, particularly as it continues to pervade facets of everyday spine surgery.
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Affiliation(s)
- Alexander L Hornung
- Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA
| | | | - G Michael Mallow
- Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA
| | - J Nicolás Barajas
- Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA
| | - Augustus Rush
- Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA
| | - Arash J Sayari
- Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA
| | | | - Hans-Joachim Wilke
- Institute of Orthopaedic Research and Biomechanics, Trauma Research Center Ulm, Ulm University, Ulm, Germany
| | - Matthew Colman
- Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA
| | - Frank M Phillips
- Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA
| | - Howard S An
- Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA
| | - Dino Samartzis
- Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA.
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Rydzewski NR, Yadav P, Musunuru HB, Condit KM, Francis D, Zhao SG, Baschnagel AM. Radiomic Modeling of Bone Density and Rib Fracture Risk After Stereotactic Body Radiation Therapy for Early-Stage Non-Small Cell Lung Cancer. Adv Radiat Oncol 2022; 7:100884. [PMID: 35647405 PMCID: PMC9133372 DOI: 10.1016/j.adro.2021.100884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Accepted: 12/21/2021] [Indexed: 11/01/2022] Open
Abstract
Purpose Methods and Materials Results Conclusion
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Xue Z, Huo J, Sun X, Sun X, Ai ST, LichiZhang, Liu C. Using radiomic features of lumbar spine CT images to differentiate osteoporosis from normal bone density. BMC Musculoskelet Disord 2022; 23:336. [PMID: 35395769 PMCID: PMC8991484 DOI: 10.1186/s12891-022-05309-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Accepted: 03/28/2022] [Indexed: 01/04/2023] Open
Abstract
OBJECTIVE This study aimed to develop a predictive model to detect osteoporosis using radiomic features from lumbar spine computed tomography (CT) images. METHODS A total of 133 patients were included in this retrospective study, 41 men and 92 women, with a mean age of 65.45 ± 9.82 years (range: 31-94 years); 53 had normal bone mineral density, 32 osteopenia, and 48 osteoporosis. For each patient, the L1-L4 vertebrae on the CT images were automatically segmented using SenseCare and defined as regions of interest (ROIs). In total, 1,197 radiomic features were extracted from these ROIs using PyRadiomics. The most significant features were selected using logistic regression and Pearson correlation coefficient matrices. Using these features, we constructed three linear classification models based on the random forest (RF), support vector machine (SVM), and K-nearest neighbor (KNN) algorithms, respectively. The training and test sets were repeatedly selected using fivefold cross-validation. The model performance was evaluated using the area under the receiver operator characteristic curve (AUC) and confusion matrix. RESULTS The classification model based on RF had the highest performance, with an AUC of 0.994 (95% confidence interval [CI]: 0.979-1.00) for differentiating normal BMD and osteoporosis, 0.866 (95% CI: 0.779-0.954) for osteopenia versus osteoporosis, and 0.940 (95% CI: 0.891-0.989) for normal BMD versus osteopenia. CONCLUSIONS The excellent performance of this radiomic model indicates that lumbar spine CT images can effectively be used to identify osteoporosis and as a tool for opportunistic osteoporosis screening.
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Affiliation(s)
- Zhihao Xue
- Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Jiayu Huo
- Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Xiaojiang Sun
- Shanghai Key Laboratory of Orthopaedic Implants, Department of Orthopaedic Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xuzhou Sun
- Shanghai Key Laboratory of Orthopaedic Implants, Department of Orthopaedic Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Song Tao Ai
- Department of Radiology, Shanghai Ninth People's Hospital, Tong University Shanghai Jiao School of Medicine, Shanghai, China
| | - LichiZhang
- Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
| | - Chenglei Liu
- Department of Radiology, Shanghai Ninth People's Hospital, Tong University Shanghai Jiao School of Medicine, Shanghai, China.
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Woisetschläger M, Klintström E, Spångeus A. The impact of imaging time and contrast agent dose on screening for osteoporosis with contrast-enhanced CT. Eur Radiol Exp 2022; 6:8. [PMID: 35141833 PMCID: PMC8828831 DOI: 10.1186/s41747-021-00259-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Accepted: 12/22/2021] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Screening for osteoporosis with contrast-enhanced computed tomography (CT) is promising for identifying high-risk osteoporotic patients. Our aims were (1) to investigate the estimated volume bone mineral density (vBMD) change over time after contrast injection (CT perfusion imaging, CTPI); and (2) to examine the influence of contrast dose on vBMD. METHODS Fifteen patients, aged 71 ± 9 years (mean ± standard deviation, range 55-86) underwent a CTPI examination (28 scans within 63 s) of the upper body followed (after a waiting time of 10 min) by a full 4-phase CT examination (4 scans within 4 min). The contrast dose for CTPI was 0.38-0.83 mL/kg, and for 4-phase CT was 0.87-1.29 mL/kg. Vertebrae L1-L3 were analysed totalling 43 vertebrae, using Mindways qCT Pro. RESULTS After contrast injection, vBMD showed a near-horizontal line until 17.5 s (non-contrast phase), followed by a steep increase 17.5-41.5 s after contrast injection, i.e., in the arterial phase, which plateaued 41.5 s after, i.e., in the early venous phase. A higher contrast dose per kg yielded significantly higher vBMD increase in both the arterial and venous phase (p < 0.003). CONCLUSIONS Both time from contrast administration and contrast dose per kg affected vBMD results. In arterial phase, the steepness of the curve makes vBMD estimation unsure. However, as values plateaued in the venous phase it might be possible to predict the correct vBMD values. Furthermore, contrast dose is a factor that needs to be adjusted for when using such a formula.
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Affiliation(s)
- Mischa Woisetschläger
- Department of Radiology in Linköping, and Department of Health, Medicine and Caring Sciences, Linköping University, 58185, Linköping, Sweden. .,Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.
| | - Eva Klintström
- Department of Radiology in Linköping, and Department of Health, Medicine and Caring Sciences, Linköping University, 58185, Linköping, Sweden.,Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
| | - Anna Spångeus
- Department of Acute Internal Medicine and Geriatrics, Department of Endocrinology and Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
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Liu L, Si M, Ma H, Cong M, Xu Q, Sun Q, Wu W, Wang C, Fagan MJ, Mur LAJ, Yang Q, Ji B. A hierarchical opportunistic screening model for osteoporosis using machine learning applied to clinical data and CT images. BMC Bioinformatics 2022; 23:63. [PMID: 35144529 PMCID: PMC8829991 DOI: 10.1186/s12859-022-04596-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Accepted: 02/02/2022] [Indexed: 01/10/2023] Open
Abstract
Background Osteoporosis is a common metabolic skeletal disease and usually lacks obvious symptoms. Many individuals are not diagnosed until osteoporotic fractures occur. Bone mineral density (BMD) measured by dual-energy X-ray absorptiometry (DXA) is the gold standard for osteoporosis detection. However, only a limited percentage of people with osteoporosis risks undergo the DXA test. As a result, it is vital to develop methods to identify individuals at-risk based on methods other than DXA. Results We proposed a hierarchical model with three layers to detect osteoporosis using clinical data (including demographic characteristics and routine laboratory tests data) and CT images covering lumbar vertebral bodies rather than DXA data via machine learning. 2210 individuals over age 40 were collected retrospectively, among which 246 individuals’ clinical data and CT images are both available. Irrelevant and redundant features were removed via statistical analysis. Consequently, 28 features, including 16 clinical data and 12 texture features demonstrated statistically significant differences (p < 0.05) between osteoporosis and normal groups. Six machine learning algorithms including logistic regression (LR), support vector machine with radial-basis function kernel, artificial neural network, random forests, eXtreme Gradient Boosting and Stacking that combined the above five classifiers were employed as classifiers to assess the performances of the model. Furthermore, to diminish the influence of data partitioning, the dataset was randomly split into training and test set with stratified sampling repeated five times. The results demonstrated that the hierarchical model based on LR showed better performances with an area under the receiver operating characteristic curve of 0.818, 0.838, and 0.962 for three layers, respectively in distinguishing individuals with osteoporosis and normal BMD. Conclusions The proposed model showed great potential in opportunistic screening for osteoporosis without additional expense. It is hoped that this model could serve to detect osteoporosis as early as possible and thereby prevent serious complications of osteoporosis, such as osteoporosis fractures. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04596-z.
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Affiliation(s)
- Liyu Liu
- School of Control Science and Engineering, Shandong University, 17923 Jingshi Road, Jinan, Shandong, People's Republic of China
| | - Meng Si
- Department of Orthopedics, Qilu Hospital of Shandong University, Jinan, Shandong, People's Republic of China
| | - Hecheng Ma
- Department of Orthopedics, Qilu Hospital of Shandong University, Jinan, Shandong, People's Republic of China
| | - Menglin Cong
- Department of Orthopedics, Qilu Hospital of Shandong University, Jinan, Shandong, People's Republic of China
| | - Quanzheng Xu
- School of Control Science and Engineering, Shandong University, 17923 Jingshi Road, Jinan, Shandong, People's Republic of China
| | - Qinghua Sun
- School of Control Science and Engineering, Shandong University, 17923 Jingshi Road, Jinan, Shandong, People's Republic of China
| | - Weiming Wu
- School of Control Science and Engineering, Shandong University, 17923 Jingshi Road, Jinan, Shandong, People's Republic of China
| | - Cong Wang
- School of Control Science and Engineering, Shandong University, 17923 Jingshi Road, Jinan, Shandong, People's Republic of China
| | - Michael J Fagan
- School of Engineering, University of Hull, Hull, HU6 7RX, UK
| | - Luis A J Mur
- Institute of Biological, Environmental and Rural Sciences (IBERS), Aberystwyth University, Aberystwyth, Wales, UK
| | - Qing Yang
- Department of Breast and Thyroid, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, People's Republic of China
| | - Bing Ji
- School of Control Science and Engineering, Shandong University, 17923 Jingshi Road, Jinan, Shandong, People's Republic of China.
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Zhao Y, Zhao T, Chen S, Zhang X, Serrano Sosa M, Liu J, Mo X, Chen X, Huang M, Li S, Zhang X, Huang C. Fully automated radiomic screening pipeline for osteoporosis and abnormal bone density with a deep learning-based segmentation using a short lumbar mDixon sequence. Quant Imaging Med Surg 2022; 12:1198-1213. [PMID: 35111616 DOI: 10.21037/qims-21-587] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Accepted: 09/16/2021] [Indexed: 11/06/2022]
Abstract
BACKGROUND Although lumbar bone marrow fat fraction (BMFF) has been demonstrated to be predictive of osteoporosis, its utility is limited by the requirement of manual segmentation. Additionally, quantitative features beyond simple BMFF average remain to be explored. In this study, we developed a fully automated radiomic pipeline using deep learning-based segmentation to detect osteoporosis and abnormal bone density (ABD) using a <20 s modified Dixon (mDixon) sequence. METHODS In total, 222 subjects underwent quantitative computed tomography (QCT) and lower back magnetic resonance imaging (MRI). Bone mineral density (BMD) were extracted from L1-L3 using QCT as the reference standard; 206 subjects (48.8±14.9 years old, 140 females) were included in the final analysis, and were divided temporally into the training/validation set (142/64 subjects). A deep-learning network was developed to perform automated segmentation. Radiomic models were built using the same training set to predict ABD and osteoporosis using the mDixon maps. The performance was evaluated using the temporal validation set comprised of 64 subjects, along with the automated segmentation. Additional 25 subjects (56.1±8.8 years, 14 females) from another site and a different scanner vendor was included as independent validation to evaluate the performance of the pipeline. RESULTS The automated segmentation achieved an outstanding mean dice coefficient of 0.912±0.062 compared to manual in the temporal validation. Task-based evaluation was performed in the temporal validation set, for predicting ABD and osteoporosis, the area under the curve, sensitivity, specificity, and accuracy were 0.925/0.899, 0.923/0.667, 0.789/0.873, 0.844/0.844, respectively. These values were comparable to that of manual segmentation. External validation (cross-vendor) was also performed; the area under the curve, sensitivity, specificity, and accuracy were 0.688/0.913, 0.786/0.857, 0.545/0.944, 0.680/0.920 for ABD and osteoporosis prediction, respectively. CONCLUSIONS Our work is the first attempt using radiomics to predict osteoporosis with BMFF map, and the deep-learning based segmentation will further facilitate the clinical utility of the pipeline as a screening tool for early detection of ABD.
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Affiliation(s)
- Yinxia Zhao
- Department of Radiology, The Third Affiliated Hospital of Southern Medical University (Orthopaedic Hospital of Guangdong Province), Guangzhou, China
| | - Tianyun Zhao
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA
| | - Shenglan Chen
- Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China
| | - Xintao Zhang
- Department of Radiology, The Third Affiliated Hospital of Southern Medical University (Orthopaedic Hospital of Guangdong Province), Guangzhou, China
| | - Mario Serrano Sosa
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA
| | - Jin Liu
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
| | - Xianfu Mo
- Department of Radiology, The Third Affiliated Hospital of Southern Medical University (Orthopaedic Hospital of Guangdong Province), Guangzhou, China
| | - Xiaojun Chen
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
| | - Mingqian Huang
- Department of Radiology, The Mount Sinai Hospital, New York, NY, USA
| | - Shaolin Li
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
| | - Xiaodong Zhang
- Department of Radiology, The Third Affiliated Hospital of Southern Medical University (Orthopaedic Hospital of Guangdong Province), Guangzhou, China
| | - Chuan Huang
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA.,Department of Radiology, Stony Brook Medicine, Stony Brook, NY, USA
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Dieckmeyer M, Löffler MT, El Husseini M, Sekuboyina A, Menze B, Sollmann N, Wostrack M, Zimmer C, Baum T, Kirschke JS. Level-Specific Volumetric BMD Threshold Values for the Prediction of Incident Vertebral Fractures Using Opportunistic QCT: A Case-Control Study. Front Endocrinol (Lausanne) 2022; 13:882163. [PMID: 35669688 PMCID: PMC9165054 DOI: 10.3389/fendo.2022.882163] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 04/08/2022] [Indexed: 11/13/2022] Open
Abstract
PURPOSE To establish and evaluate the diagnostic accuracy of volumetric bone mineral density (vBMD) threshold values at different spinal levels, derived from opportunistic quantitative computed tomography (QCT), for the prediction of incident vertebral fractures (VF). MATERIALS AND METHODS In this case-control study, 35 incident VF cases (23 women, 12 men; mean age: 67 years) and 70 sex- and age-matched controls were included, based on routine multi detector CT (MDCT) scans of the thoracolumbar spine. Trabecular vBMD was measured from routine baseline CT scans of the thoracolumbar spine using an automated pipeline including vertebral segmentation, asynchronous calibration for HU-to-vBMD conversion, and correction of intravenous contrast medium (https://anduin.bonescreen.de). Threshold values at T1-L5 were calculated for the optimal operating point according to the Youden index and for fixed sensitivities (60 - 85%) in receiver operating characteristic (ROC) curves. RESULTS vBMD at each single level of the thoracolumbar spine was significantly associated with incident VFs (odds ratio per SD decrease [OR], 95% confidence interval [CI] at T1-T4: 3.28, 1.66-6.49; at T5-T8: 3.28, 1.72-6.26; at T9-T12: 3.37, 1.78-6.36; and at L1-L4: 3.98, 1.97-8.06), independent of adjustment for age, sex, and prevalent VF. AUC showed no significant difference between vertebral levels and was highest at the thoracolumbar junction (AUC = 0.75, 95%-CI = 0.63 - 0.85 for T11-L2). Optimal threshold values increased from lumbar (L1-L4: 52.0 mg/cm³) to upper thoracic spine (T1-T4: 69.3 mg/cm³). At T11-L2, T12-L3 and L1-L4, a threshold of 80.0 mg/cm³ showed sensitivities of 85 - 88%, and specificities of 41 - 49%. To achieve comparable sensitivity (85%) at more superior spinal levels, resulting thresholds were higher: 114.1 mg/cm³ (T1-T4), 92.0 mg/cm³ (T5-T8), 88.2 mg/cm³ (T9-T12). CONCLUSIONS At all levels of the thoracolumbar spine, lower vBMD was associated with incident VFs in an elderly, predominantly oncologic patient population. Automated opportunistic osteoporosis screening of vBMD along the entire thoracolumbar spine allows for risk assessment of imminent VFs. We propose level-specific vBMD threshold at the thoracolumbar spine to identify individuals at high fracture risk.
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Affiliation(s)
- Michael Dieckmeyer
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- *Correspondence: Michael Dieckmeyer,
| | - Maximilian Thomas Löffler
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- Department of Radiology, University Medical Center, Albert-Ludwigs-University Freiburg, Freiburg, Germany
| | - Malek El Husseini
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Anjany Sekuboyina
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Bjoern Menze
- Image-Based Biomedical Modeling, Department of Computer Science, Technical University of Munich, Munich, Germany
- Department of Quantitative Biomedicine, Faculty of Medicine, University of Zurich, Zurich, Switzerland
| | - Nico Sollmann
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Ulm, Germany
| | - Maria Wostrack
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- Department of Neurosurgery, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Claus Zimmer
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Thomas Baum
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Jan Stefan Kirschke
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
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AIM in Endocrinology. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2022]
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32
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Huber FA, Guggenberger R. AI MSK clinical applications: spine imaging. Skeletal Radiol 2022; 51:279-291. [PMID: 34263344 PMCID: PMC8692301 DOI: 10.1007/s00256-021-03862-0] [Citation(s) in RCA: 3] [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: 03/31/2021] [Revised: 06/28/2021] [Accepted: 07/03/2021] [Indexed: 02/02/2023]
Abstract
Recent investigations have focused on the clinical application of artificial intelligence (AI) for tasks specifically addressing the musculoskeletal imaging routine. Several AI applications have been dedicated to optimizing the radiology value chain in spine imaging, independent from modality or specific application. This review aims to summarize the status quo and future perspective regarding utilization of AI for spine imaging. First, the basics of AI concepts are clarified. Second, the different tasks and use cases for AI applications in spine imaging are discussed and illustrated by examples. Finally, the authors of this review present their personal perception of AI in daily imaging and discuss future chances and challenges that come along with AI-based solutions.
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Affiliation(s)
- Florian A. Huber
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Raemistrasse 100, 8091 Zurich, Switzerland
| | - Roman Guggenberger
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Raemistrasse 100, 8091 Zurich, Switzerland
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Mao L, Xia Z, Pan L, Chen J, Liu X, Li Z, Yan Z, Lin G, Wen H, Liu B. Deep learning for screening primary osteopenia and osteoporosis using spine radiographs and patient clinical covariates in a Chinese population. Front Endocrinol (Lausanne) 2022; 13:971877. [PMID: 36176468 PMCID: PMC9513384 DOI: 10.3389/fendo.2022.971877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 08/23/2022] [Indexed: 11/17/2022] Open
Abstract
PURPOSE Many high-risk osteopenia and osteoporosis patients remain undiagnosed. We proposed to construct a convolutional neural network model for screening primary osteopenia and osteoporosis based on the lumbar radiographs, and to compare the diagnostic performance of the CNN model adding the clinical covariates with the image model alone. METHODS A total of 6,908 participants were collected for analysis, including postmenopausal women and men aged 50-95 years, who performed conventional lumbar x-ray examinations and dual-energy x-ray absorptiometry (DXA) examinations within 3 months. All participants were divided into a training set, a validation set, test set 1, and test set 2 at a ratio of 8:1:1:1. The bone mineral density (BMD) values derived from DXA were applied as the reference standard. A three-class CNN model was developed to classify the patients into normal BMD, osteopenia, and osteoporosis. Moreover, we developed the models integrating the images with clinical covariates (age, gender, and BMI), and explored whether adding clinical data improves diagnostic performance over the image mode alone. The receiver operating characteristic curve analysis was performed for assessing the model performance. RESULTS As for classifying osteoporosis, the model based on the anteroposterior+lateral channel performed best, with the area under the curve (AUC) range from 0.909 to 0.937 in three test cohorts. The models with images alone achieved moderate sensitivity in classifying osteopenia, in which the highest AUC achieved 0.785. The performance of models integrating images with clinical data shows a slight improvement over models with anteroposterior or lateral images input alone for diagnosing osteoporosis, in which the AUC increased about 2%-4%. Regarding categorizing osteopenia and the normal BMD, the proposed models integrating images with clinical data also outperformed the models with images solely. CONCLUSION The deep learning-based approach could screen osteoporosis and osteopenia based on lumbar radiographs.
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Affiliation(s)
- Liting Mao
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Ziqiang Xia
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Liang Pan
- Department of AI Research Lab, Guangzhou YLZ Ruitu Information Technology Co, Ltd, Guangzhou, China
| | - Jun Chen
- Department of Radiology, ZHUHAI Branch of Guangdong Hospital of Chinese Medicine, Zhuhai, China
| | - Xian Liu
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Zhiqiang Li
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Zhaoxian Yan
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Gengbin Lin
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Huisen Wen
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Bo Liu
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
- *Correspondence: Bo Liu,
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Dai H, Wang Y, Fu R, Ye S, He X, Luo S, Jin W. Radiomics and stacking regression model for measuring bone mineral density using abdominal computed tomography. Acta Radiol 2021; 64:228-236. [PMID: 34964365 DOI: 10.1177/02841851211068149] [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] [Indexed: 12/16/2022]
Abstract
BACKGROUND Measurement of bone mineral density (BMD) is the most important method to diagnose osteoporosis. However, current BMD measurement is always performed after a fracture has occurred. PURPOSE To explore whether a radiomic model based on abdominal computed tomography (CT) can predict the BMD of lumbar vertebrae. MATERIAL AND METHODS A total of 245 patients who underwent both dual-energy X-ray absorptiometry (DXA) and abdominal CT examination (training cohort, n = 196; validation cohort, n = 49) were included in our retrospective study. In total, 1218 image features were extracted from abdominal CT images for each patient. Combined with clinical information, three steps including least absolute shrinkage and selection operator (LASSO) regression were used to select key features. A two-tier stacking regression model with multi-algorithm fusion was used for BMD prediction, which can integrate the advantages of linear model and non-linear model. The prediction results of this model were compared with those using a single regressor. The degree-of-freedom adjusted coefficient of determination (Adjusted-R2), root mean square error (RMSE), and mean absolute error (MAE) were used to evaluate the regression performance. RESULTS Compared with other regression methods, the two-tier stacking regression model has a higher regression performance, with Adjusted-R2, RMSE, and MAE of 0.830, 0.077, and 0.06, respectively. Pearson correlation analysis and Bland-Altman analysis showed that the BMD predicted by the model had a high correlation with the DXA results (r = 0.932, difference = -0.01 ± 0.1412 mg/cm2). CONCLUSION Using radiomics, the BMD of lumbar vertebrae could be predicted from abdominal CT images.
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Affiliation(s)
- Hong Dai
- Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, Zhejiang, PR China
| | - Yutao Wang
- Department of Medical imaging, The Affiliated Hospital of Medical School, Ningbo University, Ningbo, Zhejiang, PR China
| | - Randi Fu
- Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, Zhejiang, PR China
| | - Sijia Ye
- Department of Medical imaging, The Affiliated Hospital of Medical School, Ningbo University, Ningbo, Zhejiang, PR China
| | - Xiuchao He
- Department of Medical imaging, The Affiliated Hospital of Medical School, Ningbo University, Ningbo, Zhejiang, PR China
| | - Shuying Luo
- Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, Zhejiang, PR China
| | - Wei Jin
- Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, Zhejiang, PR China
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Deep learning takes the pain out of back breaking work - Automatic vertebral segmentation and attenuation measurement for osteoporosis. Clin Imaging 2021; 81:54-59. [PMID: 34598006 DOI: 10.1016/j.clinimag.2021.08.009] [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: 04/26/2021] [Revised: 07/30/2021] [Accepted: 08/13/2021] [Indexed: 02/07/2023]
Abstract
BACKGROUND Osteoporosis is an underdiagnosed and undertreated disease worldwide. Recent studies have highlighted the use of simple vertebral trabecular attenuation values for opportunistic osteoporosis screening. Meanwhile, machine learning has been used to accurately segment large parts of the human skeleton. PURPOSE To evaluate a fully automated deep learning-based method for lumbar vertebral segmentation and measurement of vertebral volumetric trabecular attenuation values. MATERIAL AND METHODS A deep learning-based method for automated segmentation of bones was retrospectively applied to non-contrast CT scans of 1008 patients (mean age 57 years, 472 female, 536 male). Each vertebral segmentation was automatically reduced by 7 mm in all directions in order to avoid cortical bone. The mean and median volumetric attenuation values from Th12 to L4 were obtained and plotted against patient age and sex. L1 values were further analyzed to facilitate comparison with previous studies. RESULTS The mean L1 attenuation values decreased linearly with age by -2.2 HU per year (age > 30, 95% CI: -2.4, -2.0, R2 = 0.3544). The mean L1 attenuation value of the entire population cohort was 140 HU ± 54. CONCLUSIONS With results closely matching those of previous studies, we believe that our fully automated deep learning-based method can be used to obtain lumbar volumetric trabecular attenuation values which can be used for opportunistic screening of osteoporosis in patients undergoing CT scans for other reasons.
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Kong SH, Shin CS. Applications of Machine Learning in Bone and Mineral Research. Endocrinol Metab (Seoul) 2021; 36:928-937. [PMID: 34674509 PMCID: PMC8566132 DOI: 10.3803/enm.2021.1111] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 08/23/2021] [Accepted: 09/09/2021] [Indexed: 12/26/2022] Open
Abstract
In this unprecedented era of the overwhelming volume of medical data, machine learning can be a promising tool that may shed light on an individualized approach and a better understanding of the disease in the field of osteoporosis research, similar to that in other research fields. This review aimed to provide an overview of the latest studies using machine learning to address issues, mainly focusing on osteoporosis and fractures. Machine learning models for diagnosing and classifying osteoporosis and detecting fractures from images have shown promising performance. Fracture risk prediction is another promising field of research, and studies are being conducted using various data sources. However, these approaches may be biased due to the nature of the techniques or the quality of the data. Therefore, more studies based on the proposed guidelines are needed to improve the technical feasibility and generalizability of artificial intelligence algorithms.
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Affiliation(s)
- Sung Hye Kong
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul,
Korea
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam,
Korea
| | - Chan Soo Shin
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul,
Korea
- Department of Internal Medicine, Seoul National University Hospital, Seoul,
Korea
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Dieckmeyer M, Rayudu NM, Yeung LY, Löffler M, Sekuboyina A, Burian E, Sollmann N, Kirschke JS, Baum T, Subburaj K. Prediction of incident vertebral fractures in routine MDCT: Comparison of global texture features, 3D finite element parameters and volumetric BMD. Eur J Radiol 2021; 141:109827. [PMID: 34225250 DOI: 10.1016/j.ejrad.2021.109827] [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: 04/21/2021] [Revised: 06/06/2021] [Accepted: 06/14/2021] [Indexed: 10/21/2022]
Abstract
PURPOSE In this case-control study, we evaluated different quantitative parameters derived from routine multi-detector computed tomography (MDCT) scans with respect to their ability to predict incident osteoporotic vertebral fractures of the thoracolumbar spine. METHODS 16 patients who received baseline and follow-up contrast-enhanced MDCT and were diagnosed with an incident osteoporotic vertebral fracture at follow-up, and 16 age-, sex-, and follow-up-time-matched controls were included in the study. Vertebrae were labelled and segmented using a fully automated pipeline. Volumetric bone mineral density (vBMD), finite element analysis (FEA)-based failure load (FL) and failure displacement (FD), as well as 24 texture features were extracted from L1 - L3 and averaged. Odds ratios (OR) with 95% confidence intervals (CI), expressed per standard deviation decrease, receiver operating characteristic (ROC) area under the curve (AUC), as well as logistic regression models, including all analyzed parameters as independent variables, were used to assess the prediction of incident vertebral fractures. RESULTS The texture feature Correlation (AUC = 0.754, p = 0.014; OR = 2.76, CI = 1.16-6.58) and vBMD (AUC = 0.750, p = 0.016; OR = 2.67, CI = 1.12-6.37) classified incident vertebral fractures best, while the best FEA-based parameter FL showed an AUC = 0.719 (p = 0.035). Correlation was the only significant predictor of incident fractures in the logistic regression analysis of all parameters (p = 0.022). CONCLUSION MDCT-derived FEA parameters and texture features, averaged from L1 - L3, showed only a moderate, but no statistically significant improvement of incident vertebral fracture prediction beyond BMD, supporting the hypothesis that vertebral-specific parameters may be superior for fracture risk assessment.
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Affiliation(s)
- Michael Dieckmeyer
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany.
| | - Nithin Manohar Rayudu
- Pillar of Engineering Product Development, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore.
| | - Long Yu Yeung
- Pillar of Engineering Product Development, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore.
| | - Maximilian Löffler
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany; Department of Radiology, University Medical Center, Albert-Ludwigs-University Freiburg, Hugstetter Str. 55, 79106 Freiburg, Germany.
| | - Anjany Sekuboyina
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany.
| | - Egon Burian
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany; Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany.
| | - Nico Sollmann
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany; TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany; Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany.
| | - Jan S Kirschke
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany; TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany.
| | - Thomas Baum
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany.
| | - Karupppasamy Subburaj
- Pillar of Engineering Product Development, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore; Changi General Hospital, 2 Simei Street 3, Singapore 529889, Singapore.
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Prado M, Khosla S, Chaput C, Giambini H. Opportunistic application of phantom-less calibration methods for fracture risk prediction using QCT/FEA. Eur Radiol 2021; 31:9428-9435. [PMID: 34047849 DOI: 10.1007/s00330-021-08071-w] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 04/30/2021] [Accepted: 05/11/2021] [Indexed: 11/25/2022]
Abstract
OBJECTIVES Quantitative computed tomography (QCT)-based finite element analysis (FEA) implements a calibration phantom to estimate bone mineral density (BMD) and assign material properties to the models. The objectives of this study were to (1) propose robust phantom-less calibration methods, using subject-specific tissues, to obtain vertebral fracture properties estimations using QCT/FEA; and (2) correlate QCT/FEA predictions to DXA values of areal BMD. METHODS Eighty of a cohort of 111 clinical QCT scans were used to obtain subject-specific parameters using a phantom calibration approach and for the development of the phantom-less calibration equations. Equations were developed based on the HU measured from various soft tissues and regions, and using multiple linear regression analyses. Thirty-one additional QCT scans were used for cross-validation of QCT/FEA estimated fracture loads from the L3 vertebrae based on the phantom and phantom-less equations. Finally, QCT/FEA-predicted fracture loads were correlated with aBMD obtained from DXA. RESULTS Overall, 217 QCT/FEA models from 31 subjects (20 females, 11 men) with mean ages of 69.6 (13.1) and 67.3 (14) were used to cross-validate the phantom-less equations and assess bone strength. The proposed phantom-less equations showed high correlations with phantom-based estimates of BMD (99%). Cross-validation of QCT/FEA-predicted fracture loads from phantom-less equations and phantom-specific outcomes resulted in high correlations for all proposed methods (0.94-0.99). QCT/FEA correlation outcomes from the phantom-less equations and DXA-aBMD were moderately high (0.64-0.68). CONCLUSIONS The proposed QCT/FEA subject-specific phantom-less calibration methods demonstrated the potential to be applied to both prospective and retrospective applications in the clinical setting. KEY POINTS • QCT/FEA overcomes the disadvantages of DXA and improves fracture properties predictions of vertebrae. • QCT/FEA fracture estimates using the phantom-less approach highly correlated to values obtained using a calibration phantom. • QCT/FEA prediction using a phantom-less approach is an accurate alternative over phantom-based methods.
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Affiliation(s)
- Maria Prado
- Department of Biomedical Engineering and Chemical Engineering, University of Texas at San Antonio, One UTSA Circle, San Antonio, TX, 78249, USA
| | - Sundeep Khosla
- Kogod Center on Aging and Division of Endocrinology, Mayo Clinic College of Medicine, Mayo Clinic, Rochester, MN, USA
| | - Christopher Chaput
- Department of Orthopedics, The University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Hugo Giambini
- Department of Biomedical Engineering and Chemical Engineering, University of Texas at San Antonio, One UTSA Circle, San Antonio, TX, 78249, USA.
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Smets J, Shevroja E, Hügle T, Leslie WD, Hans D. Machine Learning Solutions for Osteoporosis-A Review. J Bone Miner Res 2021; 36:833-851. [PMID: 33751686 DOI: 10.1002/jbmr.4292] [Citation(s) in RCA: 64] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 02/04/2021] [Accepted: 03/16/2021] [Indexed: 12/11/2022]
Abstract
Osteoporosis and its clinical consequence, bone fracture, is a multifactorial disease that has been the object of extensive research. Recent advances in machine learning (ML) have enabled the field of artificial intelligence (AI) to make impressive breakthroughs in complex data environments where human capacity to identify high-dimensional relationships is limited. The field of osteoporosis is one such domain, notwithstanding technical and clinical concerns regarding the application of ML methods. This qualitative review is intended to outline some of these concerns and to inform stakeholders interested in applying AI for improved management of osteoporosis. A systemic search in PubMed and Web of Science resulted in 89 studies for inclusion in the review. These covered one or more of four main areas in osteoporosis management: bone properties assessment (n = 13), osteoporosis classification (n = 34), fracture detection (n = 32), and risk prediction (n = 14). Reporting and methodological quality was determined by means of a 12-point checklist. In general, the studies were of moderate quality with a wide range (mode score 6, range 2 to 11). Major limitations were identified in a significant number of studies. Incomplete reporting, especially over model selection, inadequate splitting of data, and the low proportion of studies with external validation were among the most frequent problems. However, the use of images for opportunistic osteoporosis diagnosis or fracture detection emerged as a promising approach and one of the main contributions that ML could bring to the osteoporosis field. Efforts to develop ML-based models for identifying novel fracture risk factors and improving fracture prediction are additional promising lines of research. Some studies also offered insights into the potential for model-based decision-making. Finally, to avoid some of the common pitfalls, the use of standardized checklists in developing and sharing the results of ML models should be encouraged. © 2021 American Society for Bone and Mineral Research (ASBMR).
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Affiliation(s)
- Julien Smets
- Center of Bone Diseases, Bone and Joint Department, Lausanne University Hospital, Lausanne, Switzerland
| | - Enisa Shevroja
- Center of Bone Diseases, Bone and Joint Department, Lausanne University Hospital, Lausanne, Switzerland
| | - Thomas Hügle
- Department of Rheumatology, Lausanne University Hospital, Lausanne, Switzerland
| | | | - Didier Hans
- Center of Bone Diseases, Bone and Joint Department, Lausanne University Hospital, Lausanne, Switzerland
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Tang C, Zhang W, Li H, Li L, Li Z, Cai A, Wang L, Shi D, Yan B. CNN-based qualitative detection of bone mineral density via diagnostic CT slices for osteoporosis screening. Osteoporos Int 2021; 32:971-979. [PMID: 33165630 DOI: 10.1007/s00198-020-05673-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/19/2019] [Accepted: 10/06/2020] [Indexed: 12/15/2022]
Abstract
UNLABELLED The features extracted from diagnostic computed tomography (CT) slices were used to qualitatively detect bone mineral density (BMD) through neural network models, and the evaluation results indicated that it may be a promising approach to perform osteoporosis screening in clinical practice. INTRODUCTION The purpose of this study is to design a novelty diagnostic method for osteoporosis screening by using the convolutional neural network (CNN), which can be incorporated into the procedure of routine CT diagnostic in medical examination thereby improving the osteoporosis diagnosis and reducing the patient burden. METHODS The proposed CNN-based method mainly comprises two functional modules to perform qualitative detection of BMD by analyzing the diagnostic 2D CT slice. The first functional module aims to locate and segment the ROI of diagnostic 2D CT slice, called Mark-Segmentation-Network (MS-Net). The second functional module is used to determine the category of BMD by the features of ROI, called BMD-Classification-Network (BMDC-Net). The diagnostic 2D CT slice of pedicle level in lumbar vertebrae (L1) was selected from 3D CT image in our experiments firstly. Then, the trained MS-Net can get the mark image of input original 2D CT slice, thereby obtain the segmentation image. Finally, the trained BMDC-Net can obtain the probability value of normal bone mass, low bone mass, and osteoporosis by inputting the segmentation image. On the basis of network results, the radiologists can provide preliminary qualitative diagnosis results of BMD. RESULTS Training of the network was performed on diagnostic 2D CT slices of 150 patients. The network was tested on 63 patients. Each patient corresponds to a 2D CT slice. The proposed MS-Net has an excellent segmentation precision on the shape preservation of different lumbar vertebra. The dice index (DI), pixel accuracy (PA), and intersection over union (IOU) of segmentation results are greater than 0.8. The proposed BMDC-Net achieved an accuracy of 76.65% and an area under the receiver operating characteristic curve of 0.9167. CONCLUSIONS This study proposed a novel method for qualitative detection of BMD via diagnostic CT slices and it has great potential in clinical applications for osteoporosis screening. The method can potentially reduce the manual burden to radiologists and diagnostic cost to patients.
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Affiliation(s)
- C Tang
- PLA Strategy Support Force Information Engineering University, No.62 Science Avenue, Zhengzhou, Henan Province, China
| | - W Zhang
- PLA Strategy Support Force Information Engineering University, No.62 Science Avenue, Zhengzhou, Henan Province, China
| | - H Li
- Department of Radiology in Henan Provincial People's Hospital, Zhengzhou, Henan Province, China
| | - L Li
- PLA Strategy Support Force Information Engineering University, No.62 Science Avenue, Zhengzhou, Henan Province, China
| | - Z Li
- PLA Strategy Support Force Information Engineering University, No.62 Science Avenue, Zhengzhou, Henan Province, China
| | - A Cai
- PLA Strategy Support Force Information Engineering University, No.62 Science Avenue, Zhengzhou, Henan Province, China
| | - L Wang
- PLA Strategy Support Force Information Engineering University, No.62 Science Avenue, Zhengzhou, Henan Province, China
| | - D Shi
- Department of Radiology in Henan Provincial People's Hospital, Zhengzhou, Henan Province, China
| | - B Yan
- PLA Strategy Support Force Information Engineering University, No.62 Science Avenue, Zhengzhou, Henan Province, China.
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Menaka R, Ramesh R, Dhanagopal R. Aggregation of Region-based and Boundary-based Knowledge Biased Segmentation for Osteoporosis Detection from X-Ray, Dual X-Ray and CT Images. Curr Med Imaging 2021; 17:288-295. [PMID: 32748751 DOI: 10.2174/1573405616999200730175526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Revised: 06/03/2020] [Accepted: 06/19/2020] [Indexed: 11/22/2022]
Abstract
BACKGROUND Osteoporosis is a term used to represent the reduced bone density, which is caused by insufficient bone tissue production to balance the old bone tissue removal. Medical Imaging procedures such as X-Ray, Dual X-Ray and Computed Tomography (CT) scans are used widely in osteoporosis diagnosis. There are several existing procedures in practice to assist osteoporosis diagnosis, which can operate using a single imaging method. OBJECTIVE The purpose of this proposed work is to introduce a framework to assist the diagnosis of osteoporosis based on consenting all these X-Ray, Dual X-Ray and CT scan imaging techniques. The proposed work named "Aggregation of Region-based and Boundary-based Knowledge biased Segmentation for Osteoporosis Detection from X-Ray, Dual X-Ray and CT images" (ARBKSOD) is the integration of three functional modules. METHODS Fuzzy Histogram Medical Image Classifier (FHMIC), Log-Gabor Transform based ANN Training for osteoporosis detection (LGTAT) and Knowledge biased Osteoporosis Analyzer (KOA). RESULTS Together, all these three modules make the proposed method ARBKSOD scored the maximum accuracy of 93.11%, the highest precision value of 93.91% while processing the 6th image batch, the highest sensitivity of 92.93%, the highest specificity of 93.79% is observed during the experiment by ARBKSOD while processing the 6th image batch. The best average processing time of 10244 mS is achieved by ARBKSOD while processing the 7th image batch. CONCLUSION Together, all these three modules make the proposed method ARBKSOD to produce a better result.
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Affiliation(s)
- R Menaka
- Department of Chennai Institute of Technology, Chennai, Tamil Nadu, India
| | - R Ramesh
- Department of Chennai Institute of Technology, Chennai, Tamil Nadu, India
| | - R Dhanagopal
- Department of Chennai Institute of Technology, Chennai, Tamil Nadu, India
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Burian E, Grundl L, Greve T, Junker D, Sollmann N, Löffler M, Makowski MR, Zimmer C, Kirschke JS, Baum T. Local Bone Mineral Density, Subcutaneous and Visceral Adipose Tissue Measurements in Routine Multi Detector Computed Tomography-Which Parameter Predicts Incident Vertebral Fractures Best? Diagnostics (Basel) 2021; 11:diagnostics11020240. [PMID: 33557092 PMCID: PMC7913817 DOI: 10.3390/diagnostics11020240] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2021] [Revised: 01/31/2021] [Accepted: 02/01/2021] [Indexed: 11/16/2022] Open
Abstract
In this case-control study the value of bone mineral density (BMD) at different vertebral levels, subcutaneous adipose tissue (SAT), and visceral adipose tissue (VAT) to identify patients with incident osteoporotic vertebral fractures in routine multi-detector computed tomography (MDCT) exams was assessed. MATERIAL AND METHODS Seventeen patients who underwent baseline and follow-up routine contrast-enhanced MDCT and had an incident osteoporotic vertebral fracture at follow-up were included. Seventeen age-, sex- and follow-up duration-matched controls were identified. Trabecular BMD (from Th5 to L5) as well as cross-sectional area of SAT and VAT were extracted. RESULTS BMD performed best to differentiate patients with an incident fracture from controls at the levels of Th5 (area under the curve [AUC] = 0.781, p = 0.014), Th7 (AUC = 0.877, p = 0.001), and Th9 (AUC = 0.818, p = 0.005). Applying multivariate logistic regression BMD at Th7 level remained the only significant predictor of incident vertebral fractures (Th5-L5) with an odds ratio of 1.07 per BMD SD decrease. VAT and SAT did not show significant differences between the fracture and control group (p > 0.05). CONCLUSION The local BMD measurement appears to be more suitable than standard mean BMD from L1-L3 for fracture risk assessment.
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Affiliation(s)
- Egon Burian
- Department of Diagnostic and Interventional Neuroradiology, Klinikum Rechts der Isar, TUM School of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany; (L.G.); (T.G.); (N.S.); (M.L.); (C.Z.); (J.S.K.); (T.B.)
- Department of Diagnostic and Interventional Radiology, Klinikum Rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany; (D.J.); (M.R.M.)
- Correspondence: ; Tel.: +49-89-4140-8791
| | - Lioba Grundl
- Department of Diagnostic and Interventional Neuroradiology, Klinikum Rechts der Isar, TUM School of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany; (L.G.); (T.G.); (N.S.); (M.L.); (C.Z.); (J.S.K.); (T.B.)
| | - Tobias Greve
- Department of Diagnostic and Interventional Neuroradiology, Klinikum Rechts der Isar, TUM School of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany; (L.G.); (T.G.); (N.S.); (M.L.); (C.Z.); (J.S.K.); (T.B.)
- Department of Neurosurgery, Ludwig-Maximilians-University, Marchioninistraße 15, 81377 Munich, Germany
| | - Daniela Junker
- Department of Diagnostic and Interventional Radiology, Klinikum Rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany; (D.J.); (M.R.M.)
| | - Nico Sollmann
- Department of Diagnostic and Interventional Neuroradiology, Klinikum Rechts der Isar, TUM School of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany; (L.G.); (T.G.); (N.S.); (M.L.); (C.Z.); (J.S.K.); (T.B.)
| | - Maximilian Löffler
- Department of Diagnostic and Interventional Neuroradiology, Klinikum Rechts der Isar, TUM School of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany; (L.G.); (T.G.); (N.S.); (M.L.); (C.Z.); (J.S.K.); (T.B.)
| | - Marcus R. Makowski
- Department of Diagnostic and Interventional Radiology, Klinikum Rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany; (D.J.); (M.R.M.)
| | - Claus Zimmer
- Department of Diagnostic and Interventional Neuroradiology, Klinikum Rechts der Isar, TUM School of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany; (L.G.); (T.G.); (N.S.); (M.L.); (C.Z.); (J.S.K.); (T.B.)
| | - Jan S. Kirschke
- Department of Diagnostic and Interventional Neuroradiology, Klinikum Rechts der Isar, TUM School of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany; (L.G.); (T.G.); (N.S.); (M.L.); (C.Z.); (J.S.K.); (T.B.)
| | - Thomas Baum
- Department of Diagnostic and Interventional Neuroradiology, Klinikum Rechts der Isar, TUM School of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany; (L.G.); (T.G.); (N.S.); (M.L.); (C.Z.); (J.S.K.); (T.B.)
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Yeung LY, Rayudu NM, Löffler M, Sekuboyina A, Burian E, Sollmann N, Dieckmeyer M, Greve T, Kirschke JS, Subburaj K, Baum T. Prediction of Incidental Osteoporotic Fractures at Vertebral-Specific Level Using 3D Non-Linear Finite Element Parameters Derived from Routine Abdominal MDCT. Diagnostics (Basel) 2021; 11:208. [PMID: 33573295 PMCID: PMC7911185 DOI: 10.3390/diagnostics11020208] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Revised: 01/27/2021] [Accepted: 01/28/2021] [Indexed: 02/06/2023] Open
Abstract
To investigate whether finite element (FE) analysis of the spine in routine thoracic/abdominal multi-detector computed tomography (MDCT) can predict incidental osteoporotic fractures at vertebral-specific level; Baseline routine thoracic/abdominal MDCT scans of 16 subjects (8(m), mean age: 66.1 ± 8.2 years and 8(f), mean age: 64.3 ± 9.5 years) who sustained incidental osteoporotic vertebral fractures as confirmed in follow-up MDCTs were included in the current study. Thoracic and lumbar vertebrae (T5-L5) were automatically segmented, and bone mineral density (BMD), finite element (FE)-based failure-load, and failure-displacement were determined. These values of individual vertebrae were normalized globally (g), by dividing the absolute value with the average of L1-3 and locally by dividing the absolute value with the average of T5-12 and L1-5 for thoracic and lumbar vertebrae, respectively. Mean-BMD of L1-3 was determined as reference. Receiver operating characteristics (ROC) and area under the curve (AUC) were calculated for different normalized FE (Kload, Kdisplacement,K(load)g, and K(displacement)g) and BMD (KBMD, and K(BMD)g) ratio parameter combinations for identifying incidental fractures. Kload, K(load)g, KBMD, and K(BMD)g showed significantly higher discriminative power compared to standard mean BMD of L1-3 (BMDStandard) (AUC = 0.67 for Kload; 0.64 for K(load)g; 0.64 for KBMD; 0.61 for K(BMD)g vs. 0.54 for BMDStandard). The combination of Kload, Kdisplacement, and KBMD increased the AUC further up to 0.77 (p < 0.001). The combination of FE with BMD measurements derived from routine thoracic/abdominal MDCT allowed an improved prediction of incidental fractures at vertebral-specific level.
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Affiliation(s)
- Long Yu Yeung
- Engineering Product Development (EPD) Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore; (L.Y.Y.); (N.M.R.)
| | - Nithin Manohar Rayudu
- Engineering Product Development (EPD) Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore; (L.Y.Y.); (N.M.R.)
| | - Maximilian Löffler
- Department of Diagnostic and Interventional Neuroradiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Ismaninger Street 22, 81675 Munich, Germany; (M.L.); (A.S.); (E.B.); (N.S.); (M.D.); (T.G.); (J.S.K.); (T.B.)
| | - Anjany Sekuboyina
- Department of Diagnostic and Interventional Neuroradiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Ismaninger Street 22, 81675 Munich, Germany; (M.L.); (A.S.); (E.B.); (N.S.); (M.D.); (T.G.); (J.S.K.); (T.B.)
| | - Egon Burian
- Department of Diagnostic and Interventional Neuroradiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Ismaninger Street 22, 81675 Munich, Germany; (M.L.); (A.S.); (E.B.); (N.S.); (M.D.); (T.G.); (J.S.K.); (T.B.)
| | - Nico Sollmann
- Department of Diagnostic and Interventional Neuroradiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Ismaninger Street 22, 81675 Munich, Germany; (M.L.); (A.S.); (E.B.); (N.S.); (M.D.); (T.G.); (J.S.K.); (T.B.)
- TUM-Neuroimaging Center, Klinikum Rechts der Isar, Technical University of Munich, 81675 Munich, Germany
- Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - Michael Dieckmeyer
- Department of Diagnostic and Interventional Neuroradiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Ismaninger Street 22, 81675 Munich, Germany; (M.L.); (A.S.); (E.B.); (N.S.); (M.D.); (T.G.); (J.S.K.); (T.B.)
| | - Tobias Greve
- Department of Diagnostic and Interventional Neuroradiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Ismaninger Street 22, 81675 Munich, Germany; (M.L.); (A.S.); (E.B.); (N.S.); (M.D.); (T.G.); (J.S.K.); (T.B.)
- Department of Neurosurgery, Ludwig-Maximilians-University, Marchioninistraße 15, 81377 Munich, Germany
| | - Jan S. Kirschke
- Department of Diagnostic and Interventional Neuroradiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Ismaninger Street 22, 81675 Munich, Germany; (M.L.); (A.S.); (E.B.); (N.S.); (M.D.); (T.G.); (J.S.K.); (T.B.)
- TUM-Neuroimaging Center, Klinikum Rechts der Isar, Technical University of Munich, 81675 Munich, Germany
| | - Karupppasamy Subburaj
- Engineering Product Development (EPD) Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore; (L.Y.Y.); (N.M.R.)
- Changi General Hospital, 2 Simei Street 3, Singapore 529889, Singapore
| | - Thomas Baum
- Department of Diagnostic and Interventional Neuroradiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Ismaninger Street 22, 81675 Munich, Germany; (M.L.); (A.S.); (E.B.); (N.S.); (M.D.); (T.G.); (J.S.K.); (T.B.)
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Sollmann N, Becherucci EA, Boehm C, Husseini ME, Ruschke S, Burian E, Kirschke JS, Link TM, Subburaj K, Karampinos DC, Krug R, Baum T, Dieckmeyer M. Texture Analysis Using CT and Chemical Shift Encoding-Based Water-Fat MRI Can Improve Differentiation Between Patients With and Without Osteoporotic Vertebral Fractures. Front Endocrinol (Lausanne) 2021; 12:778537. [PMID: 35058878 PMCID: PMC8763669 DOI: 10.3389/fendo.2021.778537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Accepted: 12/06/2021] [Indexed: 11/13/2022] Open
Abstract
PURPOSE Osteoporosis is a highly prevalent skeletal disease that frequently entails vertebral fractures. Areal bone mineral density (BMD) derived from dual-energy X-ray absorptiometry (DXA) is the reference standard, but has well-known limitations. Texture analysis can provide surrogate markers of tissue microstructure based on computed tomography (CT) or magnetic resonance imaging (MRI) data of the spine, thus potentially improving fracture risk estimation beyond areal BMD. However, it is largely unknown whether MRI-derived texture analysis can predict volumetric BMD (vBMD), or whether a model incorporating texture analysis based on CT and MRI may be capable of differentiating between patients with and without osteoporotic vertebral fractures. MATERIALS AND METHODS Twenty-six patients (15 females, median age: 73 years, 11 patients showing at least one osteoporotic vertebral fracture) who had CT and 3-Tesla chemical shift encoding-based water-fat MRI (CSE-MRI) available were analyzed. In total, 171 vertebral bodies of the thoracolumbar spine were segmented using an automatic convolutional neural network (CNN)-based framework, followed by extraction of integral and trabecular vBMD using CT data. For CSE-MRI, manual segmentation of vertebral bodies and consecutive extraction of the mean proton density fat fraction (PDFF) and T2* was performed. First-order, second-order, and higher-order texture features were derived from texture analysis using CT and CSE-MRI data. Stepwise multivariate linear regression models were computed using integral vBMD and fracture status as dependent variables. RESULTS Patients with osteoporotic vertebral fractures showed significantly lower integral and trabecular vBMD when compared to patients without fractures (p<0.001). For the model with integral vBMD as the dependent variable, T2* combined with three PDFF-based texture features explained 40% of the variance (adjusted R2[Ra2] = 0.40; p<0.001). Furthermore, regarding the differentiation between patients with and without osteoporotic vertebral fractures, a model including texture features from CT and CSE-MRI data showed better performance than a model based on integral vBMD and PDFF only ( Ra2 = 0.47 vs. Ra2 = 0.81; included texture features in the final model: integral vBMD, CT_Short-run_emphasis, CT_Varianceglobal, and PDFF_Variance). CONCLUSION Using texture analysis for spine CT and CSE-MRI can facilitate the differentiation between patients with and without osteoporotic vertebral fractures, implicating that future fracture prediction in osteoporosis may be improved.
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Affiliation(s)
- Nico Sollmann
- Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Ulm, Germany
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, United States
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- *Correspondence: Nico Sollmann,
| | - Edoardo A. Becherucci
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Christof Boehm
- Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Malek El Husseini
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Stefan Ruschke
- Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Egon Burian
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Jan S. Kirschke
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Thomas M. Link
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, United States
| | - Karupppasamy Subburaj
- Engineering Product Development (EPD) Pillar, Singapore University of Technology and Design (SUTD), Singapore, Singapore
- Changi General Hospital, Singapore, Singapore
| | - Dimitrios C. Karampinos
- Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Roland Krug
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, United States
| | - Thomas Baum
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Michael Dieckmeyer
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
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Dieckmeyer M, Sollmann N, El Husseini M, Sekuboyina A, Löffler MT, Zimmer C, Kirschke JS, Subburaj K, Baum T. Gender-, Age- and Region-Specific Characterization of Vertebral Bone Microstructure Through Automated Segmentation and 3D Texture Analysis of Routine Abdominal CT. Front Endocrinol (Lausanne) 2021; 12:792760. [PMID: 35154004 PMCID: PMC8828577 DOI: 10.3389/fendo.2021.792760] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Accepted: 12/27/2021] [Indexed: 11/13/2022] Open
Abstract
PURPOSE To identify long-term reproducible texture features (TFs) of spinal computed tomography (CT), and characterize variations with regard to gender, age and vertebral level using our automated quantification framework. METHODS We performed texture analysis (TA) on baseline and follow-up CT (follow-up duration: 30-90 days) of 21 subjects (8 females, 13 males, age at baseline 61.2 ± 9.2 years) to determine long-term reproducibility. TFs with a long-term reproducibility error Δrel<5% were further analyzed for an association with age and vertebral level in a cohort of 376 patients (129 females, 247 males, age 62.5 ± 9.2 years). Automated analysis comprised labeling and segmentation of vertebrae into subregions using a convolutional neural network, calculation of volumetric bone mineral density (vBMD) with asynchronous calibration and TF extraction. Varianceglobal measures the spread of the gray-level distribution in an image while Entropy reflects the uniformity of gray-levels. Short-run emphasis (SRE), Long-run emphasis (LRE), Run-length non-uniformity (RLN) and Run percentage (RP) contain information on consecutive voxels of a particular grey-level, or grey-level range, in a particular direction. Long runs (LRE) represent coarse texture while short runs (SRE) represent fine texture. RLN reflects similarities in the length of runs while RP reflects distribution and homogeneity of runs with a specific direction. RESULTS Six of the 24 extracted TFs had Δrel<5% (Varianceglobal, Entropy, SRE, LRE, RLN, RP), and were analyzed further in 4716 thoracolumbar vertebrae. Five TFs (Varianceglobal,SRE,LRE, RLN,RP) showed a significant difference between genders (p<0.001), potentially being caused by a finer and more directional vertebral trabecular microstructure in females compared to males. Varianceglobal and Entropy showed a significant increase from the thoracic to the lumbar spine (p<0.001), indicating a higher degree and earlier initiation of trabecular microstructure deterioration at lower spinal levels. The four higher-order TFs showed significant variations between spine regions without a clear directional gradient (p ≤ 0.001-0.012). No TF showed a clear age dependence. vBMD differed significantly between genders, age groups and spine regions (p ≤ 0.001-0.002). CONCLUSION Long-term reproducible CT-based TFs of the thoracolumbar spine were established and characterized in a predominantly older adult study population. The gender-, age- and vertebral-level-specific values may serve as foundation for osteoporosis diagnostics and facilitate future studies investigating vertebral microstructure.
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Affiliation(s)
- Michael Dieckmeyer
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- *Correspondence: Michael Dieckmeyer,
| | - Nico Sollmann
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Ulm, Germany
| | - Malek El Husseini
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Anjany Sekuboyina
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Maximilian T. Löffler
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- Department of Radiology, University Medical Center, Albert-Ludwigs-University Freiburg, Freiburg, Germany
| | - Claus Zimmer
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Jan S. Kirschke
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Karupppasamy Subburaj
- Pillar of Engineering Product Development, Singapore University of Technology and Design, Singapore, Singapore
- Changi General Hospital, Singapore, Singapore
| | - Thomas Baum
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
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Hong N, Park Y, You SC, Rhee Y. AIM in Endocrinology. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_328-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2022]
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Yamamoto N, Sukegawa S, Kitamura A, Goto R, Noda T, Nakano K, Takabatake K, Kawai H, Nagatsuka H, Kawasaki K, Furuki Y, Ozaki T. Deep Learning for Osteoporosis Classification Using Hip Radiographs and Patient Clinical Covariates. Biomolecules 2020; 10:biom10111534. [PMID: 33182778 PMCID: PMC7697189 DOI: 10.3390/biom10111534] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Revised: 11/08/2020] [Accepted: 11/08/2020] [Indexed: 01/10/2023] Open
Abstract
This study considers the use of deep learning to diagnose osteoporosis from hip radiographs, and whether adding clinical data improves diagnostic performance over the image mode alone. For objective labeling, we collected a dataset containing 1131 images from patients who underwent both skeletal bone mineral density measurement and hip radiography at a single general hospital between 2014 and 2019. Osteoporosis was assessed from the hip radiographs using five convolutional neural network (CNN) models. We also investigated ensemble models with clinical covariates added to each CNN. The accuracy, precision, recall, specificity, negative predictive value (npv), F1 score, and area under the curve (AUC) score were calculated for each network. In the evaluation of the five CNN models using only hip radiographs, GoogleNet and EfficientNet b3 exhibited the best accuracy, precision, and specificity. Among the five ensemble models, EfficientNet b3 exhibited the best accuracy, recall, npv, F1 score, and AUC score when patient variables were included. The CNN models diagnosed osteoporosis from hip radiographs with high accuracy, and their performance improved further with the addition of clinical covariates from patient records.
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Affiliation(s)
- Norio Yamamoto
- Department of Orthopaedic Surgery, Kagawa Prefectural Central Hospital, Takamatsu, Kagawa 760-8557, Japan; (N.Y.); (K.K.)
| | - Shintaro Sukegawa
- Department of Oral and Maxillofacial Surgery, Kagawa Prefectural Central Hospital, 1-2-1, Asahi-machi, Takamatsu, Kagawa 760-8557, Japan;
- Department of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama 700-8525, Japan; (K.N.); (K.T.); (H.K.); (H.N.)
- Correspondence: ; Tel.: +81-87-811-3333; Fax: +81-87-835-8363
| | - Akira Kitamura
- Search Space Inc., Tokyo 151-0072, Japan; (A.K.); (R.G.)
| | - Ryosuke Goto
- Search Space Inc., Tokyo 151-0072, Japan; (A.K.); (R.G.)
| | - Tomoyuki Noda
- Department of Musculoskeletal Traumatology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama 700-8558, Japan;
| | - Keisuke Nakano
- Department of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama 700-8525, Japan; (K.N.); (K.T.); (H.K.); (H.N.)
| | - Kiyofumi Takabatake
- Department of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama 700-8525, Japan; (K.N.); (K.T.); (H.K.); (H.N.)
| | - Hotaka Kawai
- Department of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama 700-8525, Japan; (K.N.); (K.T.); (H.K.); (H.N.)
| | - Hitoshi Nagatsuka
- Department of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama 700-8525, Japan; (K.N.); (K.T.); (H.K.); (H.N.)
| | - Keisuke Kawasaki
- Department of Orthopaedic Surgery, Kagawa Prefectural Central Hospital, Takamatsu, Kagawa 760-8557, Japan; (N.Y.); (K.K.)
| | - Yoshihiko Furuki
- Department of Oral and Maxillofacial Surgery, Kagawa Prefectural Central Hospital, 1-2-1, Asahi-machi, Takamatsu, Kagawa 760-8557, Japan;
| | - Toshifumi Ozaki
- Department of Orthopaedic Surgery, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama 700-8558, Japan;
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Petraikin AV, Belaya ZE, Kiseleva AN, Artyukova ZR, Belyaev MG, Kondratenko VA, Pisov ME, Solovev AV, Smorchkova AK, Abuladze LR, Kieva IN, Fedanov VA, Iassin LR, Semenov DS, Kudryavtsev ND, Shchelykalina SP, Zinchenko VV, Akhmad ES, Sergunova KA, Gombolevsky VA, Nisovstova LA, Vladzymyrskyy AV, Morozov SP. [Artificial intelligence for diagnosis of vertebral compression fractures using a morphometric analysis model, based on convolutional neural networks]. ACTA ACUST UNITED AC 2020; 66:48-60. [PMID: 33369372 DOI: 10.14341/probl12605] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Revised: 09/03/2020] [Accepted: 09/21/2020] [Indexed: 11/06/2022]
Abstract
BACKGROUND Pathological low-energy (LE) vertebral compression fractures (VFs) are common complications of osteoporosis and predictors of subsequent LE fractures. In 84% of cases, VFs are not reported on chest CT (CCT), which calls for the development of an artificial intelligence-based (AI) assistant that would help radiology specialists to improve the diagnosis of osteoporosis complications and prevent new LE fractures. AIMS To develop an AI model for automated diagnosis of compression fractures of the thoracic spine based on chest CT images. MATERIALS AND METHODS Between September 2019 and May 2020 the authors performed a retrospective sampling study of ССТ images. The 160 of results were selected and anonymized. The data was labeled by seven readers. Using the morphometric analysis, the investigators received the following metric data: ventral, medial and dorsal dimensions. This was followed by a semiquantitative assessment of VFs degree. The data was used to develop the Comprise-G AI mode based on CNN, which subsequently measured the size of the vertebral bodies and then calculates the compression degree. The model was evaluated with the ROC curve analysis and by calculating sensitivity and specificity values. RESULTS Formed data consist of 160 patients (a training group - 100 patients; a test group - 60 patients). The total of 2,066 vertebrae was annotated. When detecting Grade 2 and 3 maximum VFs in patients the Comprise-G model demonstrated sensitivity - 90,7%, specificity - 90,7%, AUC ROC - 0.974 on the 5-FOLD cross-validation data of the training dataset; on the test data - sensitivity - 83,2%, specificity - 90,0%, AUC ROC - 0.956; in vertebrae demonstrated sensitivity - 91,5%, specificity - 95,2%, AUC ROC - 0.981 on the cross-validation data; for the test data sensitivity - 79,3%, specificity - 98,7%, AUC ROC - 0.978. CONCLUSIONS The Comprise-G model demonstrated high diagnostic capabilities in detecting the VFs on CCT images and can be recommended for further validation.
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Affiliation(s)
- A V Petraikin
- Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies
| | | | | | - Z R Artyukova
- Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies
| | - M G Belyaev
- Skolkovo Institute of Science and Technology
| | | | - M E Pisov
- Skolkovo Institute of Science and Technology; Kharkevich Institute for Information Transmission Problems
| | - A V Solovev
- Sklifosovsky Clinical and Research Institute of Emergency Medicine
| | - A K Smorchkova
- Central State Medical Academy of the Presidential Administration of the Russian Federation
| | | | - I N Kieva
- Peoples' Friendship University of Russia
| | - V A Fedanov
- Central State Medical Academy of the Presidential Administration of the Russian Federation
| | | | - D S Semenov
- Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies
| | | | | | - V V Zinchenko
- Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies
| | - E S Akhmad
- Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies
| | - K A Sergunova
- Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies
| | - V A Gombolevsky
- Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies
| | - L A Nisovstova
- Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies
| | - A V Vladzymyrskyy
- Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies
| | - S P Morozov
- Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies
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Opportunistic osteoporosis screening in multi-detector CT images using deep convolutional neural networks. Eur Radiol 2020; 31:1831-1842. [PMID: 33001308 DOI: 10.1007/s00330-020-07312-8] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 07/29/2020] [Accepted: 09/17/2020] [Indexed: 10/23/2022]
Abstract
OBJECTIVE To explore the application of deep learning in patients with primary osteoporosis, and to develop a fully automatic method based on deep convolutional neural network (DCNN) for vertebral body segmentation and bone mineral density (BMD) calculation in CT images. MATERIALS AND METHODS A total of 1449 patients were used for experiments and analysis in this retrospective study, who underwent spinal or abdominal CT scans for other indications between March 2018 and May 2020. All data was gathered from three different CT vendors. Among them, 586 cases were used for training, and other 863 cases were used for testing. A fully convolutional neural network, called U-Net, was employed for automated vertebral body segmentation. The manually sketched region of vertebral body was used as the ground truth for comparison. A convolutional neural network, called DenseNet-121, was applied for BMD calculation. The values post-processed by quantitative computed tomography (QCT) were identified as the standards for analysis. RESULTS Based on the diversity of CT vendors, all testing cases were split into three testing cohorts: Test set 1 (n = 463), test set 2 (n = 200), and test set 3 (n = 200). Automated segmentation correlated well with manual segmentation regarding four lumbar vertebral bodies (L1-L4): the minimum average dice coefficients for three testing sets were 0.823, 0.786, and 0.782, respectively. For testing sets from different vendors, the average BMDs calculated by automated regression showed high correlation (r > 0.98) and agreement with those derived from QCT. CONCLUSIONS A deep learning-based method could achieve fully automatic identification of osteoporosis, osteopenia, and normal bone mineral density in CT images. KEY POINTS • Deep learning can perform accurate fully automated segmentation of lumbar vertebral body in CT images. • The average BMDs obtained by deep learning highly correlates with ones derived from QCT. • The deep learning-based method could be helpful for clinicians in opportunistic osteoporosis screening in spinal or abdominal CT scans.
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Röhrich S, Hofmanninger J, Prayer F, Müller H, Prosch H, Langs G. Prospects and Challenges of Radiomics by Using Nononcologic Routine Chest CT. Radiol Cardiothorac Imaging 2020; 2:e190190. [PMID: 33778599 DOI: 10.1148/ryct.2020190190] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2019] [Revised: 03/10/2020] [Accepted: 04/21/2020] [Indexed: 02/06/2023]
Abstract
Chest CT scans are one of the most common medical imaging procedures. The automatic extraction and quantification of imaging features may help in diagnosis, prognosis of, or treatment decision in cardiovascular, pulmonary, and metabolic diseases. However, an adequate sample size as a statistical necessity for radiomics studies is often difficult to achieve in prospective trials. By exploiting imaging data from clinical routine, a much larger amount of data could be used than in clinical trials. Still, there is only little literature on the implementation of radiomics in clinical routine chest CT scans. Reasons are heterogeneous CT scanning protocols and the resulting technical variability (eg, different slice thicknesses, reconstruction kernels or timings after contrast material administration) in routine CT imaging data. This review summarizes the recent state of the art of studies aiming to develop quantifiable imaging biomarkers at chest CT, such as for osteoporosis, chronic obstructive pulmonary disease, interstitial lung disease, and coronary artery disease. This review explains solutions to overcome heterogeneity in routine data such as the use of imaging repositories, the standardization of radiomic features, algorithmic approaches to improve feature stability, test-retest studies, and the evolution of deep learning for modeling radiomics features. Supplemental material is available for this article. © RSNA, 2020 See also the commentary by Kay in this issue.
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Affiliation(s)
- Sebastian Röhrich
- Computational Imaging Research Laboratory (J.H., G.L) of the Department of Biomedical Imaging and Image-guided Therapy (S.R., F.P., H.P.), Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria; and Department of Information Systems, University of Applied Sciences of Western Switzerland, Sierre, Switzerland (H.M.)
| | - Johannes Hofmanninger
- Computational Imaging Research Laboratory (J.H., G.L) of the Department of Biomedical Imaging and Image-guided Therapy (S.R., F.P., H.P.), Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria; and Department of Information Systems, University of Applied Sciences of Western Switzerland, Sierre, Switzerland (H.M.)
| | - Florian Prayer
- Computational Imaging Research Laboratory (J.H., G.L) of the Department of Biomedical Imaging and Image-guided Therapy (S.R., F.P., H.P.), Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria; and Department of Information Systems, University of Applied Sciences of Western Switzerland, Sierre, Switzerland (H.M.)
| | - Henning Müller
- Computational Imaging Research Laboratory (J.H., G.L) of the Department of Biomedical Imaging and Image-guided Therapy (S.R., F.P., H.P.), Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria; and Department of Information Systems, University of Applied Sciences of Western Switzerland, Sierre, Switzerland (H.M.)
| | - Helmut Prosch
- Computational Imaging Research Laboratory (J.H., G.L) of the Department of Biomedical Imaging and Image-guided Therapy (S.R., F.P., H.P.), Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria; and Department of Information Systems, University of Applied Sciences of Western Switzerland, Sierre, Switzerland (H.M.)
| | - Georg Langs
- Computational Imaging Research Laboratory (J.H., G.L) of the Department of Biomedical Imaging and Image-guided Therapy (S.R., F.P., H.P.), Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria; and Department of Information Systems, University of Applied Sciences of Western Switzerland, Sierre, Switzerland (H.M.)
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