1
|
Park MS, Ha HI, Lim HK, Han J, Pak S. Femoral osteoporosis prediction model using autosegmentation and machine learning analysis with PyRadiomics on abdomen-pelvic computed tomography (CT). Quant Imaging Med Surg 2024; 14:3959-3969. [PMID: 38846273 PMCID: PMC11151236 DOI: 10.21037/qims-23-1751] [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: 12/11/2023] [Accepted: 04/07/2024] [Indexed: 06/09/2024]
Abstract
Background With the advancement of artificial intelligence technology and radiomics analysis, opportunistic prediction of osteoporosis with computed tomography (CT) is a new paradigm in osteoporosis screening. This study aimed to assess the diagnostic performance of osteoporosis prediction by the combination of autosegmentation of the proximal femur and machine learning analysis with a reference standard of dual-energy X-ray absorptiometry (DXA). Methods Abdomen-pelvic CT scans were retrospectively analyzed from 1,122 patients who received both DXA and abdomen-pelvic computed tomography (APCT) scan from January 2018 to December 2020. The study cohort consisted of a training cohort and a temporal validation cohort. The left proximal femur was automatically segmented, and a prediction model was built by machine-learning analysis using a random forest (RF) analysis and 854 PyRadiomics features. The technical success rate of autosegmentation, diagnostic test, area under the receiver operator characteristics curve (AUC), and precision recall curve (AUC-PR) analysis were used to analyze the training and validation cohorts. Results The osteoporosis prevalence of the training and validation cohorts was 24.5%, and 10.3%, respectively. The technical success rate of autosegmentation of the proximal femur was 99.7%. In the diagnostic test, the training and validation cohorts showed 78.4% vs. 63.3% sensitivity, 89.4% vs. 98.1% specificity. The prediction performance to identify osteoporosis within the groups used for training and validation cohort was high and the AUC and AUC-PR to forecast the occurrence of osteoporosis within the training and validation cohorts were 90.8% [95% confidence interval (CI), 88.4-93.2%] vs. 78.0% (95% CI, 76.0-79.9%) and 94.6% (95% CI, 89.3-99.8%) vs. 88.8% (95% CI, 86.2-91.5%), respectively. Conclusions The osteoporosis prediction model using autosegmentation of proximal femur and machine-learning analysis with PyRadiomics features on APCT showed excellent diagnostic feasibility and technical success.
Collapse
Affiliation(s)
- Min Su Park
- Department of Radiology, Hallym University Sacred Heart Hospital, Anyang-si, Gyeonggi-do, Republic of Korea
| | - Hong Il Ha
- Department of Radiology, Hallym University Sacred Heart Hospital, Anyang-si, Gyeonggi-do, Republic of Korea
| | - Hyun Kyung Lim
- Department of Radiology, Soonchunhyang University Seoul Hospital, Seoul, Republic of Korea
| | - Junhee Han
- Department of Statistics and Data Science Convergence Research Center, Hallym University, Chuncheon-si, Gangwon-do, Republic of Korea
| | - Seongyong Pak
- CT Research Collaboration, Siemens-Healthineers, Seoul, Republic of Korea
| |
Collapse
|
2
|
Park JY, Lee SH, Kim YJ, Kim KG, Lee GJ. Machine learning model based on radiomics features for AO/OTA classification of pelvic fractures on pelvic radiographs. PLoS One 2024; 19:e0304350. [PMID: 38814948 PMCID: PMC11139281 DOI: 10.1371/journal.pone.0304350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2024] [Accepted: 05/10/2024] [Indexed: 06/01/2024] Open
Abstract
Depending on the degree of fracture, pelvic fracture can be accompanied by vascular damage, and in severe cases, it may progress to hemorrhagic shock. Pelvic radiography can quickly diagnose pelvic fractures, and the Association for Osteosynthesis Foundation and Orthopedic Trauma Association (AO/OTA) classification system is useful for evaluating pelvic fracture instability. This study aimed to develop a radiomics-based machine-learning algorithm to quickly diagnose fractures on pelvic X-ray and classify their instability. data used were pelvic anteroposterior radiographs of 990 adults over 18 years of age diagnosed with pelvic fractures, and 200 normal subjects. A total of 93 features were extracted based on radiomics:18 first-order, 24 GLCM, 16 GLRLM, 16 GLSZM, 5 NGTDM, and 14 GLDM features. To improve the performance of machine learning, the feature selection methods RFE, SFS, LASSO, and Ridge were used, and the machine learning models used LR, SVM, RF, XGB, MLP, KNN, and LGBM. Performance measurement was evaluated by area under the curve (AUC) by analyzing the receiver operating characteristic curve. The machine learning model was trained based on the selected features using four feature-selection methods. When the RFE feature selection method was used, the average AUC was higher than that of the other methods. Among them, the combination with the machine learning model SVM showed the best performance, with an average AUC of 0.75±0.06. By obtaining a feature-importance graph for the combination of RFE and SVM, it is possible to identify features with high importance. The AO/OTA classification of normal pelvic rings and pelvic fractures on pelvic AP radiographs using a radiomics-based machine learning model showed the highest AUC when using the SVM classification combination. Further research on the radiomic features of each part of the pelvic bone constituting the pelvic ring is needed.
Collapse
Affiliation(s)
- Jun Young Park
- Department of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology (GAIHST), Gachon University, Incheon, Republic of Korea
| | - Seung Hwan Lee
- Department of Trauma Surgery, Gachon University Gil Medical Center, Gachon University, Incheon, Republic of Korea
- Department of Traumatology, Gachon University College of Medicine, Gachon University, Incheon, Republic of Korea
| | - Young Jae Kim
- Department of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology (GAIHST), Gachon University, Incheon, Republic of Korea
- Department of Medical Devices R&D Center, Gachon University Gil Medical Center, Gachon University, Incheon, Republic of Korea
- Department of Biomedical Engineering, Pre-medical Course, College of Medicine, Gachon University, Incheon, Republic of Korea
| | - Kwang Gi Kim
- Department of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology (GAIHST), Gachon University, Incheon, Republic of Korea
- Department of Medical Devices R&D Center, Gachon University Gil Medical Center, Gachon University, Incheon, Republic of Korea
- Department of Biomedical Engineering, Pre-medical Course, College of Medicine, Gachon University, Incheon, Republic of Korea
| | - Gil Jae Lee
- Department of Trauma Surgery, Gachon University Gil Medical Center, Gachon University, Incheon, Republic of Korea
- Department of Traumatology, Gachon University College of Medicine, Gachon University, Incheon, Republic of Korea
| |
Collapse
|
3
|
Deng L, Shuai P, Liu Y, Yong T, Liu Y, Li H, Zheng X. Diagnostic performance of radiomics for predicting osteoporosis in adults: a systematic review and meta-analysis. Osteoporos Int 2024:10.1007/s00198-024-07136-y. [PMID: 38802557 DOI: 10.1007/s00198-024-07136-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/07/2024] [Accepted: 05/16/2024] [Indexed: 05/29/2024]
Abstract
This study aimed to assess the diagnostic accuracy of radiomics for predicting osteoporosis and the quality of radiomic studies. The study protocol was prospectively registered on PROSPERO (CRD42023425058). We searched PubMed, EMBASE, Web of Science, and Cochrane Library databases from inception to June 1, 2023, for eligible articles that applied radiomic techniques to diagnosing osteoporosis or abnormal bone mass. Quality and risk of bias of the included studies were evaluated with radiomics quality score (RQS), METhodological RadiomICs Score (METRICS), and Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tools. The data analysis utilized the R program with mada, metafor, and meta packages. Ten retrospective studies with 5926 participants were included in the systematic review and meta-analysis. The overall risk of bias and applicability concerns for each domain of the studies were rated as low, except for one study which was considered to have a high risk of flow and time bias. The mean METRICS score was 70.1% (range 49.6-83.2%). There was moderate heterogeneity across studies and meta-regression identified sources of heterogeneity in the data, including imaging modality, feature selection method, and classifier. The pooled diagnostic odds ratio (DOR) under the bivariate random effects model across the studies was 57.22 (95% CI 27.62-118.52). The pooled sensitivity and specificity were 87% (95% CI 81-92%) and 87% (95% CI 77-93%), respectively. The area under the summary receiver operating characteristic curve (AUC) of the radiomic models was 0.94 (range 0.8 to 0.98). The results supported that the radiomic techniques had good accuracy in diagnosing osteoporosis or abnormal bone mass. The application of radiomics in osteoporosis diagnosis needs to be further confirmed by more prospective studies with rigorous adherence to existing guidelines and multicenter validation.
Collapse
Affiliation(s)
- Ling Deng
- Department of Health Management & Institute of Health Management, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Ping Shuai
- Department of Health Management & Institute of Health Management, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Youren Liu
- Department of Health Management & Institute of Health Management, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Tao Yong
- Department of Medical Information Center, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Yuping Liu
- Department of Health Management & Institute of Health Management, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Hang Li
- Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
| | - Xiaoxia Zheng
- Department of Health Management & Institute of Health Management, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
| |
Collapse
|
4
|
Küçükçiloğlu Y, Şekeroğlu B, Adalı T, Şentürk N. Prediction of osteoporosis using MRI and CT scans with unimodal and multimodal deep-learning models. Diagn Interv Radiol 2024; 30:9-20. [PMID: 37309886 PMCID: PMC10773174 DOI: 10.4274/dir.2023.232116] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 05/06/2023] [Indexed: 06/14/2023]
Abstract
PURPOSE Osteoporosis is the systematic degeneration of the human skeleton, with consequences ranging from a reduced quality of life to mortality. Therefore, the prediction of osteoporosis reduces risks and supports patients in taking precautions. Deep-learning and specific models achieve highly accurate results using different imaging modalities. The primary purpose of this research was to develop unimodal and multimodal deep-learning-based diagnostic models to predict bone mineral loss of the lumbar vertebrae using magnetic resonance (MR) and computed tomography (CT) imaging. METHODS Patients who received both lumbar dual-energy X-ray absorptiometry (DEXA) and MRI (n = 120) or CT (n = 100) examinations were included in this study. Unimodal and multimodal convolutional neural networks (CNNs) with dual blocks were proposed to predict osteoporosis using lumbar vertebrae MR and CT examinations in separate and combined datasets. Bone mineral density values obtained by DEXA were used as reference data. The proposed models were compared with a CNN model and six benchmark pre-trained deep-learning models. RESULTS The proposed unimodal model obtained 96.54%, 98.84%, and 96.76% balanced accuracy for MRI, CT, and combined datasets, respectively, while the multimodal model achieved 98.90% balanced accuracy in 5-fold cross-validation experiments. Furthermore, the models obtained 95.68%-97.91% accuracy with a hold-out validation dataset. In addition, comparative experiments demonstrated that the proposed models yielded superior results by providing more effective feature extraction in dual blocks to predict osteoporosis. CONCLUSION This study demonstrated that osteoporosis was accurately predicted by the proposed models using both MR and CT images, and a multimodal approach improved the prediction of osteoporosis. With further research involving prospective studies with a larger number of patients, there may be an opportunity to implement these technologies into clinical practice.
Collapse
Affiliation(s)
- Yasemin Küçükçiloğlu
- Near East University Faculty of Medicine, Department of Radiology, Nicosia, Cyprus
- Near East University, Center of Excellence, Tissue Engineering and Biomaterials Research Center, Nicosia, Cyprus
| | - Boran Şekeroğlu
- Near East University, Applied Artificial Intelligence Research Center, Nicosia, Cyprus
| | - Terin Adalı
- Near East University, Center of Excellence, Tissue Engineering and Biomaterials Research Center, Nicosia, Cyprus
- Near East University Faculty of Engineering, Department of Biomedical Engineering, Nicosia, Cyprus
- Sabancı University, Nanotechnology Research and Application Center, İstanbul, Turkey
| | - Niyazi Şentürk
- Near East University, Center of Excellence, Tissue Engineering and Biomaterials Research Center, Nicosia, Cyprus
- Near East University Faculty of Engineering, Department of Biomedical Engineering, Nicosia, Cyprus
| |
Collapse
|
5
|
Wu Y, Chao J, Bao M, Zhang N. Predictive value of machine learning on fracture risk in osteoporosis: a systematic review and meta-analysis. BMJ Open 2023; 13:e071430. [PMID: 38070927 PMCID: PMC10728980 DOI: 10.1136/bmjopen-2022-071430] [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: 12/27/2022] [Accepted: 11/06/2023] [Indexed: 12/18/2023] Open
Abstract
OBJECTIVES Early identification of fracture risk in patients with osteoporosis is essential. Machine learning (ML) has emerged as a promising technique to predict the risk, whereas its predictive performance remains controversial. Therefore, we conducted this systematic review and meta-analysis to explore the predictive efficiency of ML for the risk of fracture in patients with osteoporosis. METHODS Relevant studies were retrieved from four databases (PubMed, Embase, Cochrane Library and Web of Science) until 31 May 2023. A meta-analysis of the C-index was performed using a random-effects model, while a bivariate mixed-effects model was used for the meta-analysis of sensitivity and specificity. In addition, subgroup analysis was performed according to the types of ML models and fracture sites. RESULTS Fifty-three studies were included in our meta-analysis, involving 15 209 268 patients, 86 prediction models specifically developed for the osteoporosis population and 41 validation sets. The most commonly used predictors in these models encompassed age, BMI, past fracture history, bone mineral density T-score, history of falls, BMD, radiomics data, weight, height, gender and other chronic diseases. Overall, the pooled C-index of ML was 0.75 (95% CI: 0.72, 0.78) and 0.75 (95% CI: 0.71, 0.78) in the training set and validation set, respectively; the pooled sensitivity was 0.79 (95% CI: 0.72, 0.84) and 0.76 (95% CI: 0.80, 0.81) in the training set and validation set, respectively; and the pooled specificity was 0.81 (95% CI: 0.75, 0.86) and 0.83 (95% CI: 0.72, 0.90) in the training set and validation set, respectively. CONCLUSIONS ML has a favourable predictive performance for fracture risk in patients with osteoporosis. However, most current studies lack external validation. Thus, external validation is required to verify the reliability of ML models. PROSPERO REGISTRATION NUMBER CRD42022346896.
Collapse
Affiliation(s)
- Yanqian Wu
- Key Laboratory of Environmental Medicine Engineering of Ministry of Education/Health Management Research Center, School of Public Health, Southeast University, Nanjing, Jiangsu, China
| | - Jianqian Chao
- Key Laboratory of Environmental Medicine Engineering of Ministry of Education/Health Management Research Center, School of Public Health, Southeast University, Nanjing, Jiangsu, China
| | - Min Bao
- Key Laboratory of Environmental Medicine Engineering of Ministry of Education/Health Management Research Center, School of Public Health, Southeast University, Nanjing, Jiangsu, China
| | - Na Zhang
- Key Laboratory of Environmental Medicine Engineering of Ministry of Education/Health Management Research Center, School of Public Health, Southeast University, Nanjing, Jiangsu, China
| |
Collapse
|
6
|
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.
Collapse
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
| |
Collapse
|
7
|
Martel D, Monga A, Chang G. Radiomic analysis of the proximal femur in osteoporosis women using 3T MRI. FRONTIERS IN RADIOLOGY 2023; 3:1293865. [PMID: 38077634 PMCID: PMC10702560 DOI: 10.3389/fradi.2023.1293865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 11/06/2023] [Indexed: 02/12/2024]
Abstract
Introduction Osteoporosis (OP) results in weak bone and can ultimately lead to fracture. MRI assessment of bone structure and microarchitecture has been proposed as method to assess bone quality and fracture risk in vivo. Radiomics provides a framework to analyze the textural information of MR images. The purpose of this study was to analyze the radiomic features and its abilityto differentiate between subjects with and without prior fragility fracture. Methods MRI acquisition was performed on n = 45 female OP subjects: 15 with fracture history (Fx) and 30 without fracture history (nFx) using a high-resolution 3D Fast Low Angle Shot (FLASH) sequence at 3T. Second and first order radiomic features were calculated in the trabecular region of the proximal femur on T1-weighted MRI signal of a matched dataset. Significance of the feature's predictive ability was measured using Wilcoxon test and Area Under the ROC (AUROC) curve analysis. The features were correlated DXA and FRAX score. Result A set of three independent radiomic features (Dependence Non-Uniformity (DNU), Low Gray Level Emphasis (LGLE) and Kurtosis) showed significant ability to predict fragility fracture (AUROC DNU = 0.751, p < 0.05; AUROC LGLE = 0.729, p < 0.05; AUROC Kurtosis = 0.718, p < 0.05) with low to moderate correlation with FRAX and DXA. Conclusion Radiomic features can measure bone health in MRI of proximal femur and has the potential to predict fracture.
Collapse
Affiliation(s)
- Dimitri Martel
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States
| | | | | |
Collapse
|
8
|
Tzou SJ, Peng CH, Huang LY, Chen FY, Kuo CH, Wu CZ, Chu TW. Comparison between linear regression and four different machine learning methods in selecting risk factors for osteoporosis in a Chinese female aged cohort. J Chin Med Assoc 2023; 86:1028-1036. [PMID: 37729604 DOI: 10.1097/jcma.0000000000000999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/22/2023] Open
Abstract
BACKGROUND Population aging is emerging as an increasingly acute challenge for countries around the world. One particular manifestation of this phenomenon is the impact of osteoporosis on individuals and national health systems. Previous studies of risk factors for osteoporosis were conducted using traditional statistical methods, but more recent efforts have turned to machine learning approaches. Most such efforts, however, treat the target variable (bone mineral density [BMD] or fracture rate) as a categorical one, which provides no quantitative information. The present study uses five different machine learning methods to analyze the risk factors for T-score of BMD, seeking to (1) compare the prediction accuracy between different machine learning methods and traditional multiple linear regression (MLR) and (2) rank the importance of 25 different risk factors. METHODS The study sample includes 24 412 women older than 55 years with 25 related variables, applying traditional MLR and five different machine learning methods: classification and regression tree, Naïve Bayes, random forest, stochastic gradient boosting, and eXtreme gradient boosting. The metrics used for model performance comparisons are the symmetric mean absolute percentage error, relative absolute error, root relative squared error, and root mean squared error. RESULTS Machine learning approaches outperformed MLR for all four prediction errors. The average importance ranking of each factor generated by the machine learning methods indicates that age is the most important factor determining T-score, followed by estimated glomerular filtration rate (eGFR), body mass index (BMI), uric acid (UA), and education level. CONCLUSION In a group of women older than 55 years, we demonstrated that machine learning methods provide superior performance in estimating T-Score, with age being the most important impact factor, followed by eGFR, BMI, UA, and education level.
Collapse
Affiliation(s)
- Shiow-Jyu Tzou
- Teaching and Researching Center, Kaohsiung Armed Forces General Hospital, Kaohsiung, Taiwan, ROC
- Institute of Medical Science and Technology, National Sun Yat-sen University, Kaohsiung, Taiwan, ROC
| | - Chung-Hsin Peng
- Department of Urology, Cardinal Tien Hospital, School of Medicine, Fu Jen Catholic University, New Taipei City, Taiwan, ROC
| | - Li-Ying Huang
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Fu Jen Catholic University Hospital, New Taipei, Taiwan
| | - Fang-Yu Chen
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Fu Jen Catholic University Hospital, New Taipei, Taiwan
| | - Chun-Heng Kuo
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Fu Jen Catholic University Hospital, New Taipei, Taiwan
| | - Chung-Ze Wu
- Department of Internal Medicine, Shuang Ho Hospital, New Taipei City, Division of Endocrinology and Metabolism, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan, ROC
| | - Ta-Wei Chu
- Department of Obstetrics and Gynecology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, ROC
- MJ Health Research Foundation, Taipei, Taiwan, ROC
| |
Collapse
|
9
|
Zhao J, Liu Z, Ren Q, Nie G, Zhao D. Measurement of Hounsfield units on proximal femur computed tomography for predicting regional osteoporosis. RADIOLOGIE (HEIDELBERG, GERMANY) 2023; 63:90-97. [PMID: 37603067 DOI: 10.1007/s00117-023-01190-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 07/15/2023] [Indexed: 08/22/2023]
Abstract
OBJECTIVE This study was designed to investigate the use of proximal femoral Hounsfield units (HU) in conventional abdominal and pelvic computed tomography (CT) to predict hip osteoporosis by coupling with data from quantitative CT (QCT). METHODS In this study, 315 patients who underwent routine abdominal and pelvic CT with the proximal femur included in the scanning range were also subjected to QCT of the proximal femur. Pearson correlation test was performed to analyze the correlations of the femoral head, femoral neck, proximal femur, and femoral trochanter CT HU with the femoral neck, femoral trochanter, and intertrochanteric femur bone mineral density (BMD) values from QCT. The diagnostic performance of CT HU measurement of the proximal femur for osteoporosis was analyzed using receiver operating characteristic (ROC) curves. RESULTS The CT HU of the proximal femur showed the highest correlation with the BMD value of the hip (r = 0.826; p < 0.01). The mean CT HU of the proximal femur differed significantly (all p < 0.01) for the three QCT-defined BMD categories of osteoporosis (192.23 HU vs. 188.71), of osteopenia (247.86 HU vs. 248.36 HU), and of normal individuals (308.13 HU vs. 310.41 HU) in left and right sides, respectively. In the ROC curve analysis, the area under the ROC curve values to predict osteoporosis in the left and right proximal femurs were 0.942 and 0.941, respectively. CONCLUSION The CT HU of the proximal femur was significantly associated with the BMD value of the hip measured by QCT. The CT HU of the proximal femur is highly effective in diagnosing osteoporosis and could be used for hip osteoporosis screening.
Collapse
Affiliation(s)
- Junlu Zhao
- Department of Radiology, The First Hospital of Hebei Medical University, 050031, Shijiazhuang, Hebei Province, China
| | - Zhai Liu
- Department of Radiology, The First Hospital of Hebei Medical University, 050031, Shijiazhuang, Hebei Province, China
| | - Qingyun Ren
- Department of Radiology, The First Hospital of Hebei Medical University, 050031, Shijiazhuang, Hebei Province, China.
| | - Guanwei Nie
- Operating room, The First Hospital Hebei Medical University, Shijiazhuang, Hebei Province, China
| | - Deyuan Zhao
- Department of Radiology, The First Hospital of Hebei Medical University, 050031, Shijiazhuang, Hebei Province, China
| |
Collapse
|
10
|
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.
Collapse
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.
| |
Collapse
|
11
|
Radiomics and Deep Learning for Disease Detection in Musculoskeletal Radiology: An Overview of Novel MRI- and CT-Based Approaches. Invest Radiol 2023; 58:3-13. [PMID: 36070548 DOI: 10.1097/rli.0000000000000907] [Citation(s) in RCA: 26] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
ABSTRACT Radiomics and machine learning-based methods offer exciting opportunities for improving diagnostic performance and efficiency in musculoskeletal radiology for various tasks, including acute injuries, chronic conditions, spinal abnormalities, and neoplasms. While early radiomics-based methods were often limited to a smaller number of higher-order image feature extractions, applying machine learning-based analytic models, multifactorial correlations, and classifiers now permits big data processing and testing thousands of features to identify relevant markers. A growing number of novel deep learning-based methods describe magnetic resonance imaging- and computed tomography-based algorithms for diagnosing anterior cruciate ligament tears, meniscus tears, articular cartilage defects, rotator cuff tears, fractures, metastatic skeletal disease, and soft tissue tumors. Initial radiomics and deep learning techniques have focused on binary detection tasks, such as determining the presence or absence of a single abnormality and differentiation of benign versus malignant. Newer-generation algorithms aim to include practically relevant multiclass characterization of detected abnormalities, such as typing and malignancy grading of neoplasms. So-called delta-radiomics assess tumor features before and after treatment, with temporal changes of radiomics features serving as surrogate markers for tumor responses to treatment. New approaches also predict treatment success rates, surgical resection completeness, and recurrence risk. Practice-relevant goals for the next generation of algorithms include diagnostic whole-organ and advanced classification capabilities. Important research objectives to fill current knowledge gaps include well-designed research studies to understand how diagnostic performances and suggested efficiency gains of isolated research settings translate into routine daily clinical practice. This article summarizes current radiomics- and machine learning-based magnetic resonance imaging and computed tomography approaches for musculoskeletal disease detection and offers a perspective on future goals and objectives.
Collapse
|
12
|
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.
Collapse
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
| |
Collapse
|
13
|
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.
Collapse
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
| |
Collapse
|
14
|
Klontzas ME, Karantanas AH. Research in Musculoskeletal Radiology: Setting Goals and Strategic Directions. Semin Musculoskelet Radiol 2022; 26:354-358. [PMID: 35654100 DOI: 10.1055/s-0042-1748319] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
The future of musculoskeletal (MSK) radiology is being built on research developments in the field. Over the past decade, MSK imaging research has been dominated by advancements in molecular imaging biomarkers, artificial intelligence, radiomics, and novel high-resolution equipment. Adequate preparation of trainees and specialists will ensure that current and future leaders will be prepared to embrace and critically appraise technological developments, will be up to date on clinical developments, such as the use of artificial tissues, will define research directions, and will actively participate and lead multidisciplinary research. This review presents an overview of the current MSK research landscape and proposes tangible future goals and strategic directions that will fortify the future of MSK radiology.
Collapse
Affiliation(s)
- Michail E Klontzas
- Department of Medical Imaging, University Hospital of Heraklion, Crete, Greece.,Advanced Hybrid Imaging Systems, Institute of Computer Science, Foundation for Research and Technology (FORTH), Heraklion, Crete, Greece.,Department of Radiology, School of Medicine, University of Crete, Heraklion, Greece
| | - Apostolos H Karantanas
- Department of Medical Imaging, University Hospital of Heraklion, Crete, Greece.,Advanced Hybrid Imaging Systems, Institute of Computer Science, Foundation for Research and Technology (FORTH), Heraklion, Crete, Greece.,Department of Radiology, School of Medicine, University of Crete, Heraklion, Greece
| |
Collapse
|
15
|
Lan YS, Feng YJ. The volume of brisk walking is the key determinant of BMD improvement in premenopausal women. PLoS One 2022; 17:e0265250. [PMID: 35294490 PMCID: PMC8926180 DOI: 10.1371/journal.pone.0265250] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Accepted: 02/25/2022] [Indexed: 11/18/2022] Open
Abstract
SUMMARY Osteoporosis is an increasing health problem in postmenopausal women. Our findings indicated that long-term brisk walking with a volume greater than 16 per week is effective for improving BMD in premenopausal women. PURPOSE To examine the effects of brisk walking on bone mineral density (BMD) in premenopausal women, and further determine the effective frequency, intensity, time and volume (frequency x duration) of brisk walking for training strategy prescription. METHODS 222 healthy premenopausal women were recruited for BMD measurement. According to the survey of their physical activity level, 84 subjects (age: 46±1.8) whose physical activity index ≥40 were categorized into the brisk walking group, and 138 subjects (age: 47±2.2) whose physical activity index <40 were assigned to the sedentary group. The BMD of these two groups were statistically compared with an independent t test. Next, 35 subjects from the original sedentary group were recruited for BMD measurement after 2-year moderate brisk walking. According to the volume of physical activity per week, they were divided into the control group (n = 10, aged 49±0.9), volume 8 group (n = 4, aged 48±1.2), volume 12 group (n = 7, aged 49±1.4), volume 16 group (n = 8, aged 49±1.3), and volume 20 group (n = 6, aged 49±1.5). ANOVA was used to analyze BMD before and after brisk walking among the five groups. RESULTS The BMD in the brisk walking group (1.00±0.008 g/cm2) was significantly higher than that in the sedentary group (0.89±0.008 g/cm2) (P<0.001). Stepwise regression analysis revealed that the volume of brisk walking was significantly correlated with BMD (P<0.001). In particular, brisk walking with a volume greater than 16 (a score of duration up to 4 and a score of frequency up to 4 or 5) per week is effective for improving BMD in premenopausal women (P = 0.03, P = 0.002, respectively). CONCLUSIONS Long-term brisk walking is an efficient way to improve BMD. Taking brisk walks for 30 minutes per day 3 or more times per week (volume>16) is recommended to prevent bone loss in premenopausal women.
Collapse
Affiliation(s)
- Yong-Sheng Lan
- School of Physical Education, Changchun Normal University, Changchun, Jilin, China
- * E-mail:
| | - Yu-Juan Feng
- Shandong University of Art and Design, Jinan, Shandong, China
| |
Collapse
|
16
|
Qiu H, Yang H, Yang Z, Yao Q, Duan S, Qin J, Zhu J. The value of radiomics to predict abnormal bone mass in type 2 diabetes mellitus patients based on CT imaging for paravertebral muscles. Front Endocrinol (Lausanne) 2022; 13:963246. [PMID: 36313781 PMCID: PMC9606777 DOI: 10.3389/fendo.2022.963246] [Citation(s) in RCA: 4] [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: 06/07/2022] [Accepted: 09/27/2022] [Indexed: 11/17/2022] Open
Abstract
OBJECTIVE To investigate the value of CT imaging features of paravertebral muscles in predicting abnormal bone mass in patients with type 2 diabetes mellitus. METHODS The clinical and QCT data of 149 patients with type 2 diabetes mellitus were collected retrospectively. Patients were randomly divided into the training group (n = 90) and the validation group (n = 49). The radiologic model and Nomogram model were established by multivariate Logistic regression analysis. Predictive performance was evaluated using receiver operating characteristic (ROC) curves. RESULTS A total of 829 features were extracted from CT images of paravertebral muscles, and 12 optimal predictive features were obtained by the mRMR and Lasso feature selection methods. The radiomics model can better predict bone abnormality in type 2 diabetes mellitus, and the (Area Under Curve) AUC values of the training group and the validation group were 0.94(95% CI, 0.90-0.99) and 0.90(95% CI, 0.82-0.98). The combined Nomogram model, based on radiomics and clinical characteristics (vertebral CT values), showed better predictive efficacy with an AUC values of 0.97(95% CI, 0.94-1.00) in the training group and 0.95(95% CI, 0.90-1.00) in the validation group, compared with the clinical model. CONCLUSION The combination of Nomogram model and radiomics-clinical features of paravertebral muscles has a good predictive value for abnormal bone mass in patients with type 2 diabetes mellitus.
Collapse
Affiliation(s)
- Hui Qiu
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai’an, China
| | - Hui Yang
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai’an, China
| | - Zhe Yang
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai’an, China
| | - Qianqian Yao
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai’an, China
| | - Shaofeng Duan
- GE Healthcare, Precision Health Institution, Shanghai, China
| | - Jian Qin
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai’an, China
- *Correspondence: Jian Qin, ; Jianzhong Zhu,
| | - Jianzhong Zhu
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai’an, China
- *Correspondence: Jian Qin, ; Jianzhong Zhu,
| |
Collapse
|
17
|
Comparison of HU histogram analysis and BMD for proximal femoral fragility fracture assessment: a retrospective single-center case-control study. Eur Radiol 2021; 32:1448-1455. [PMID: 34647175 DOI: 10.1007/s00330-021-08281-2] [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: 06/10/2021] [Revised: 08/03/2021] [Accepted: 08/17/2021] [Indexed: 10/20/2022]
Abstract
OBJECTIVES To evaluate the feasibility of HU histogram analysis (HUHA) to assess proximal femoral fragility fractures with respect to BMD. METHODS This retrospective study included 137 patients with femoral fragility fractures who underwent hip CT and 137 control patients without fractures who underwent abdominal CT between January 2018 and February 2019. HUHA was calculated with the 3D volume of interest from the femoral head to the lesser trochanter. HUHAfat (percentage of negative HU values) and HUHAbone (percentage of HU values ≥ 125 HU) were assumed to be fat and bone components, respectively. Statistical significance was assessed using the area under the receiver operating characteristic curve (AUC), Spearman correlation (ρ), and odds ratio. RESULTS HUHAfat was strongly positively correlated (ρ = 0.56) and BMD was moderately negatively correlated with fragility fractures (ρ = - 0.37). AUC of HUHAfat (0.82, 95% CI [0.77, 0.87]) significantly differed from that of BMD (0.69, 95% CI [0.63, 0.75]) (p < .001). The cutoff value was 15.8% for HUHAfat (sensitivity: 90.4%; specificity: 67.7%) and 0.709 g/cm2 for BMD (sensitivity: 87.5%; specificity: 51.5%), with higher HUHAfat and lower BMD values indicating fragility fractures. The odds ratio of HUHAfat was 19.5 (95% CI [9.9, 38.2], p < .001), which was higher than that of BMD, 7.4 (95% CI [4.0, 13.6], p < .001). CONCLUSION HUHAfat revealed better performance than BMD and demonstrated feasibility in assessing proximal femoral fragility fractures. KEY POINTS • HUHAfat showed a strong positive correlation (Spearman ρ = 0.56, p < .001), and BMD showed a moderate negative correlation (Spearman ρ = - 0.37, p < .001) with proximal femoral fragility fractures. • HUHAfat (AUC = 0.82) performed significantly better than BMD in assessing proximal femoral fragility fractures (AUC = 0.69) (p < .001). • The odds ratio of HUHAfat for proximal femoral fragility fractures was higher than that of BMD (19.5 and 7.4, respectively; p < .001).
Collapse
|
18
|
Radiomics and Machine Learning Can Differentiate Transient Osteoporosis from Avascular Necrosis of the Hip. Diagnostics (Basel) 2021; 11:diagnostics11091686. [PMID: 34574027 PMCID: PMC8468167 DOI: 10.3390/diagnostics11091686] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Revised: 09/12/2021] [Accepted: 09/14/2021] [Indexed: 02/07/2023] Open
Abstract
Differentiation between transient osteoporosis (TOH) and avascular necrosis (AVN) of the hip is a longstanding challenge in musculoskeletal radiology. The purpose of this study was to utilize MRI-based radiomics and machine learning (ML) for accurate differentiation between the two entities. A total of 109 hips with TOH and 104 hips with AVN were retrospectively included. Femoral heads and necks with segmented radiomics features were extracted. Three ML classifiers (XGboost, CatBoost and SVM) using 38 relevant radiomics features were trained on 70% and validated on 30% of the dataset. ML performance was compared to two musculoskeletal radiologists, a general radiologist and two radiology residents. XGboost achieved the best performance with an area under the curve (AUC) of 93.7% (95% CI from 87.7 to 99.8%) among ML models. MSK radiologists achieved an AUC of 90.6% (95% CI from 86.7% to 94.5%) and 88.3% (95% CI from 84% to 92.7%), respectively, similar to residents. The general radiologist achieved an AUC of 84.5% (95% CI from 80% to 89%), significantly lower than of XGboost (p = 0.017). In conclusion, radiomics-based ML achieved a performance similar to MSK radiologists and significantly higher compared to general radiologists in differentiating between TOH and AVN.
Collapse
|
19
|
Salamanna F, Maglio M, Borsari V, Landini MP, Fini M. Blood factors as biomarkers in osteoporosis: points from the COVID-19 era. Trends Endocrinol Metab 2021; 32:672-679. [PMID: 34246532 PMCID: PMC8261630 DOI: 10.1016/j.tem.2021.05.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Revised: 05/18/2021] [Accepted: 05/24/2021] [Indexed: 11/20/2022]
Abstract
The restrictions adopted during the coronavirus disease 2019 (COVID-19) pandemic limiting direct medical consultations and access to healthcare centers reduced the participation of patients with chronic diseases, such as osteoporosis (OP), in screening and monitoring programs. This highlighted the need for new screening diagnostic tools that are clinically effective, but require minimal technical and time commitments, to stratify populations and identify who is more at risk for OP and related complications. This paper provides an overview of the potential use of blood-related factors, such as platelet (PLT)- and monocyte-related factors, as biomarkers able to quickly screen, detect, and monitor OP in both sexes. Such biomarkers might be of key importance not only during the COVID-19 pandemic but also, even more importantly, during periods of better global health stability.
Collapse
Affiliation(s)
- Francesca Salamanna
- IRCCS Istituto Ortopedico Rizzoli, Complex Structure of Surgical Sciences and Technologies, via di Barbiano 1/10, Bologna 40136, Italy
| | - Melania Maglio
- IRCCS Istituto Ortopedico Rizzoli, Complex Structure of Surgical Sciences and Technologies, via di Barbiano 1/10, Bologna 40136, Italy.
| | - Veronica Borsari
- IRCCS Istituto Ortopedico Rizzoli, Complex Structure of Surgical Sciences and Technologies, via di Barbiano 1/10, Bologna 40136, Italy
| | - Maria Paola Landini
- IRCCS Istituto Ortopedico Rizzoli, Scientific Direction, via di Barbiano 1/10, Bologna 40136, Italy
| | - Milena Fini
- IRCCS Istituto Ortopedico Rizzoli, Complex Structure of Surgical Sciences and Technologies, via di Barbiano 1/10, Bologna 40136, Italy
| |
Collapse
|