Radiomics Based on Lumbar Spine Magnetic Resonance Imaging to Detect Osteoporosis

He, Li; Liu, Zhai; Liu, Chunying; Gao, Zhimei; Ren, Qiushi; Lei, Licun; Ren, Jialiang

doi:10.1016/j.acra.2020.03.046

Cited by 45 publications

(27 citation statements)

References 22 publications

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“…41,42 Therefore, we assessed bone microarchitecture in complement to BMD using radiomics-based feature extraction after collecting baseline CT images and follow-up CT images at 6 months and 12 months. 41,43,44 Repeated measures ANOVA showed significant differences in 10th percentile, mean, and energy. Here, the 10th percentile is the tenth lowest intensity value in the ROI of the axial plane at the index level of the OVCF.…”

Section: Discussionmentioning

confidence: 95%

Section: Discussionmentioning

confidence: 99%

“…41,42 Therefore, we assessed bone microarchitecture in complement to BMD using radiomics-based feature extraction after collecting baseline CT images and follow-up CT images at 6 months and 12 months. 41,43,44 MSCs have been reported to be a new therapeutic strategy to treat osteoporosis and OVCF, mainly because of their ability to secrete factors that are directly or indirectly involved in bone repair, as well as their ability to graft into tissues and differentiate into functional osteoblasts. Emerging evidence suggests that inflammation exerts a significant influence on bone turnover, thereby in osteoporosis.…”

Section: Discussionmentioning

confidence: 99%

“…Texture analysis has been reported to be independent and complementary with BMD for determining the odds ratio of fractures 41,42 . Therefore, we assessed bone microarchitecture in complement to BMD using radiomics‐based feature extraction after collecting baseline CT images and follow‐up CT images at 6 months and 12 months 41,43,44 . Repeated measures ANOVA showed significant differences in 10th percentile, mean, and energy.…”

Section: Discussionmentioning

confidence: 99%

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Safety and efficacy of Wharton's jelly-derived mesenchymal stem cells with teriparatide for osteoporotic vertebral fractures: A phase I/IIa study

Shim¹,

Kim

et al. 2020

Stem Cells Translational Medicine

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Osteoporotic vertebral compression fractures (OVCFs) are serious health problems. We conducted a randomized, open-label, phase I/IIa study to determine the feasibility, safety, and effectiveness of Wharton's jelly-derived mesenchymal stem cells (WJ-MSCs) and teriparatide (parathyroid hormone 1-34) in OVCFs. Twenty subjects with recent OVCFs were randomized to teriparatide (20 μg/day, daily subcutaneous injection for 6 months) treatment alone or combined treatment of WJ-MSCs (intramedullary [4 × 10 7 cells] injection and intravenous [2 × 10 8 cells] injection after 1 week) and teriparatide (20 μg/day, daily subcutaneous injection for 6 months). Fourteen subjects (teriparatide alone, n = 7; combined treatment, n = 7) completed follow-up assessment (visual analog scale [VAS], Oswestry Disability Index [ODI], Short Form-36 [SF-36], bone mineral density [BMD], bone turnover measured by osteocalcin and C-terminal telopeptide of type 1 collagen, dual-energy x-ray absorptiometry [DXA], computed tomography [CT]). Our results show that (a) combined treatment with WJ-MSCs and teriparatide is feasible and tolerable for the patients with OVCFs; (b) the mean VAS, ODI, and SF-36 scores significantly improved in the combined treatment group; (c) the level of bone turnover markers were not significantly different between the two groups; (d) BMD T-scores of spine and hip by DXA increased in both control and experimental groups without a statistical difference; and (e) baseline spine CT images and follow-up CT images at 6 and 12 months showed better microarchitecture in the combined treatment group. Our results indicate that combined treatment of WJ-MSCs and teriparatide is feasible and tolerable and has a clinical benefit for fracture healing by promoting bone architecture. Clinical trial registration: https://nedrug.mfds.go.kr/, MFDS: 201600282-30937.

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“…41,42 Therefore, we assessed bone microarchitecture in complement to BMD using radiomics-based feature extraction after collecting baseline CT images and follow-up CT images at 6 months and 12 months. 41,43,44 Repeated measures ANOVA showed significant differences in 10th percentile, mean, and energy. Here, the 10th percentile is the tenth lowest intensity value in the ROI of the axial plane at the index level of the OVCF.…”

Section: Discussionmentioning

confidence: 95%

Section: Discussionmentioning

confidence: 99%

“…41,42 Therefore, we assessed bone microarchitecture in complement to BMD using radiomics-based feature extraction after collecting baseline CT images and follow-up CT images at 6 months and 12 months. 41,43,44 MSCs have been reported to be a new therapeutic strategy to treat osteoporosis and OVCF, mainly because of their ability to secrete factors that are directly or indirectly involved in bone repair, as well as their ability to graft into tissues and differentiate into functional osteoblasts. Emerging evidence suggests that inflammation exerts a significant influence on bone turnover, thereby in osteoporosis.…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Safety and efficacy of Wharton's jelly-derived mesenchymal stem cells with teriparatide for osteoporotic vertebral fractures: A phase I/IIa study

Shim¹,

Kim

et al. 2020

Stem Cells Translational Medicine

View full text Add to dashboard Cite

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“…Previously, 18 F-FDG PET/CT radiomics from bone marrows were used to differentiate various types of diseases [5][6][7], and radiomics from T1 and T2-weighted MRI were used to discriminate bone chondrosarcoma [8], metastatic diseases [9], and osteoporosis [10]. However, the MRI-based radiomics studies often discriminated diseases with low predictive performance [10] or with performance only comparable with experienced radiologists [8]. We hypothesized that the welltrained model based on bone marrow radiomics would advance clinical decision of bone marrow diseases than conventional methods of radiologists' readings and quantitative measurements.…”

Section: Introductionmentioning

confidence: 99%

Bone Marrow Radiomics of T1-Weighted Lumber Spinal MRI to Identify Diffuse Hematologic Marrow Diseases: Comparison With Human Readings

2020

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We developed a radiomics model to differentiate hematologic marrow diseases and compared the performance with radiologists' readings and a quantitative measurement. Patients were retrospectively analyzed from the diseased (n = 254) and control groups (n = 230). A sagittal T1-weighted lumbar spinal MR image was normalized by an intervertebral disk, and bone marrow was segmented. A hundred features were extracted, and final features were selected using Principle Component Analysis (PCA) and least absolute shrinkage and selection operator (LASSO). Finally, Random forest (RF) and logistic regression (LR) models were trained. Two radiologists with different levels of experience analyzed the images for the presence of bone marrow diseases, independently. The area under the receiver operating characteristic curves (AUC) and decision curve analysis (DCA) was evaluated. Among the subjects, 363 cases were assigned as a training set and 121 as a validation set. The combination of LASSO and RF produced the best results. With the validation set, the sensitivity (SE) was 87.3%, specificity (SP) was 86.2% and AUC was 0.928 (p < 0.05). We selected Firstorder-Maximum as the best feature to identify diseased marrows, which achieved SE of 75.0% and AUC of 0.787 (p < 0.05). The reader with 11 years of experience yielded SE of 86.5% and AUC of 0.861 (p < 0.05). The second reader with 1 year of experience yielded SE of 75.0% and AUC of 0.767 (p < 0.05). We demonstrated the advantage of bone marrow radiomics over conventional methods of diagnosing with radiologists' readings and quantitative measurements.

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Machine learning‐based prediction of osteoporosis in postmenopausal women with clinical examined features: A quantitative clinical study

Ullah,

Rehman,

Anwar

et al. 2023

Health Science Reports

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Osteoporosis is a skeletal disease that is commonly seen in older people but often neglected due to its silent nature. To overcome the issue of osteoporosis in men and women, we proposed an advanced prediction model with the help of machine learning techniques which can help to identify the potential occurrence of this bone disease by its advanced screening tools. To achieve more reliable and accurate results, various machine‐learning techniques were applied to the presented data sets. Moreover, we also compared the performance of our results with other existing algorithms to solely focus on the advanced features of the proposed methodology. The two data sets, the clinical tests of patients in Taiwan and medical reports of postmenopausal women in Korea through Korean Health and Nutrition Examination Surveys (2010–2011) were considered in this study. To predict bone disorders, we utilized the data about females and developed a system using artificial neural networks, support vector machines, and K‐nearest neighbor. To compare the performance of the model Area under the Receiver Operating Characteristic Curve and other evaluation metrics were compared. The achieved results from all the algorithms and compared them with Osteoporosis Self‐Assessment Tool for Asians and the results were noticeably better and more reliable than existing systems due to the involvement of ML. Using machine learning techniques to predict these types of diseases is better because physicians and patients can take early action to prevent the consequences in advance.

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Radiomics Based on Lumbar Spine Magnetic Resonance Imaging to Detect Osteoporosis

Cited by 45 publications

References 22 publications

Safety and efficacy of Wharton's jelly-derived mesenchymal stem cells with teriparatide for osteoporotic vertebral fractures: A phase I/IIa study

Safety and efficacy of Wharton's jelly-derived mesenchymal stem cells with teriparatide for osteoporotic vertebral fractures: A phase I/IIa study

Bone Marrow Radiomics of T1-Weighted Lumber Spinal MRI to Identify Diffuse Hematologic Marrow Diseases: Comparison With Human Readings

Machine learning‐based prediction of osteoporosis in postmenopausal women with clinical examined features: A quantitative clinical study

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