Comprehensive assessment of osteoporosis in lumbar spine using compositional MR imaging of trabecular bone.

OBJECTIVES: To comprehensively assess osteoporosis in the lumbar spine, a compositional MR imaging technique is proposed to quantify proton fractions for all the water components as well as fat in lumbar vertebrae measured by a combination of a 3D short repetition time adiabatic inversion recovery prepared ultrashort echo time (STAIR-UTE) MRI and IDEAL-IQ. METHODS: A total of 182 participants underwent MRI, quantitative CT, and DXA. Lumbar collagen-bound water proton fraction (CBWPF), free water proton fraction (FWPF), total water proton fraction (TWPF), bone mineral density (BMD), and T-score were calculated in three vertebrae (L2-L4) for each subject. The correlations of the CBWPF, FWPF, and TWPF with BMD and T-score were investigated respectively. A comprehensive diagnostic model combining all the water components and clinical characteristics was established. The performances of all the water components and the comprehensive diagnostic model to discriminate between normal, osteopenia, and osteoporosis cohorts were also evaluated using receiver operator characteristic (ROC). RESULTS: The CBWPF showed strong correlations with BMD (r = 0.85, p < 0.001) and T-score (r = 0.72, p < 0.001), while the FWPF and TWPF showed moderate correlations with BMD (r = 0.65 and 0.68, p < 0.001) and T-score (r = 0.47 and 0.49, p < 0.001). The high area under the curve values obtained from ROC analysis demonstrated that CBWPF, FWPF, and TWPF have the potential to differentiate the normal, osteopenia, and osteoporosis cohorts. At the same time, the comprehensive diagnostic model shows the best performance. CONCLUSIONS: The compositional MRI technique, which quantifies CBWPF, FWPF, and TWPF in trabecular bone, is promising in the assessment of bone quality. KEY POINTS: • Compositional MR imaging technique is able to quantify proton fractions for all the water components (i.e., collagen-bound water proton fraction (CBWPF), free water proton fraction (FWPF), and total water proton fraction (TWPF)) in the human lumbar spine. • The biomarkers derived from the compositional MR imaging technique showed moderate to high correlations with bone mineral density (BMD) and T-score and showed good performance in distinguishing people with different bone mass. • The comprehensive diagnostic model incorporating CBWPF, FWPF, TWPF, and clinical characteristics showed the highest clinical diagnostic capability for the assessment of osteoporosis.

Assessment of Osteoporosis in Lumbar Spine: In Vivo Quantitative MR Imaging of Collagen Bound Water in Trabecular Bone.

AIM: Bone collagen matrix makes a crucial contribution to the mechanical properties of bone by imparting tensile strength and elasticity. The collagen content of bone is accessible via quantification of collagen bound water (CBW) indirectly. We prospectively study the performance of the CBW proton density (CBWPD) measured by a 3D short repetition time adiabatic inversion recovery prepared ultrashort echo time (STAIR-UTE) magnetic resonance imaging (MRI) sequence in the diagnosis of osteoporosis in human lumbar spine. METHODS: A total of 189 participants with a mean age of 56 (ranged from 50 to 86) years old were underwent MRI, quantitative computed tomography (QCT), and dual-energy X-ray absorptiometry (DXA) in lumbar spine. Major fracture risk was also evaluated for all participants using Fracture Risk Assessment Tool (FRAX). Lumbar CBWPD, bone marrow fat fraction (BMFF), bone mineral density (BMD) and T score values were calculated in three vertebrae (L2-L4) for each subject. Both the CBWPD and BMFF were correlated with BMD, T score, and FRAX score for comparison. The abilities of the CBWPD and BMFF to discriminate between three different cohorts, which included normal subjects, patients with osteopenia, and patients with osteoporosis, were also evaluated and compared using receiver operator characteristic (ROC) analysis. RESULTS: The CBWPD showed strong correlation with standard BMD (R2 = 0.75, P < 0.001) and T score (R2 = 0.59, P < 0.001), as well as a moderate correlation with FRAX score (R2 = 0.48, P < 0.001). High area under the curve (AUC) values (≥ 0.84 using QCT as reference; ≥ 0.76 using DXA as reference) obtained from ROC analysis demonstrated that the CBWPD was capable of well differentiating between the three different subject cohorts. Moreover, the CBWPD had better correlations with BMD, T score, and FRAX score than BMFF, and also performed better in cohort discrimination. CONCLUSION: The STAIR-UTE-measured CBWPD is a promising biomarker in the assessment of bone quality and fracture risk.

Automatic Grading of Disc Herniation, Central Canal Stenosis and Nerve Roots Compression in Lumbar Magnetic Resonance Image Diagnosis.

Aim: Accurate severity grading of lumbar spine disease by magnetic resonance images (MRIs) plays an important role in selecting appropriate treatment for the disease. However, interpreting these complex MRIs is a repetitive and time-consuming workload for clinicians, especially radiologists. Here, we aim to develop a multi-task classification model based on artificial intelligence for automated grading of lumbar disc herniation (LDH), lumbar central canal stenosis (LCCS) and lumbar nerve roots compression (LNRC) at lumbar axial MRIs. Methods: Total 15254 lumbar axial T2W MRIs as the internal dataset obtained from the Fifth Affiliated Hospital of Sun Yat-sen University from January 2015 to May 2019 and 1273 axial T2W MRIs as the external test dataset obtained from the Third Affiliated Hospital of Southern Medical University from June 2016 to December 2017 were analyzed in this retrospective study. Two clinicians annotated and graded all MRIs using the three international classification systems. In agreement, these results served as the reference standard; In disagreement, outcomes were adjudicated by an expert surgeon to establish the reference standard. The internal dataset was randomly split into an internal training set (70%), validation set (15%) and test set (15%). The multi-task classification model based on ResNet-50 consists of a backbone network for feature extraction and three fully-connected (FC) networks for classification and performs the classification tasks of LDH, LCCS, and LNRC at lumbar MRIs. Precision, accuracy, sensitivity, specificity, F1 scores, confusion matrices, receiver-operating characteristics and interrater agreement (Gwet k) were utilized to assess the model's performance on the internal test dataset and external test datasets. Results: A total of 1115 patients, including 1015 patients from the internal dataset and 100 patients from the external test dataset [mean age, 49 years ± 15 (standard deviation); 543 women], were evaluated in this study. The overall accuracies of grading for LDH, LCCS and LNRC were 84.17% (74.16%), 86.99% (79.65%) and 81.21% (74.16%) respectively on the internal (external) test dataset. Internal and external testing of three spinal diseases showed substantial to the almost perfect agreement (k, 0.67 - 0.85) for the multi-task classification model. Conclusion: The multi-task classification model has achieved promising performance in the automated grading of LDH, LCCS and LNRC at lumbar axial T2W MRIs.

Automated Magnetic Resonance Image Segmentation of Spinal Structures at the L4-5 Level with Deep Learning: 3D Reconstruction of Lumbar Intervertebral Foramen.

OBJECTIVE: 3D reconstruction of lumbar intervertebral foramen (LIVF) has been beneficial in evaluating surgical trajectory. Still, the current methods of reconstructing the 3D LIVF model are mainly based on manual segmentation, which is laborious and time-consuming. This study aims to explore the feasibility of automatically segmenting lumbar spinal structures and increasing the speed and accuracy of 3D lumbar intervertebral foramen (LIVF) reconstruction on magnetic resonance image (MRI) at the L4-5 level. METHODS: A total of 100 participants (mean age: 42.2 ± 14.0 years; 52 males and 48 females; mean body mass index, 22.7 ± 3.2 kg/m2 ), were enrolled in this prospective study between March and July 2020. All participants were scanned on L4-5 level with a 3T MR unit using 3D T2-weighted sampling perfection with application-optimized contrast with various flip-angle evolutions (SPACE) sequences. The lumbar spine's vertebra bone structures (VBS) and intervertebral discs (IVD) were manually segmented by skilled surgeons according to their anatomical outlines from MRI. Then all manual segmentation were saved and used for training. An automated segmentation method based on a 3D U-shaped architecture network (3D-UNet) was introduced for the automated segmentation of lumbar spinal structures. A number of quantitative metrics, including dice similarity coefficient (DSC), precision, and recall, were used to evaluate the performance of the automated segmentation method on MRI. Wilcoxon signed-rank test was applied to compare morphometric parameters, including foraminal area, height and width of 3D LIVF models between automatic and manual segmentation. The intra-class correlation coefficient was used to assess the test-retest reliability and inter-observer reliability of multiple measurements for these morphometric parameters of 3D LIVF models. RESULTS: The automatic segmentation performance of all spinal structures (VBS and IVD) was found to be 0.918 (healthy levels: 0.922; unhealthy levels: 0.916) for the mean DSC, 0.922 (healthy levels: 0.927; unhealthy levels: 0.920) for the mean precision, and 0.917 (healthy levels: 0.918; unhealthy levels: 0.917) for the mean recall in the test dataset. It took approximately 2.5 s to achieve each automated segmentation, far less than the 240 min for manual segmentation. Furthermore, no significant differences were observed in the foraminal area, height and width of the 3D LIVF models between manual and automatic segmentation images (P > 0.05). CONCLUSION: A method of automated MRI segmentation based on deep learning algorithms was capable of rapidly generating accurate segmentation of spinal structures and can be used to construct 3D LIVF models from MRI at the L4-5 level.

NVP-BEZ235 synergizes cisplatin sensitivity in osteosarcoma.

Osteosarcoma(OS) remains a major health concern in childhood and adolescence, although cisplatin is one of the gold standard chemotherapeutic drugs in the treatment of OS, chemoresistant to cisplatin is common. Phosphoinositide 3-kinase (PI3K)-Akt-mammalian target of rapamycin inhibitor (mTOR) pathway and autophagy regulates chemosensitivity incancer cells. In this study, we hypothesized that NVP-BEZ235, a dual inhibitor of PI3K/mTOR, could synergize cisplatin sensitivity in OS. In vitro, NVP-BEZ235 plus cisplatinexerted a synergistic effect on cell proliferation inhibition and apoptosis induction. Cisplatin could activate PI3K-Akt-mTOR pathway activity in early times, whereas, NVP-BEZ235 could inhibit PI3K-Akt -mTOR pathway activity all the times alone or combined with cisplatin. What's more, NVP-BEZ235 could switch function of autophagy induced by cisplatin to synergize cisplatin sensitivity. In vivo, pronounced decrease in tumor cell proliferation and increase in apoptosisin combination-treated mouse xenograft models compared with cisplatin or NVP-BEZ235 treated models. All these results suggest NVP-BEZ235 could synergize cisplatin sensitivity in OS, combination of NVP-BEZ235 with cisplatin could represent a novel therapeutic strategy for treatment of OS.