With an aging society, osteoporosis is one of the most common diseases threatening the health of China's elderly population and is an issue that is raising increasing concern. Osteoporosis is characterized by bone loss and increased susceptibility to fragility fractures. Various imaging modalities such as X-ray, CT, MRI and nuclear medicine along with assessment of bone mineral density (BMD) play an important role in its diagnosis and management, and the treatment requires multidisciplinary teamwork. A lack of consensus in the approach to imaging and BMD measurement is hampering the quality of service and patient care in China. Therefore a panel of Chinese experts from the fields of radiology, orthopedics, endocrinology and nuclear medicine reviewed the international guidelines, consensus and literature with the most recent data from China and, taking account of current clinical practice in China, the panel reached this consensus to help guide the diagnosis of osteoporosis using imaging and BMD. This consensus report provides guidelines and standards for the imaging and BMD assessment of osteoporosis and criteria for the diagnosis of osteoporosis in China.
Background Diffusion‐weighted imaging (DWI) can quantify the microstructural changes in the spinal cord. It might be a substitute for T2 increased signal intensity (ISI) for cervical spondylotic myelopathy (CSM) evaluation and prognosis. Purpose The purpose of the study is to investigate the relationship between DWI metrics and neurologic function of patients with CSM. Study Type Retrospective. Population Forty‐eight patients with CSM (18.8% females) and 36 healthy controls (HCs, 25.0% females). Field Strength/Sequence 3 T; spin‐echo echo‐planar imaging‐DWI; turbo spin‐echo T1/T2; multi‐echo gradient echo T2*. Assessment For patients, conventional MRI indicators (presence and grades of T2 ISI), DWI indicators (neurite orientation dispersion and density imaging [NODDI]‐derived isotropic volume fraction [ISOVF], intracellular volume fraction, and orientation dispersion index [ODI], diffusion tensor imaging [DTI]‐derived fractional anisotropy [FA] and mean diffusivity [MD], and diffusion kurtosis imaging [DKI]‐derived FA, MD, and mean kurtosis), clinical conditions, and modified Japanese Orthopaedic Association (mJOA) were recorded before the surgery. Neurologic function improvement was measured by the 3‐month follow‐up recovery rate (RR). For HCs, DWI, and mJOA were measured as baseline comparison. Statistical Tests Continuous (categorical) variables were compared between patients and HCs using Student's t‐tests or Mann–Whitney U tests (chi‐square or Fisher exact tests). The relationships between DWI metrics/conventional MRI findings, and the pre‐operative mJOA/RR were assessed using correlation and multivariate analysis. P < 0.05 was considered statistically significant. Results Among patients, grades of T2 ISI were not correlated with pre‐surgical mJOA/RR (P = 0.717 and 0.175, respectively). NODDI ODI correlated with pre‐operative mJOA (r = −0.31). DTI FA, DKI FA, and NODDI ISOVF were correlated with the recovery rate (r = 0.31, 0.41, and −0.34, respectively). In multivariate analysis, NODDI ODI (DTI FA, DKI FA, NODDI ISOVF) significantly contributed to the pre‐operative mJOA (RR) after adjusting for age. Data Conclusion DTI FA, DKI FA, and NODDI ISOVF are predictors for prognosis in patients with CSM. NODDI ODI can be used to evaluate CSM severity. Level of Evidence 3 Technical Efficacy Stage 5
Background:The diagnosis of labral injury on MRI is time-consuming and potential for incorrect diagnoses. Purpose: To explore the feasibility of applying deep learning to diagnose and classify labral injuries with MRI. Study Type: Retrospective. Population: A total of 1016 patients were divided into normal (n = 168, class 0) and abnormal labrum (n = 848) groups. The abnormal group consisted of n = 111 with class 1 (degeneration), n = 437 with class 2 (partial or complete tear), and n = 300 with unclassified injury. Patients were randomly divided into training, validation, and test cohort according to the ratio of 55%:15%:30%. Field Strength/Sequence: Fat-saturation proton density-weighted fast spin-echo sequence at 3.0 T. Assessment: Convolutional neural network-6 (CNN-6) was used to extract, discriminate, and detect oblique coronal (OCOR) and oblique sagittal (OSAG) images. Mask R-CNN was used for segmentation. LeNet-5 was used to diagnose and classify labral injuries. The weighting method combined the models of OCOR and OSAG. The output-input connection was used to correlate the whole diagnosis/classification system. Four radiologists performed subjective diagnoses to obtain the diagnosis results. Statistical Tests: CNN-6 and LeNet-5 were evaluated by area under the receiver operating characteristic (ROC) curve and related parameters. The mean average precision (MAP) evaluated the Mask R-CNN. McNemar's test was used to compare the radiologists and models. A P value < 0.05 was considered statistically significant. Results: The area under the curve (AUC) of CNN-6 was 0.99 for extraction, discrimination, and detection. MAP values of Mask R-CNN for OCOR and OSAG image segmentation were 0.96 and 0.99. The accuracies of LeNet-5 in the diagnosis and classification were 0.94/0.94 (OCOR) and 0.92/0.91 (OSAG), respectively. The accuracy of the weighted models in the diagnosis and classification were 0.94 and 0.97, respectively. The accuracies of radiologists in the diagnosis and classification of labrum injuries ranged from 0.85 to 0.92 and 0.78 to 0.94, respectively. Data Conclusion: Deep learning can assist radiologists in diagnosing and classifying labrum injuries.
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