Nonunion of osteoporotic vertebral fractures (OVF), predominantly affecting the elderly, can lead to intractable pain, vertebral collapse, progressive kyphotic deformity, and neurological impairment, significantly compromising patients′ quality of life. There exists considerable debate on diagnosis and management of OVF, encompassing key issues such as clinical diagnosis and staging criteria for nonunion, surgical indications and procedure selection, and postoperative rehabilitation planning. Currently, there lacks standardized clinical guideline and expert consensus on the diagnosis and management of OVF nonunion in China. To address this gap, Minimally Invasive Surgery Group of Chinese Orthopedic Association, Osteoporosis Committee of Chinese Association of Orthopedic Surgeons, Prevention and Rehabilitation Committee for Osteoporosis of Chinese Association of Rehabilitation Medicine and Minimally Invasive Orthopedic Surgery Branch of China Association for Geriatric Care jointly organized domestic experts in spinal surgery, endocrinology, and rehabilitation to formulate the Clinical guideline for the diagnosis and treatment for nonunion of osteoporotic vertebral fractures ( version 2025), based on existing literature and clinical experience and adhering to principles of scientific rigor and practicality. The guideline provided 13 evidence-based recommendations encompassing diagnosis and treatment of OVF nonunion, aiming to standardize its clinical management.

Purpose To analyze the value of a deep learning(DL)radiomics model based on dynamic contrast-enhanced MRI images in predicting the expression of Ki-67 status in breast cancer.Materials and Methods A retrospective analysis of 152 breast cancer patients confirmed by pathological results at Guangdong Women and Children Hospital,MRI images and clinical pathological data were reviewed,and based on postoperative immunohistochemistry results,the images of the high and low expression groups of Ki-67 were randomly sampled in a ratio of 8∶2 to form a training set of 122 cases and a validation set of 30 cases.Single-factor and multi-factor Logistic regression analyses of clinical data were performed to select independent predictors of breast cancer expressing Ki-67 status.The ResNet-18 model was used as the basic model for DL feature extraction.Hand-crafted radiomic features and DL features were extracted.Eight machine learning models were constructed based on clinical features,hand-crafted radiomic features,DL features,and their combinations.The area under the receiver operating characteristic curve was used to evaluate the predictive performance of the models,and the best model was determined as the output model.Results The progesterone receptor status(OR=0.764,P=0.040)and human epidermal growth factor receptor-2 status(OR=1.187,P=0.046)were independent clinical predictors of breast cancer expressing Ki-67 status.The combined feature models demonstrated superior performance over the individual feature models,and the support vector machine algorithm had the highest prediction performance in the validation set,with an area under the curve of 0.847.Conclusion The DL radiomics model based on dynamic contrast-enhanced MRI images can effectively predict the expression of Ki-67 status in breast cancer.The support vector machine algorithm combined with feature model is the best,which can help the clinical diagnosis and treatment of breast cancer and prognosis evaluation.

Purpose To analyze the value of a deep learning(DL)radiomics model based on dynamic contrast-enhanced MRI images in predicting the expression of Ki-67 status in breast cancer.Materials and Methods A retrospective analysis of 152 breast cancer patients confirmed by pathological results at Guangdong Women and Children Hospital,MRI images and clinical pathological data were reviewed,and based on postoperative immunohistochemistry results,the images of the high and low expression groups of Ki-67 were randomly sampled in a ratio of 8∶2 to form a training set of 122 cases and a validation set of 30 cases.Single-factor and multi-factor Logistic regression analyses of clinical data were performed to select independent predictors of breast cancer expressing Ki-67 status.The ResNet-18 model was used as the basic model for DL feature extraction.Hand-crafted radiomic features and DL features were extracted.Eight machine learning models were constructed based on clinical features,hand-crafted radiomic features,DL features,and their combinations.The area under the receiver operating characteristic curve was used to evaluate the predictive performance of the models,and the best model was determined as the output model.Results The progesterone receptor status(OR=0.764,P=0.040)and human epidermal growth factor receptor-2 status(OR=1.187,P=0.046)were independent clinical predictors of breast cancer expressing Ki-67 status.The combined feature models demonstrated superior performance over the individual feature models,and the support vector machine algorithm had the highest prediction performance in the validation set,with an area under the curve of 0.847.Conclusion The DL radiomics model based on dynamic contrast-enhanced MRI images can effectively predict the expression of Ki-67 status in breast cancer.The support vector machine algorithm combined with feature model is the best,which can help the clinical diagnosis and treatment of breast cancer and prognosis evaluation.

Nonunion of osteoporotic vertebral fractures (OVF), predominantly affecting the elderly, can lead to intractable pain, vertebral collapse, progressive kyphotic deformity, and neurological impairment, significantly compromising patients′ quality of life. There exists considerable debate on diagnosis and management of OVF, encompassing key issues such as clinical diagnosis and staging criteria for nonunion, surgical indications and procedure selection, and postoperative rehabilitation planning. Currently, there lacks standardized clinical guideline and expert consensus on the diagnosis and management of OVF nonunion in China. To address this gap, Minimally Invasive Surgery Group of Chinese Orthopedic Association, Osteoporosis Committee of Chinese Association of Orthopedic Surgeons, Prevention and Rehabilitation Committee for Osteoporosis of Chinese Association of Rehabilitation Medicine and Minimally Invasive Orthopedic Surgery Branch of China Association for Geriatric Care jointly organized domestic experts in spinal surgery, endocrinology, and rehabilitation to formulate the Clinical guideline for the diagnosis and treatment for nonunion of osteoporotic vertebral fractures ( version 2025), based on existing literature and clinical experience and adhering to principles of scientific rigor and practicality. The guideline provided 13 evidence-based recommendations encompassing diagnosis and treatment of OVF nonunion, aiming to standardize its clinical management.

Objective:To explore the relationship between the life satisfaction of family caregivers and the de-gree of disability of disabled elderly people in Xinjiang Uygur and Kazak nationality,and the role of family mem-bers'participation in the relationship.Methods:A total of 431 elderly people with disabilities at home and their fam-ily caregivers(247 without family members and 184 with family members)were selected from Xinjiang Uygur and Kazak ethnic groups,and the survey was conducted with the Activity of Daily Living Scale(ADL)and Life Satis-faction Index B(LSIB).Results:The LSIB scores in family caregivers were negatively correlated with the ADL scores in the disabled elderly(r=-0.19,P＜0.01),and the family members'participation in care was positively correlated with the LSIB scores of family caregivers(r=0.52,P＜0.01).Family members'participation in care could moderate the negative effect of the ADL scores in the disabled elderly on the LSIB scores in family caregivers(β=0.08,P＜0.05).Conclusion:The involvement of family members in care has a moderating effect on the life satisfaction of Uyghur and Kazak family caregivers and the degree of disability of disabled elderly people.

Lumbar spondylolysis refers to the bone injury between the upper and lower articular processes and the transition zone of the transverse process of the unilateral or bilateral pedicle of the lumbar spine, being a common cause of low back pain in patients that seriously affects their quality of life. The mechanism of the occurrence and development of lumbar spondylolysis is complex, and long-term stress wear and sudden damage with an external force are the main causes. At the same time, risk factors related to spinal anatomy are important causes of lumbar spondylolysis. A full understanding of the pathogenesis of lumbar spondylolysis, early identification of high-risk groups, and active preventive measures can reduce its incidence. For this purpose, the authors reviewed the research progress in risk factors related to the spinal anatomy of lumbar spondylolysis from three aspects including genetical susceptibility, local anatomy and overall spine-pelvic sequence, so as to provide references for the prevention and treatment of spondylolysis.

Objective:To introduce a novel risk factor for lumbar spondylolysis, the pedicle isthmus angle (PIA), and to explore its underlying mechanism and clinical relevance.Methods:A retrospective analysis of CT imaging data from young male patients with lumbar spondylolysis, admitted to the 960th Hospital of the Joint Logistic Support Force of the PLA between January 2018 and August 2023, was conducted. The study included 119 cases of unilateral spondylolysis and 339 cases of bilateral spondylolysis, with a mean age of 22.8±3.4 years (range 18-30 years). A control group of 458 patients with normal lumbar CT scans, presenting with low back pain, was also analyzed. Their mean age was 22.9±3.5 years (range 18-30 years). The PIA of the left and right sides of the L 3, L 4, and L 5 vertebrae in both the spondylolysis and control groups were measured using CT imaging. Differences in PIA measurements between the left and right sides, as well as between groups, were compared. Binary logistic regression analysis identified risk factors for lumbar spondylolysis. The receiver operating characteristic (ROC) curve and Youden index were used to determine the critical risk threshold for lumbar spondylolysis. Results:No significant differences were found between the spondylolysis and control groups in terms of gender, age, height, weight, or body mass index (BMI) ( P>0.05). Similarly, there was no significant difference in the left and right PIA measurements for the L 3, L 4, and L 5 vertebrae in either group ( P>0.05). The PIA of the L 3 and L 4 vertebrae was not significantly different between the groups (107.2°±3.5° vs. 107.1°±3.5°, t=0.270, P=0.787; 110.6°±3.5° vs. 110.5°±4.0°, t=0.441, P=0.659). However, the PIA of the L 5 vertebra was significantly larger in the spondylolysis group (117.7°±4.7°) compared to the control group (114.0°±4.9°) ( t=11.654, P<0.001). Logistic regression analysis identified an increased PIA at L 5 ( β=0.159, OR=1.172, P<0.001) as a risk factor for lumbar spondylolysis. According to the ROC curve and Youden index, the risk of lumbar spondylolysis increased substantially when the L 5 PIA exceeded 115.8°. The area under the curve (AUC) was 0.709, with a sensitivity of 0.670 and a specificity of 0.644. Conclusion:PIA is an objective and effective imaging parameter for predicting lumbar spondylolysis. It aids in understanding the pathophysiology of spondylolysis, identifying high-risk individuals, and informing prevention and treatment strategies for lumbar spondylolysis.

Purpose: Knowing the distinction between benign and borderline/malignant phyllodes tumors (PTs) can help in the surgical treatment course. Herein, we investigated the value of magnetic resonance imaging-based texture analysis (MRI-TA) in differentiating between benign and borderline/malignant PTs. Methods: Forty-three women with 44 histologically proven PTs underwent breast MRI before surgery and were classified into benign (n = 26) and borderline/malignant groups (n = 18 [15 borderline, 3 malignant]). Clinical and routine MRI parameters (CRMP) and MRI-TA were used to distinguish benign from borderline/malignant PT. In total, 298 texture parameters were extracted from fat-suppression (FS) T2-weighted, FS unenhanced T1-weighted, and FS first-enhanced T1-weighted sequences. To evaluate the diagnostic performance, receiver operating characteristic curve analysis was performed for the K-nearest neighbor classifier trained with significantly different parameters of CRMP, MRI sequence-based TA, and the combination strategy. Results: Compared with benign PTs, borderline/malignant ones presented a higher local recurrence (p = 0.045); larger size (p < 0.001); different time-intensity curve pattern (p = 0.010); and higher frequency of strong lobulation (p = 0.024), septation enhancement (p = 0.048), cystic component (p = 0.023), and irregular cystic wall (p = 0.045). TA of FS T2-weighted images (0.86) showed a significantly higher area under the curve (AUC) than that of FS unenhanced T1-weighted (0.65, p = 0.010) or first-enhanced phase (0.72, p = 0.049) images. The texture parameters of FS T2-weighted sequences tended to have a higher AUC than CRMP (0.79, p = 0.404). Additionally, the combination strategy exhibited a similar AUC (0.89, p = 0.622) in comparison with the texture parameters of FS T2-weighted sequences. Conclusion MRI-TA demonstrated good predictive performance for breast PT pathological grading and could provide surgical planning guidance. Clinical data and routine MRI features were also valuable for grading PTs.

Objective To explore the reliability and validity of poly ether ether ketone (PEEK) rods and pedicle screw system for lumbar degenerative diseases.Methods Sixty-three patients,including 23 men and 40 women with the mean age of 56.2±5.1 years (range,35-75 years),treated by PEEK rods pedicle screw system from September 2012 to March 2013 were retrospectively recruited.Fusion procedure were performed in 10 patients,with 2 cases at L3,4,5 cases at L4,5,3 cases at L5S1; nonfusion procedure were performed in 17 patients,with 4 cases at L3,4,10 cases at L4,5,3 cases at L5S1; hybrid procedure were performed in 36 patients,with 22 case fusion at L4,5 and nonfusion at L3,4,with 2 cases fusion at L3,4 and nonfusion at L4,5,with 12 cases fusion at L5S1 and nonfusion at L4,5.Clinical effects were evaluated via Japanese Orthopedics Association (JOA) score and Oswestry disfunction index (ODI) at 3 months,6 months and 24 months postoperatively.Disc heights were measured by disc height index on lateral lumbar radiograph.Range of motion (ROM) was measured by extension-flexion lumbar X-ray.Three-dimensional CT reconstruction was achieved to observe the integrity of PEEK rods at the final follow-up.Results The mean follow-up were 23.4±3.3 months.The mean operation time were 110±17 min in the fusion group,98±22 min in the non-fusion group and 121±25 min in the hybrid group.The mean blood loss were 270±85 ml in the fusion group,255±72 ml in the non-fusion group and 316±80 ml in the hybrid group.The JOA score improved from 10.5±2.6 preoperatively to 24.0±3.1 at the final follow-up in the fusion group,from 10.3±2.2 to 24.3±3.4 in the non-fusion group and from 11.1±2.3 to 23.9±3.3 in the hybrid group (P＜0.05).The ODI decreased from 51.8％±10.3％ preoperatively to 14.1％±3.7％ at the final follow-up in the fusion group,from 52.2％±11.1％ to 13.2％±3.2％ in the non-fusion group and from 53.4％±9.2％ to 13.8％±2.5％ in the hybrid group.There was no significantly statistical difference in the disc height index at 3 months,6 months and 24 months postoperatively compared with that at preoperative.The ROM decreased from 8.8°±1.8° preoperatively to 2.2°±0.3° at the final follow-up (P＜0.05).One case demonstrated cage migration at one month follow-up,bony fusion in situ at 3 months follow-up,and the patient had no related symptoms during the follow-up.No screw loosening or breakage and rods breakage was observed during the follow-up.Conclusion PEEK rods and pedicle screw system for lumbar degenerative diseases by fusion,non-fusion and hybrid procedure can achieve considerable clinical outcome with low complication rate.