Objective:To explore the effect of cluster nursing in robot-assisted surgery for the treatment of reducible atlantoaxial dislocation.Methods:A retrospective cohort study was conducted to analyze the clinical data of 41 patients with reducible atlantoaxial dislocation treated by robot-assisted surgery in Honghui Hospital affiliated to Xi′an Jiaotong University from January 2019 to December 2021, including 28 males and 13 females; aged 18-79 years [(45.2±10.3)years]. Ninteen patients received cluster nursing (cluster nursing group), with operating room nursing team set up on the basis of routine nursing and performed cluster nursing in line with evidence-based medicine. Twenty-two patients received routine nursing (routine nursing group). The operation time, intraoperative blood loss, frequency of intraoperative C-arm fluoroscopy, time of drainage tube placement and chief surgeon′s satisfaction for nursing were compared between the two groups. The degree of pain was evaluated by pain numerical score (NRS) at 12 hours, 24 hours, 48 hours, 72 hours, 1 month and 3 months after operation and at the last follow-up. The neck disability index (NDI) was assessed at 1 day before operation, 1 month after operation, 3 months after operation and at the last follow-up. The complications were observed.Results:All patients were followed up for 12-18 months [(16.7±3.7)months]. The operation time, intraoperative blood loss, frequency of C-arm fluoroscopy and time of drainage tube placement in cluster nursing group were (82.9±10.4)minutes, (105.9±11.8)ml, (3.8±0.6)times and (1.5±0.4)days, while those in routine nursing group were (125.7±12.8)minutes, (208.4±13.8)ml, (9.7±2.3)times and (3.6±0.6)days, respectively (all P<0.01). The chief surgeon′s satisfaction for nursing was 94.7% (18/19) in cluster nursing group and was 68.2% (15/22) in routine nursing group ( P<0.05). The NRS in cluster nursing group was (6.2±0.4)points, (6.0±0.7)points, (4.9±1.1)points, (2.7±0.5)points, (1.9±0.4)points, (1.8±0.4)points and (1.5±0.3)points at 12 hours, 24 hours, 48 hours, 72 hours, 1 month and 3 months after operation and at the last follow-up, while it was (7.6±0.6)points, (6.8±1.2)points, (5.8±1.5)points, (4.2±0.8)points, (3.4±0.7)points, (2.6±0.5)points and (2.2±0.5)points in routine nursing group ( P<0.05 or 0.01). There was no significant difference in the NDI between the two groups at 1 day before operation, but the NDI in cluster nursing group was 20.6±4.5, 14.6±2.8 and 10.7±2.5 at 1 month and 3 months after operation and at the last follow-up, while it was 26.9±4.1, 18.7±3.3 and 13.7±1.7 in routine nursing group (all P<0.01). There was no hematoma, infection or implant-related complications in both groups .Conclusion:For robot-assisted surgery in the treatment of reducible atlantoaxial dislocation, cluster nursing is associated with shortened operation time and time of drainage tube placement, decreased intraoperative blood loss and frequency of intraoperative fluoroscopy, increased chief surgeon′s satisfaction for nursing, reduced pain and accelerated functional recovery.

Objective:To investigate the value of preoperative prediction of Ki-67 expression status in breast cancer based on multi-phase enhanced MRI combined with clinical imaging characteristics prediction model.Methods:This study was retrospective. A total of 213 breast cancer patients who underwent surgical treatment at Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College between June 2016 and May 2017 were enrolled. All patients were female, aged 24-78 (51±10) years, and underwent routine breast MRI within 2 weeks prior to surgery. According to the different Ki-67 expression of postoperative pathological results, patients were divided into high expression group (Ki-67≥20%, 153 cases) and low expression group (Ki-67<20%, 60 cases). The radiomic features of breast cancer lesions were extracted from phase 2 (CE-2) and phase 7 (CE-7) images of dynamic contrast enhanced (DCE)-MRI, and all cases were divided into training and test sets according to the ratio of 7∶3. The radiomic features were first selected using ANOVA and Wilcoxon signed-rank test, followed by the least absolute shrinkage and selection operator method regression model. The same method of parameters selection was applied to clinical information and conventional imaging features [including gland classification, degree of background parenchymal enhancement, multifocal/multicentric, lesion location, lesion morphology, lesion long diameter, lesion short diameter, T 2WI signal characteristics, diffusion-weighted imaging (DWI) signal characteristics, apparent diffusion coefficient (ADC) values, time-signal intensity curve type, and axillary lymph nodes larger than 1 cm in short axis]. Support vector machine (SVM) was then used to construct prediction models for Ki-67 high and low expression states. The predictive performance of the models were evaluated using receiver operating characteristic (ROC) curves and area under cueve(AUC). Results:Totally 1 029 radiomic features were extracted from CE-2 and CE-7 images, respectively, and 9 and 7 best features were obtained after selection, respectively. And combining the two sets of features for a total of 16 features constituted the CE-2+CE-7 image best features. Five valuable parameters including lesion location, lesion short diameter, DWI signal characteristics, ADC values, and axillary lymph nodes larger than 1 cm in short axis, were selected from all clinical image features. The SVM prediction models obtained from the radiomic features of CE-2 and CE-7 images had a high AUC in predicting Ki-67 expression status (>0.70) in both the training set and the test set. The models were constructed by combining the CE-2, CE-7, and CE-2+CE-7 radiomic features with clinical imaging features, respectively, and the corresponding model performance in predicting Ki-67 expression status was improved compared with the models obtained by using the CE-2, CE-7, and CE-2+CE-7 radiomic features alone. The SVM prediction model obtained from CE-2+CE-7 radiomic features combined with clinical imaging features had the best prediction performance, with AUC of 0.895, accuracy of 84.6%, sensitivity of 87.9%, and specificity of 76.2% for predicting Ki-67 expression status in the training set and AUC of 0.822, accuracy of 70.3%, sensitivity of 76.1%, and specificity of 55.6% in test sets.Conclusion:The SVM prediction model based on DCE-MRI radiomic features can effectively predict Ki-67 expression status, and the combination of radiomic features and clinical imaging features can further improve the model prediction performance.