Objective To explore the establishment,implementation,and outcomes of an integrated physician-nurse team-based comprehensive management model for breast cancer chemotherapy outpatients,ai-ming to provide a reference for standardized patient care.Methods In January 2019,the Breast Surgery De-partment of Peking Union Medical College Hospital developed an integrated physician-nurse team and estab-lished a full-cycle management pathway covering the pre-chemotherapy,chemotherapy,and inter-cycle phases.This model featured appointment-based scheduling,time-segmented visits,and closed-loop patient management.Key performance indicators-including healthcare efficiency,chemotherapy safety,staffing ratios,and satisfaction levels among patients and healthcare providers-were compared between pre-implementation(2018)and post-imple-mentation(2019)periods.Results Before implementation,patient waited times ranged from 30 to 120(75.40±20.97)minutes,with an annual chemotherapy volume of 8 715 cases.Two ward nurses were routinely redeployed daily to support the chemotherapy clinic.Post-implementation,patients received timely chemotherapy per scheduled appointments without delays,annual chemotherapy volume in-creased to 10 101 cases,and staffing between two chemotherapy units became flexibly adjustable.Ward nurses transitioned from an on-call to a reserve role.Adverse events(chemotherapy-related,catheter-related,and nursing incidents)remained at 0,consistent with pre-implementation levels.Both patient and staff satisfaction scores significantly improved across all domains(all P＜0.05).Conclusions The integrated physician-nurse team-based chemotherapy management model enhances service efficiency,ensures treatment safety,optimizes workforce allocation,and improves satisfaction among patients and healthcare providers.

Objective:To evaluate the diagnostic value of micro flow imaging (MFI) supported by Image Pro Plus (IPP) software for BI-RADS classification of breast nodules and the correlation between its parameters and Ki-67 expression level.Methods:A total of 205 cases of breast solid nodules with ultrasound BI-RADS classification of 4 categories were enrolled in this study. BI-RADS classification, CDFI score, MFI score, CDFI-IPP score and MFI-IPP score by IPP software were performed for each nodule. Ki-67 immunohistochemical staining of invasive ductal carcinoma was performed to analyze the correlation between MFI-IPP-VI and Ki-67 expression levels.Results:The combined BI-RADS classification for diagnosis of malignant nodules had the largest area under the ROC curve, which was significantly higher than that of BI-RADS, MFI-IPP-VI, MFI score, CDFI-IPP-VI, and CDFI score (all P<0.05). The combined BI-RADS classification had the best diagnostic efficacy for the diagnosis of malignant nodules with a sensitivity of 96.4%, specificity of 65.6% and accuracy of 82.4%. In addition, the agreement between experienced and inexperienced sonographers applying MFI-IPP-VI was higher than MFI (Kappa=0.780 vs. Kappa=0.743). The mean value of Ki-67 in invasive ductal carcinoma was 21.93%±16.37%, and Spearman's correlation analysis showed a positive correlation between MFI-IPP-VI and Ki-67 expression level ( r=0.815, P<0.05). Conclusions:MFI-IPP can assist sonographers to classify breast nodules by BI-RADS, providing an effective basis for puncture biopsy. MFI-IPP-VI offers the possibility of noninvasively predicting the expression level of Ki-67 in invasive ductal carcinoma.

Objective To explore the establishment,implementation,and outcomes of an integrated physician-nurse team-based comprehensive management model for breast cancer chemotherapy outpatients,ai-ming to provide a reference for standardized patient care.Methods In January 2019,the Breast Surgery De-partment of Peking Union Medical College Hospital developed an integrated physician-nurse team and estab-lished a full-cycle management pathway covering the pre-chemotherapy,chemotherapy,and inter-cycle phases.This model featured appointment-based scheduling,time-segmented visits,and closed-loop patient management.Key performance indicators-including healthcare efficiency,chemotherapy safety,staffing ratios,and satisfaction levels among patients and healthcare providers-were compared between pre-implementation(2018)and post-imple-mentation(2019)periods.Results Before implementation,patient waited times ranged from 30 to 120(75.40±20.97)minutes,with an annual chemotherapy volume of 8 715 cases.Two ward nurses were routinely redeployed daily to support the chemotherapy clinic.Post-implementation,patients received timely chemotherapy per scheduled appointments without delays,annual chemotherapy volume in-creased to 10 101 cases,and staffing between two chemotherapy units became flexibly adjustable.Ward nurses transitioned from an on-call to a reserve role.Adverse events(chemotherapy-related,catheter-related,and nursing incidents)remained at 0,consistent with pre-implementation levels.Both patient and staff satisfaction scores significantly improved across all domains(all P＜0.05).Conclusions The integrated physician-nurse team-based chemotherapy management model enhances service efficiency,ensures treatment safety,optimizes workforce allocation,and improves satisfaction among patients and healthcare providers.

Objective:To evaluate the diagnostic value of micro flow imaging (MFI) supported by Image Pro Plus (IPP) software for BI-RADS classification of breast nodules and the correlation between its parameters and Ki-67 expression level.Methods:A total of 205 cases of breast solid nodules with ultrasound BI-RADS classification of 4 categories were enrolled in this study. BI-RADS classification, CDFI score, MFI score, CDFI-IPP score and MFI-IPP score by IPP software were performed for each nodule. Ki-67 immunohistochemical staining of invasive ductal carcinoma was performed to analyze the correlation between MFI-IPP-VI and Ki-67 expression levels.Results:The combined BI-RADS classification for diagnosis of malignant nodules had the largest area under the ROC curve, which was significantly higher than that of BI-RADS, MFI-IPP-VI, MFI score, CDFI-IPP-VI, and CDFI score (all P<0.05). The combined BI-RADS classification had the best diagnostic efficacy for the diagnosis of malignant nodules with a sensitivity of 96.4%, specificity of 65.6% and accuracy of 82.4%. In addition, the agreement between experienced and inexperienced sonographers applying MFI-IPP-VI was higher than MFI (Kappa=0.780 vs. Kappa=0.743). The mean value of Ki-67 in invasive ductal carcinoma was 21.93%±16.37%, and Spearman's correlation analysis showed a positive correlation between MFI-IPP-VI and Ki-67 expression level ( r=0.815, P<0.05). Conclusions:MFI-IPP can assist sonographers to classify breast nodules by BI-RADS, providing an effective basis for puncture biopsy. MFI-IPP-VI offers the possibility of noninvasively predicting the expression level of Ki-67 in invasive ductal carcinoma.

ObjectiveThis study aimed to investigate the current status of biosafety management in laboratory animal institutions in Hunan Province, identify management shortcomings, and provide targeted recommendations for improvement. MethodsA questionnaire survey was conducted in July 2023 among 40 laboratory animal institutions in Hunan Province, covering topics such as the establishment of laboratory management system and its overall operation status, emergency management for incidents, medical waste disposal, animal transportation, personnel practitioner management, environmental disinfection, and equipment maintenance. The awareness of biosafety, animal bite incidents, and vaccination history was surveyed from at least one animal feeder or experimental operation practitioner from each institution, totaling 50 practitioners. Through literature review and comparative analysis, this paper analyzed the current status of biosafety management in production, transportation, and use of laboratory animals in Hunan Province from aspects such as biosafety management systems, personnel training, disinfection of environmental facilities and waste disposal, and provided recommendations. ResultsAmong the 40 laboratory animal institutions in Hunan Province,39 institutions (97.5%) had established laboratory management systems, 37 (92.5%) had emergency response plans for incidents of to transport laboratory animals, 15 (37.5%) had complete records of emergency drills for laboratory animal biosafety, and 39 (97.5%) had established protocols for carcass and waste management. Moreover, 26 institutions (65.0%) had laboratory biosafety risk assessment reports. 4 out of 8 (50.0%) production units had emergency plans for animal transportation, with 7 (87.5%) using special vehicles to transport laboratory animals. 2 (4.0%) of the 50 practitioners were unaware of emergency response plans, 6 participants (12.0%) did not consistently wear personal protective equipment, and 38 participants (76.0%) had been bitten or scratched by laboratory animals. The results of the survey showed that emergency management, transportation management and personnel management of laboratory animal incidents still need improvement in Hunan Province. Relevant functional departments should strengthen supervision and management efforts, establish local regulations for the transportation management of laboratory animals, ensure strict compliance with relevant documents, conduct emergency drills in all units, and strengthen training for practitioners. ConclusionWhile daily management practices in the 40 laboratory animal institutions in Hunan Province are generally satisfactory, there remains a need for further improvement in emergency management, transportation management, personnel management, etc. Gradually enhancing the level of laboratory animal safety management is essential to collectively safeguard public health safety.

The emergency management of biosafety for laboratory animals in Hunan Province is an essential component of the province's implementation of the national biosafety strategy.To strengthen the prevention and control of biosafety risks in laboratory animals and to ensure a quick and effective response to laboratory animal biosafety emergencies,Hunan Province has formulated the"Emergency Response Plan for Laboratory Animal Biosafety Emergencies in Hunan Province".This plan aims to minimize damages caused to practitioners,public health,and laboratory animal industry,protect lives and property,and safeguard public safety and social stability.This paper analyzes the necessity,guiding ideology,principles,and basis for formulating the plan.It details the main contents of the plan,which includes scope of application,incident classification,organizational structure and responsibilities,monitoring and early warning mechanisms,emergency reporting and response,post-incident assessment,and safeguard measures.Moreover,this paper provides a summary and outlook on the emergency management of biosafety laboratory animals in Hunan Province in recent years.

Due to the low contrast of CT images,irregular shape of the liver,and blurred boundaries with adjacent organs,the existing methods based on convolutional neural network underperform in liver segmentation tasks,especially for boundary recognition and small object detection.A novel liver segmentation method is proposed based on multi-scale feature fusion and attention,namely MFFA UNet.Multi-scale feature fusion is firstly employed to acquire abundant segmentation details,while spatial and channel attention mechanisms are utilized to capture global spatial and inter-channel relationships.Additionally,a deep supervision module fully leverages the output of intermediate hidden layers,enhancing the learning capability of the network,which in turn accelerates the network's convergence speed.Moreover,a hybrid loss function is adopted to address the issue of class imbalance,further boosting the model's segmentation efficacy.Experimental results demonstrate that the proposed MFFA UNet outperforms the prevailing segmentation networks on the public LITS dataset,producing results that are closer to the ground truth.

Objective:To predict the clinical value of World Health Organization/International Society of Urological Pathology (WHO/ISUP) grading of clear cell renal cell carcinoma (ccRCC) pre-scholarly based on ultrasound imaging group.Methods:Clinical and ultrasound imaging data of patients with surgically pathologically confirmed ccRCC at Tianjin Medical University Cancer Institue and Hospital from January 2021 to October 2022 were retrospectively collected and divided into a low grade group (grade Ⅰ and Ⅱ, 105 cases) and a high grade group (grade Ⅲ and Ⅳ, 70 cases) using WHO/ISUP pathological grading criteria. The clear image of the largest diameter of the tumor was selected and imported into ITK-SNAP software for manual segmentation of the image and extraction of ultrasonographic radiomics features. The patients were randomly divided into a training group and a test group in the ratio of 7∶3, with 122 cases in the training group and 53 cases in the test group. Stable radiomics features were obtained by dimensionality reduction. The support vector machines (SVM) algorithm was applied to predict the pathological grading of ccRCC. Finally, a clinical-ultrasound imaging model, an ultrasonographic radiomics model and a comprehensive model combining the two were constructed. The predictive effects of the three models were analyzed by the area under the ROC curve (AUC). The performance of each model was evaluated by applying the calibration curve. The net benefit of patients was obtained by applying the decision curve.Results:A total of 873 radiomics features were extracted, and 10 features were finally obtained for model construction after dimensionality reduction. Final test results showed that the AUC, sensitivity, specificity and accuracy of the clinical-ultrasound imaging model were 0.68, 0.47, 0.78, 0.66. The AUC, sensitivity, specificity and accuracy of the ultrasonographic radiomics model were 0.74, 0.53, 0.88, 0.74. The AUC, sensitivity, specificity and accuracy of the comprehensive model were 0.84, 0.63, 0.86, 0.77. The AUC of the comprehensive model being larger than that of the clinical-ultrasound imaging model ( Z=-3.224, P=0.001) and ultrasonographic radiomics model ( Z=-2.594, P=0.009). The calibration curves showed that the comprehensive model was more stable than the other two models. The decision curve showed a higher net clinical benefit for the comprehensive model than for the other two models within a threshold of 0.1-1.0. Conclusions:The preoperative prediction of ccRCC pathological grading by the radiomics model based on ultrasound images is effective. The comprehensive model constructed by combining relevant clinical and ultrasound parameters has better performance, which can help predict ccRCC pathological grading preoperatively to a certain extent. It is crucial to help physicians choose the best management plan in the era of personalized medicine.

Objective:To investigate the prediction of National Institute of Healthy (NIH) risk stratification of gastrointestinal stromal tumor(GIST) based on clinical ultrasound model, ultrasonographic radiomics model and combined model by oral contrast enhanced ultrasonography.Methods:The clinical and ultrasound imaging data of 204 gastric GIST patients attending Tianjin Medical University Cancer Institute and Hospital from June 2021 to June 2022 were retrospectively analyzed, among whom a total of 101 patients with high and moderate NIH risk stratification GIST confirmed by postoperative pathology were included in the high risk group, and a total of 103 patients with low and extremely low NIH risk stratification GIST were in the low risk group. The ultrasound images of the largest diameter of the GIST were manually segmented by ITK-SNAP software, and Pyradiomics (v3.0.1) module in Python 3.8.7 was applied to extract ultrasonographic radiomics features from the ROI segmented images. The patients were randomly divided into training and validation sets in the ratio of 7∶3. The XGBoost of Sklearn module was applied to construct the clinical ultrasound imaging model, ultrasonographic radiomics model, and combined model. Then the area under ROC curve (AUC), sensitivity, specificity, and accuracy were evaluated; the predictive ability of the three models was compared by Delong test. Calibration Curve was applied to evaluate the model performance, and the clinical Decision Curve Analysis was applied to determine the net benefit to patients.Results:A total of 578 ultrasonographic radiomics features were extracted from ROI, and 8 ultrasonographic radiomics features were finally retained for modeling after regression and dimensionality reduction. Finally, test results showed that AUC, sensitivity, specificity and accuracy of clinical ultrasound imaging model, ultrasonographic radiomics model and combined model were 0.75, 69.3%, 68.9%, 69.1%; 0.87, 79.2%, 81.6%, 80.4%; 0.91, 80.2%, 83.5%, 81.9%, respectively. Delong test showed that the difference of AUC between ultrasonographic radiomics model and clinical ultrasound imaging model was statistically significant ( Z=2.698, P<0.001), and the combined model was significantly better than clinical ultrasound imaging model ( Z=4.062, P<0.001) and ultrasonographic radiomics model ( Z=2.225, P=0.026). Calibration Curve showed the high performance of combined model, and Decision Curve Analysis showed the superior clinical usefulness of combined model. Conclusions:It is feasible to construct an ultrasonographic radiomics model for GIST NIH risk stratification based on oral contrast enhanced ultrasonography images, and the combined model has more advantageous diagnostic performance, which can identify high risk NIH GIST objectively and stably for clinical purposes.