Objective To predict the lymphovascular invasion(LVI)status of breast cancer patients based on digital breast tomo-synthesis(DBT)intratumoral and peritumoral radiomics nomogram.Methods A total of 192 breast cancer patients from 2 institu-tions were retrospectively selected,in which institution 1 was used for train(n=113)and test(n=49),while institution 2 was used for external validation(n=30).Radiomics features were extracted and selected based on intratumoral and peritumoral 1 mm regions from DBT images.Different machine learning algorithms were used to construct intratumoral,peritumoral,and combined intratumoral and peritumoral models,respectively.Patient clinical data were analyzed by both univariate and multivariate logistic regression analy-ses to identify independent risk factors for the clinical imaging model.The performance of the models was evaluated using the receiver operating characteristic(ROC)curve.The radiomics features with the optimal diagnostic performance and the selected clinical imaging features were combined to construct a comprehensive clinical-radiomics model,and a nomogram was drawn.Results The combined intratumoral and peritumoral model was the optimal radiomics model.Maximum tumor diameter[odds ratio(OR)=1.486,P=0.014],suspicious malignant calcifications(OR=2.898,P=0.015),and axillary lymph node(ALN)metastasis(OR=3.615,P<0.001)were independent risk factors for LVI positive.Furthermore,the area under the curve(AUC)of the comprehensive clinical-radiomics model in the training set,test set and external valida-tion set was 0.889,0.916,and 0.862,respectively,which was higher than those of the combined intratumoral and peritumoral model(0.858,0.849,0.844)and the clinical imaging model(0.743,0.759,0.732).Conclusion The predictive nomogram,derived from both radiomics and clinical imaging features,is relatively accurate in identifying future LVI occurrence in breast cancer,demonstra-ting its potential as an assistive tool for clinicians to devise individualized treatment regimes.

Medical equipment, as an important indicator of smart hospital evaluation, plays a vital role in hospital operations. To ensure the safe and efficient operation of medical equipment, a reasonable performance evaluation system is indispensable. This study introduces a platform based on Internet of Things (IoT) technology that connects medical devices and collects data, achieving standardized and structured data processing, and supporting online operational supervision. Through the Delphi method, a performance evaluation system for large medical equipment is constructed, including 4 primary indicators and 22 secondary indicators. DICOM data acquisition devices are used to achieve functions such as efficiency analysis, benefit analysis, usage evaluation, and decision-making support for medical equipment. The study is still in its early stages, and in the future, it is expected to integrate more types of equipment, achieve rational resource allocation, and significantly impact decision-making for the development of public hospitals.
Internet of Things ; Delphi Technique

OBJECTIVE: Evaluate the clinical application effect of low-field infant MRI. METHODS: Using literature review, expert consultation, and two rounds of Delphi to determine the evaluation index system. Then retrospectively analyze and compare the data of low-field infant MRI and high-field MRI from January 2023 to December 2024. RESULTS: There is a certain gap between low-field infant MRI and high-field MRI in terms of signal-to-noise ratio, image uniformity, software system reliability, scanning time, user interface friendliness and image result consistency. However, there was no difference in terms of spatial resolution and image quality. The noise, hardware system reliability, mean time between failure and the rate of examination completed without sedation are better than that of high-field MRI. CONCLUSION Low-field infant MRI meets needs of clinical diagnostic and has stable performance. It can be used as a routine screening tool for brain diseases near the bed.
Magnetic Resonance Imaging/methods* ; Humans ; Infant ; Retrospective Studies ; Signal-To-Noise Ratio ; Reproducibility of Results ; Brain Diseases/diagnostic imaging* ; Brain/diagnostic imaging* ; Software

Objective To predict the lymphovascular invasion(LVI)status of breast cancer patients based on digital breast tomo-synthesis(DBT)intratumoral and peritumoral radiomics nomogram.Methods A total of 192 breast cancer patients from 2 institu-tions were retrospectively selected,in which institution 1 was used for train(n=113)and test(n=49),while institution 2 was used for external validation(n=30).Radiomics features were extracted and selected based on intratumoral and peritumoral 1 mm regions from DBT images.Different machine learning algorithms were used to construct intratumoral,peritumoral,and combined intratumoral and peritumoral models,respectively.Patient clinical data were analyzed by both univariate and multivariate logistic regression analy-ses to identify independent risk factors for the clinical imaging model.The performance of the models was evaluated using the receiver operating characteristic(ROC)curve.The radiomics features with the optimal diagnostic performance and the selected clinical imaging features were combined to construct a comprehensive clinical-radiomics model,and a nomogram was drawn.Results The combined intratumoral and peritumoral model was the optimal radiomics model.Maximum tumor diameter[odds ratio(OR)=1.486,P=0.014],suspicious malignant calcifications(OR=2.898,P=0.015),and axillary lymph node(ALN)metastasis(OR=3.615,P<0.001)were independent risk factors for LVI positive.Furthermore,the area under the curve(AUC)of the comprehensive clinical-radiomics model in the training set,test set and external valida-tion set was 0.889,0.916,and 0.862,respectively,which was higher than those of the combined intratumoral and peritumoral model(0.858,0.849,0.844)and the clinical imaging model(0.743,0.759,0.732).Conclusion The predictive nomogram,derived from both radiomics and clinical imaging features,is relatively accurate in identifying future LVI occurrence in breast cancer,demonstra-ting its potential as an assistive tool for clinicians to devise individualized treatment regimes.

Objective To establish an optimal limiting dose for dose limiting rings placed in the anterior and posterior regions of the neck for reducing head and neck lymphedema under intensity-modulated radiation therapy(IMRT)for early nasopharyngeal carcinoma(NPC)from a dosimetric perspective.Method Fifteen newly diagnosed early-stage nasopharyngeal carcinoma patients who underwent CT localization for radiotherapy at the Cancer Hospital of Guangzhou Medical University from January to September 2022 were included in the study.Each case was designed with five sets of radiotherapy plans.Plan A consisted of conventional unlimited-field plans,while Plans B-E consisted of limited-field plans with dose constraints set at 20,18,16,and 14 Gy,respectively,with the remaining parameters consistent with Plan A.The impact on target coverage and organ-at-risk constraints was evaluated through variance analysis and pairwise multiple comparisons using a randomized block design to determine the optimal dose limits.Results The gradient of 16Gy was determined as the optimal dose limiting cutoff point for achieving the balance between target coverage and organ limiting dose.Compared with the conventional plan,The plans with the placement of a cervical anterior and posterior dose limiting ring(16Gy)did not change the target dose coverage(P>0.05),but only yielded a slight change in the homogeneity index(P<0.05).It did not cause any changes of the dosage in the inner ear,mandible,and brainstem(all P>0.05),but lead to statisti-cally significant reductions in the oral cavity,throat,and thyroid(all P<0.05).It caused a slight increase of the dose in the parotid gland and spinal cord(both P<0.05),but the increased dose was anyhow within the tolerance range.Conclusion The dosimetric investigation determines an optimal dose limit cutoff point for the cervical ante-rior and posterior dose limiting rings.It is expected to provide a design method for IMRT plans to reduce head and neck lymphedema after radiotherapy for early NPC.

Objective:To explore the differences in the clinical application of different brands of infrared ear thermometers in pediatric patients,and to guide the rational use of infrared ear thermometers.Methods:A total of 164 inpatients and outpatients who received treatment in the Children's Hospital of Zhejiang University School of Medicine from May 2023 to July 2023 were randomly selected,and three different brands of ear thermometers were used to measure the ear temperature of the children in the same measurement method.The differences in temperature and time measured by different brands of ear thermometers in children of different ages were compared.Results:The temperature and time measurements of the three ear thermometers were compared,the difference was statistically significant(F=5.007,264.988,P<0.05).The temperature of children aged≤3 months was measured by the three ear thermometers,and the difference was statistically significant(F=3.868,P<0.05).there was no difference in children aged over 3 months.Conclusion:For children of different ages,medical institutions can make choices based on the comprehensive consideration of the use cost,the speed of measurement time and other factors.For children aged over 3 months,choose product B with lower cost and faster measurement time,for children aged 0-3 months,it is recommended to choose product A with more accurate temperature measurement values.

Based on the Kraljic model,this paper established the scoring standard system for medical equipment procurement,and determined the weight coefficients of each evaluation factor by the analytic hierarchy process (AHP),then the procurement classification model of medical equipment was successfully established.It is proved that this model can classify medical equipment effectively,which enables the purchasing personnel to adopt corresponding purchasing strategies according to different classification.This model can be extended to all medical equipment procurement,realizing the delicacy and individualized procurement management of the equipment,which has strong operability and guiding significance.
Equipment and Supplies ; economics ; Models, Theoretical

Objective The article aims at survey and analysis on ventilator alarm status in pediatric hospital. Methods First,we design the statistical table of ventilator, and analyze the reliability of statistical table. Then we evaluate the effectiveness of the alarm. Finally, we evaluate the alarm information synthetically through investigation and statistics.Results The ventilator alarm is ineffective, a large number of nonsensical alarms, leading to lower alert sensitivity of medical staff. Conclusion According to the survey results, we should analyze the cause of the problem, and propose the corresponding alarm management method.

The content related to the quality during life-cycle in operation of medical device includes daily use, repair volume, preventive maintenance, quality control and adverse event monitoring. In view of this, the article aims at discussion on the quality evaluation method of medical devices during their life cycle in operation based on the Analytic Hierarchy Process (AHP). The presented method is proved to be effective by evaluating patient monitors as example. The method presented in can promote and guide the device quality control work, and it can provide valuable inputs to decisions about purchase of new device.
Decision Support Techniques ; Durable Medical Equipment ; standards ; Equipment Design ; Quality Control

OBJECTIVETo introduce algorithms for effective segmentation of teeth MRI-UTE image.

METHODSTo construct second-segmentation algorithm process based on layer-dependent multi-constrained method. Firstly, a level set method was used to segment the initial boundary from the region determined by user in the reference slice. Secondly, both crown and root of the tooth were segmented by the improved level set method which took the information of the former layer's result as constraint conditions. Finally, the improved level set based on the information of the former and later layer's results was executed for the second time to improve the accuracy of segmentation, in which, the parameter of the overlapping ratio was considered.

RESULTSThe accuracy was 86.98% for the first-segmentation and was increased to 88.35% for the second-segmentation. Compared to the two other methods, the accuracy of the algorithms provided was improved significantly (P < 0.05).

CONCLUSIONSThe proposed algorithms can effectively achieve the segmentation of teeth MRI-UTE image and has a great improvement on accuracy.