Objective:To explore the value of nomogram model based on multimodal ultrasound features for predicting the malignant risk of micro lesions in breast areola region.Methods:The case data of Beijing Tiantan Hospital affiliated to Capital Medical University from May 2020 to July 2024 were retrospectively analyzed. A total of 50 patients with benign intraductal papilloma（bIDP group）and 54 patients with malignant risk breast tumor（mrBT group）were found to have micro lesions in breast areola region and confirmed by puncture or surgical pathology. Clinical data，conventional ultrasound and contrast-enhanced ultrasound features were compared between the two groups. Multivariate Logistic regression analysis and Lasso regression analysis were performed on statistically significant factors to screen out influencing factors. ROC curves were plotted to evaluate diagnostic efficacy，nomogram model and clinical decision curves were constructed to evaluate clinical benefits.Results:The differences of age，nipple discharge presentation，conventional ultrasound features（including boundary，morphology，aspect ratio，internal echo，internal microcalcification，far-field echo，peripheral irregular hyperechoic ring，dilate of peripheral ducts），and contrast-enhanced ultrasound features（including wash-in time，enhancement intensity，enhancement mode，enhancement scope，blood perfusion defect，crab foot sign，penetrating vessels）were statistically significant between the bIDP group and mrBTgroup（all P<0.05）. Regression analysis showed that age，uniformity of internal echo within the lesion，dilation of surrounding ducts，and enhanced crab foot sign were the affect factors for the diagnosis of mrBT（all P<0.05）. Based on these factors，a nomogram model was constructed with an area under ROC curve（AUC）of 0.907（95% CI=0.851-0.963），a sensitivity of 0.907，and a specificity of 0.780. The decision curve analysis showed that the collective model had good predictive performance. Conclusions:The nomogram model based on multimodal ultrasound features has good value in predicting malignant risk micro breast tumor of areola region.

Objective:To establish a model based on the characteristics of breast cancer ultrasound images through deep learning methods to predict the risk of axillary lymph node metastasis（ALNM）in patients with breast cancer（maximum diameter ≤2.5 cm）before surgery.Methods:A total of 419 patients（3 433 breast tumor ultrasound images）with breast cancer（maximum diameter ≤2.5 cm）who underwent axillary lymph node dissection at Beijing Tiantan Hospital，Capital Medical University from January 2019 to December 2024 were retrospectively included. According to the pathological results of axillary lymph nodes，they were divided into 220 cases in the ALNM occurrence group（positive group）and 199 cases in the non-ALNM occurrence group（negative group）. The breast cancer ultrasound images of the two groups of cases were randomly classified into the training set（2 404 images），the validation set（687 images）and the test set（342 images）according to a ratio of 7∶2∶1. YOLOv8 was used as the basic model of You Only Look Once（YOLO）and optimized. The optimized model was applied to locate and capture the potential ultrasound features of breast cancer cases in the training set. A prediction model was constructed based on the captured ultrasound features. The model was adjusted and optimized through the validation set，and then matched with the case images in the test set. The confusion classification matrix graph and the curve graph for measuring the model performance were used to evaluate the model prediction performance and interpret the model，and the efficacy of this model in identifying breast cancer patients at risk of ALNM was analyzed.Results:There were statistically significant differences between the positive and negative groups in terms of the pathological maximum diameter of breast tumors，pathological T staging，the differentiation degree，the presence of distant metastasis，the maximum diameter measured by ultrasound，the quadrant of breast tumor occurrence，the Breast Imaging - Reporting and Data System（BI-RADS）classification of breast tumors，and the presence of abnormal ultrasound features of lymph node（all P<0.05）. The established deep learning model could automatically perform bounding box localization for the breast cancer of patients.The breast tumors in the positive group had potential ultrasound features that could be captured by the model compared with those in the negative group. The mean average precision（mAP）50 was 0.883，mAP 50-95 was 0.636，PR-AUC was 0.884 5，strict PR-AUC was 0.636 4，the sensitivity was 90.5%，and the specificity was 91.2%，and it had a good predictive efficacy. Conclusions:This prediction model based on the ultrasound characteristics of breast cancer through deep learning can effectively predict breast cancer（maximum diameter ≤ 2.5 cm）with the risk of ALNM，providing an effective basis for the clinical management of axillary lymph nodes in breast cancer patients.

Objective:To explore the value of nomogram model based on multimodal ultrasound features for predicting the malignant risk of micro lesions in breast areola region.Methods:The case data of Beijing Tiantan Hospital affiliated to Capital Medical University from May 2020 to July 2024 were retrospectively analyzed. A total of 50 patients with benign intraductal papilloma（bIDP group）and 54 patients with malignant risk breast tumor（mrBT group）were found to have micro lesions in breast areola region and confirmed by puncture or surgical pathology. Clinical data，conventional ultrasound and contrast-enhanced ultrasound features were compared between the two groups. Multivariate Logistic regression analysis and Lasso regression analysis were performed on statistically significant factors to screen out influencing factors. ROC curves were plotted to evaluate diagnostic efficacy，nomogram model and clinical decision curves were constructed to evaluate clinical benefits.Results:The differences of age，nipple discharge presentation，conventional ultrasound features（including boundary，morphology，aspect ratio，internal echo，internal microcalcification，far-field echo，peripheral irregular hyperechoic ring，dilate of peripheral ducts），and contrast-enhanced ultrasound features（including wash-in time，enhancement intensity，enhancement mode，enhancement scope，blood perfusion defect，crab foot sign，penetrating vessels）were statistically significant between the bIDP group and mrBTgroup（all P<0.05）. Regression analysis showed that age，uniformity of internal echo within the lesion，dilation of surrounding ducts，and enhanced crab foot sign were the affect factors for the diagnosis of mrBT（all P<0.05）. Based on these factors，a nomogram model was constructed with an area under ROC curve（AUC）of 0.907（95% CI=0.851-0.963），a sensitivity of 0.907，and a specificity of 0.780. The decision curve analysis showed that the collective model had good predictive performance. Conclusions:The nomogram model based on multimodal ultrasound features has good value in predicting malignant risk micro breast tumor of areola region.

Objective:To establish a model based on the characteristics of breast cancer ultrasound images through deep learning methods to predict the risk of axillary lymph node metastasis（ALNM）in patients with breast cancer（maximum diameter ≤2.5 cm）before surgery.Methods:A total of 419 patients（3 433 breast tumor ultrasound images）with breast cancer（maximum diameter ≤2.5 cm）who underwent axillary lymph node dissection at Beijing Tiantan Hospital，Capital Medical University from January 2019 to December 2024 were retrospectively included. According to the pathological results of axillary lymph nodes，they were divided into 220 cases in the ALNM occurrence group（positive group）and 199 cases in the non-ALNM occurrence group（negative group）. The breast cancer ultrasound images of the two groups of cases were randomly classified into the training set（2 404 images），the validation set（687 images）and the test set（342 images）according to a ratio of 7∶2∶1. YOLOv8 was used as the basic model of You Only Look Once（YOLO）and optimized. The optimized model was applied to locate and capture the potential ultrasound features of breast cancer cases in the training set. A prediction model was constructed based on the captured ultrasound features. The model was adjusted and optimized through the validation set，and then matched with the case images in the test set. The confusion classification matrix graph and the curve graph for measuring the model performance were used to evaluate the model prediction performance and interpret the model，and the efficacy of this model in identifying breast cancer patients at risk of ALNM was analyzed.Results:There were statistically significant differences between the positive and negative groups in terms of the pathological maximum diameter of breast tumors，pathological T staging，the differentiation degree，the presence of distant metastasis，the maximum diameter measured by ultrasound，the quadrant of breast tumor occurrence，the Breast Imaging - Reporting and Data System（BI-RADS）classification of breast tumors，and the presence of abnormal ultrasound features of lymph node（all P<0.05）. The established deep learning model could automatically perform bounding box localization for the breast cancer of patients.The breast tumors in the positive group had potential ultrasound features that could be captured by the model compared with those in the negative group. The mean average precision（mAP）50 was 0.883，mAP 50-95 was 0.636，PR-AUC was 0.884 5，strict PR-AUC was 0.636 4，the sensitivity was 90.5%，and the specificity was 91.2%，and it had a good predictive efficacy. Conclusions:This prediction model based on the ultrasound characteristics of breast cancer through deep learning can effectively predict breast cancer（maximum diameter ≤ 2.5 cm）with the risk of ALNM，providing an effective basis for the clinical management of axillary lymph nodes in breast cancer patients.

Objective:To explore the value of different levels of sonographers and International Ovarian Tumor Analysis (IOTA) simple rules in judging benign and malignant ovarian tumors.Methods:The ultrasound images of 182 patients treated in Beijing Tiantan Hospital, Capital Medical University from January 2017 to November 2020 with ovarian tumors were retrospectively analyzed. The ovarian tumors were diagnosed by two senior sonographers and two junior sonographers without knowing the pathological diagnosis. Another junior sonographer trained in IOTA terminology and simple rules applied IOTA simple rules to diagnose 182 ovarian tumors. The sensitivity, specificity, positive predictive value and negative predictive value of the diagnosis of ovarian tumors by senior sonographers, junior sonographers and IOTA simple rules were calculated using the postoperative pathological diagnosis as the gold standard. The Kappa value was calculated for the consistency between different levels of sonographers and the IOTA simple rules and pathological diagnosis.Results:Of the 182 cases, 61 cases were pathologically benign and 121 cases were pathologically malignant. The diagnostic sensitivity, specificity and accuracy of senior sonographers were 93.4%, 99.2%, 97.2%, respectively, Kappa value was 0.938. The diagnostic sensitivity, specificity, and accuracy of junior sonographers were 80.3%, 90.0%, 86.8%, respectively, Kappa value was 0.704. The diagnostic sensitivity, specificity and accuracy of IOTA simple rules(When an uncertain tumor was classified as malignant) were 95.0%, 73.5%, 80.7%, respectively, Kappa value was 0.614. The diagnostic sensitivity, specificity and accuracy of IOTA simple rules(when an uncertain tumor was excluded) were 94.2%, 90.9%, 92.0%, respectively, Kappa value was 0.834.Conclusions:IOTA simple rules is a very useful diagnostic tool for junior sonographers to judge benign and malignant ovarian tumors. When IOTA simple principle is judged as an uncertain case, it is recommended to refer to experienced senior sonographers for further diagnosis.

Objective: To discuss a method, increasing the resistance and decreasing the power of the levator palpebrae superioris, to treat the upper eyelid retraction, after upper blepharoplasty, and summarize the feasibility and efficacy of this operation. Methods: A total of 33 female patients (42 eyes) with upper eyelid retraction after blepharoplasty were treated. According to preoperative evaluation, an adjusted method, levator tendon membrane and Muller′s muscle compound tissue turnover flap, was selected. Following the incision of past blepharoplasty, scar and adhesions were removed as much as possible. The space between orbital septum and levator palpebral tendon membrane was widely separated, as well as the space between levator palpebral tendon membrane and Muller′s muscle, and the conjunctiva. A composite tissue flap consisting of levator palpebral tendon membrane and Muller′s muscle was formed. At the spot above the end of the composite tissue flap, paralleling to the upper edge of upper tarsal plate, the tissue was stripped. The compound flap was divided into two layers, a deep and a shallow layer, to form the aponeurosis turnover flap with pedicle at the free end. The turnover flap was horizontally sutured to the upper edge of tarsal plate. The buccal fat pad was cut and covered, between the levator palpebral tendon membrane and the orbital septum fat. At the end, conventional blepharoplasty was performed to close the incision. Results: All the incisions were primary healed. Stitches were taken out 7 days after surgery. There was different scar proliferation. The recovery period last 3-6 months. Transplanted buccal fat was survived, without nodule, liquefaction, unevenness or other complications. All patients were followed for 3 to 12 months, with a mean follow-up of 6 months, for static and dynamic assessment. In static evaluation, the upper palpebral margin decreased by 2 mm. The upper palpebral margin decreased by 3 mm on average. Three cases (9%) had insufficiently corrected upper eyelid retraction, 2 cases (6%) recurred upper eyelid retraction in 3 months after operation, while the other 28 cases (85%) showed satisfactory results. Conclusions The upper eyelid tendon membrane and Muller′s muscle compound tissue turnover flap extension is helpful to correct the upper eyelid retraction, caused by blepharoplasty.

By collecting and analyzing information regarding the history development, current situation, legislation, health insurance, education and academic organizations of acupuncture in Israel, the development characteristics of acupuncture in Israel were summarized. The overall traditional medicine developed well in Israel, yet acupuncture have only appeared in Israel for 18 years. The proposal of Israeli Acupuncture Legislation Act improved the development of health insurance, education and public awareness of acupuncture in Israel. However, improvement in areas of education, legislation and health insurance are still in need. Suggestions are proposed to improve the education quality, acupuncture legislation, international cooperation, volunteer activities and cultural exchanges. In addition, the legislative process of acupuncture in the countries of "the Belt and Road", especially the Middle East countries, are promoted.