Objective To summarize the evaluation methods of postoperative healing of supraspinatus tendon tear in recent years, in order to provide reference for clinic. Methods CNKI, Wanfang, PubMed, and Foreign Medical Literature Retrieval Service (FMRS) databases were used to search the literatures between 2005 and 2022. The literature related to the diagnosis and postoperative healing of supraspinatus tendon tear was included. Finally, 50 articles were reviewed. ResultsSupraspinatus tendon tear is a common shoulder disease. Physical examination, clinical score, and imaging examination are used to predict and evaluate the postoperative healing. Among them, physical examination and clinical score are non-invasive and the most economical methods, but their accuracy and sensitivity are lower than imaging examination, so they can only be used as auxiliary methods. The acromio-humeral distance (AHD) and upward migration index (UMI) measured by X-ray films can directly reflect the change of supraspinatus tendon thickness, but they are impossible to distinguish whether there is tear or not. Ultrasound and MRI are the main methods for the clinical diagnosis of supraspinatus tendon tear, but the commonly used MRI sequence can not accurately judge the internal healing of the tendon. Shear wave elastrography (SWE) and ultrashort-echo-time (UTE) techniques are the latest research directions in recent years, but different studies have shown opposite conclusions on the application of SWE technique. This conclusion shows that the principle of SWE technique and its relationship with tendons need to be further studied. UTE technique has good clinical effect, and the T2* value obtained by UTE technique is more accurate than that of traditional Sugaya typing, but there are still few research samples. Conclusion AHD and UMI measured by X-ray film and T2* value measured by UTE technique can be used as effective methods for evaluating the healing of supraspinatus tendon tear after repairing, and can be used as a follow-up evaluation method combined with physical examination and clinical score for patients with supraspinatus tendon tear.
Objective To analyze the correlation among the clinicopathologic features, ultrasound imaging features, and axillary lymph node metastasis in breast cancer patients with negative clinical evaluation of axillary lymph nodes (cN0), and to establish a logistic regression model to predict axillary lymph node metastasis, so as to provide a reference for more accurate evaluation of axillary lymph node status in cN0 breast cancer patients. Methods The data of 501 female patients with cN0 breast cancer who were hospitalized and operated in the Affiliated Hospital of Wuhan University of Science and Technology (Xiaogan Central Hospital) from December 2013 to October 2020 were collected. Among them, 376 patients from December 2013 to December 2019 were selected to establish a prediction model for axillary lymph node metastasis of cN0 breast cancer. In the modeling group, the basic information, clinical pathological characteristics, and ultrasound imaging features of patients were analyzed by single factor analysis. The factors with statistical significance were included in the multivariate logistic regression analysis, and the logistic regression prediction model was established. The model was evaluated by the correction curve and Hosmer-Lemeshow test goodness of fit. The model was validated in the validation group (125 patients from January to October 2020), and the receiver operation characteristic (ROC) curve was drawn. Results The probability of positive axillary lymph nodes in 501 patients with cN0 breast cancer was 28.14% (141/501). The univariate analysis results of the modeling group showed that the histological grade, vascular invasion, progesterone receptor (PR), Ki-67, age, molecular typing, ultrasound breast imaging-reporting and data system (BI-RADS) grade were associated with axillary lymph node metastasis. Multivariate logistic regression analysis showed that the vascular infiltration, positive estrogen receptor (ER) , ultrasound BI-RADS grade 4C and Ki-67≥14% increased the probability of axillary lymph node metastasis (P<0.05). Using the above prediction factors to establish the prediction nomogram, the area under the ROC curve (AUC) of the modeling group was 0.72 [95%CI (0.66, 0.78)], the cut-off value was 0.30, the sensitivity was 61.00%, and the specificity was 71.20%. The newly established axillary lymph node transfer logistic regression model was applied to the validation group (n=125), and the AUC was 0.72 [95%CI (0.53, 0.76)]. The truncation value was 0.40, and the total coincidence rate was 69.60% (87/125), positive predictive value was 47.37% (18/38), and negative predictive value was 91.95% (80/87). Conclusions Vascular invasion, positive ER , ultrasound BI-RADS grade 4C, and Ki-67≥14% are risk predictors of axillary lymph node metastasis in cN0 breast cancer patients. The negative predictive value of the model is 91.95%, which has a higher value in predicting axillary lymph node metastasis in early breast cancer patients, and can provide a reference for screening exempt sentinel lymph node biopsy population.