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Objective To explore the clinical efficacy of staged surgery for non-lactating mastitis. Methods A retrospective analysis was conducted on 317 patients with non-lactating mastitis admitted to our department from January 2015 to December 2020, all of whom underwent staged surgical treatment. The recovery time, recurrence rate, and breast appearance score were observed. Results The median follow-up time was 24(17，33) months, the recovery time was (25.5±17.9) days, and the recurrence rate was 4.4%. There were 96.2% of patients satisfied with the breast appearance. Conclusions Staged surgery for non-lactating mastitis can effectively shorten the course of the disease, protect the appearance of breast, and have good clinical efficacy.

Abstract Objectives This study aims to build a machine learning (ML) model to predict the recurrence probability for postoperative non-lactating mastitis (NLM) by Random Forest (RF) and XGBoost algorithms. It can provide the ability to identify the risk of NLM recurrence and guidance in clinical treatment plan. Methods This study was conducted on inpatients who were admitted to the Mammary Department of Shuguang Hospital affiliated to Shanghai University of Traditional Chinese Medicine between July 2019 to December 2021. Inpatient data follow-up has been completed until December 2022. Ten features were selected in this study to build the ML model: age, body mass index (BMI), number of abortions, presence of inverted nipples, extent of breast mass, white blood cell count (WBC), neutrophil to lymphocyte ratio (NLR), albumin-globulin ratio (AGR) and triglyceride (TG) and presence of intraoperative discharge. We used two ML approaches (RF and XGBoost) to build models and predict the NLM recurrence risk of female patients. Totally 258 patients were randomly divided into a training set and a test set according to a 75%-25% proportion. The model performance was evaluated based on Accuracy, Precision, Recall, F1-score and AUC. The Shapley Additive Explanations (SHAP) method was used to interpret the model. Results There were 48 (18.6%) NLM patients who experienced recurrence during the follow-up period. Ten features were selected in this study to build the ML model. For the RF model, BMI is the most important influence factor and for the XGBoost model is intraoperative discharge. The results of tenfold cross-validation suggest that both the RF model and the XGBoost model have good predictive performance, but the XGBoost model has a better performance than the RF model in our study. The trends of SHAP values of all features in our models are consistent with the trends of these features’ clinical presentation. The inclusion of these ten features in the model is necessary to build practical prediction models for recurrence. Conclusions The results of tenfold cross-validation and SHAP values suggest that the models have predictive ability. The trend of SHAP value provides auxiliary validation in our models and makes it have more clinical significance.

Objectives: This study aims to build a machine learning (ML) model to predict the recurrence probability for postoperative non-lactating mastitis (NLM) by Random Forest (RF) and XGBoost algorithms. It can provide the ability to identify the risk of NLM recurrence and guidance in clinical treatment plan. Methods: This study was conducted on inpatients who were admitted to the Mammary Department of Shuguang Hospital affiliated to Shanghai University of Traditional Chinese Medicine between July 2019 to December 2021. Inpatient data follow-up has been completed until December 2022. Ten features were selected in this study to build the ML model: age, body mass index (BMI), number of abortions, presence of inverted nipples, extent of breast mass, white blood cell count (WBC), neutrophil to lymphocyte ratio (NLR), albumin-globulin ratio (AGR) and triglyceride (TG) and presence of intraoperative discharge. We used two ML approaches (RF and XGBoost) to build models and predict the NLM recurrence risk of female patients. Totally 258 patients were randomly divided into a training set and a test set according to a 75%-25% proportion. The model performance was evaluated based on Accuracy, Precision, Recall, F1-score and AUC. The Shapley Additive Explanations (SHAP) method was used to interpret the model. Results: There were 48 (18.6%) NLM patients who experienced recurrence during the follow-up period. Ten features were selected in this study to build the ML model. For the RF model, BMI is the most important influence factor and for the XGBoost model is intraoperative discharge. The results of tenfold cross-validation suggest that both the RF model and the XGBoost model have good predictive performance, but the XGBoost model has a better performance than the RF model in our study. The trends of SHAP values of all features in our models are consistent with the trends of these features' clinical presentation. The inclusion of these ten features in the model is necessary to build practical prediction models for recurrence. Conclusions: The results of tenfold cross-validation and SHAP values suggest that the models have predictive ability. The trend of SHAP value provides auxiliary validation in our models and makes it have more clinical significance. [ABSTRACT FROM AUTHOR]

The article analyzes the results of a microbiological examination of 336 patients with various forms of non-lactating mastitis. The causative agent was identified in 321 (95.5%) patients. Monoculture was identified in the majority of observations (84.4%), microbial associations were found in 15.6% of cases. Staphylococcus aureus had prevailed as the causative agent (69.7% of cases). Microflora composition depended on the form of the disease: S.aureus was isolated from the samples in 86.1% of cases of acute form of non-lactating mastitis, in 69.8% of cases of subacute form, and in 57.4% of patients with chronic form of non-lactating mastitis. The role of gram-negative microorganisms and microbial association is constantly evolving. The frequency of isolation of multiple antibioticresistant strains of microorganisms depended on the duration of the disease, the previous intake of antibacterial drugs and the clinical course of the disease. In acute forms of non-lactating mastitis methicillin-resistant S.aureus (MRSA) was isolated in only 2% of cases among all isolated S.aureus, while in subacute and chronic forms of the disease the frequency of MRSA isolation dramatically increased to 11.7% and 23.4% of all strains of staphylococci, respectively.Microbiological monitoring of wound fluid in the treatment of patients with non-lactating mastitis in combination with rational antibiotic therapy and adequate surgical intervention is the key to successful treatment of this category of patients.

Abstract Objective To describe a minimally invasive comprehensive treatment for granulomatous lobular mastitis (GLM) and compare its effect with the existing methods, particularly in terms of its recurrence rate and esthetic outcomes. Methods This retrospective study reviewed 69 GLM patients receiving the minimally invasive comprehensive treatment. Patients’ information, including age, clinical features, image characteristics, histopathological findings, mastitis history, treatment process, operative technique, recurrence, and esthetic effect, was evaluated. Results All patients were female with a median age of 32 (range 17–55) years. Hospital stays ranged from 2 to 34 days, with a median of 6 days. The shortest time for complete rehabilitation was 2 days and the longest time was 365 days, with a median of 30 days. After a median follow-up of 391 days (range 162–690), 7 patients (10.14%) relapsed. The average cosmetic score was 2.62 ± 0.57 points and was mainly related to the past treatment, especially the surgical history. Conclusion Minimally invasive comprehensive treatment is a new method for the treatment of GLM, ensuring a therapeutic effect while maintaining breast beauty.

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Objective: To describe a minimally invasive comprehensive treatment for granulomatous lobular mastitis (GLM) and compare its effect with the existing methods, particularly in terms of its recurrence rate and esthetic outcomes.Methods: This retrospective study reviewed 69 GLM patients receiving the minimally invasive comprehensive treatment. Patients' information, including age, clinical features, image characteristics, histopathological findings, mastitis history, treatment process, operative technique, recurrence, and esthetic effect, was evaluated.Results: All patients were female with a median age of 32 (range 17-55) years. Hospital stays ranged from 2 to 34 days, with a median of 6 days. The shortest time for complete rehabilitation was 2 days and the longest time was 365 days, with a median of 30 days. After a median follow-up of 391 days (range 162-690), 7 patients (10.14%) relapsed. The average cosmetic score was 2.62 ± 0.57 points and was mainly related to the past treatment, especially the surgical history.Conclusion: Minimally invasive comprehensive treatment is a new method for the treatment of GLM, ensuring a therapeutic effect while maintaining breast beauty. [ABSTRACT FROM AUTHOR]