Ultrasound-guided vacuum-assisted excision for idiopathic granulomatous mastitis at the abscess stage.

BACKGROUND: Idiopathic granulomatous mastitis (IGM) is a rare inflammatory disease of the breast with clinical features that are often confused with those of breast cancer leading to delayed diagnosis and treatment. This retrospective study aimed to evaluate the therapeutic effectiveness and cosmetic results of drainage surgery using ultrasound-guided vacuum-assisted excision (VAE) for the treatment of IGM at the abscess stage. METHODS: The time of recovery, cases of further surgical intervention, and cosmetic results were retrospectively collected and analyzed from patients who underwent drainage with VAE or conventional drainage between October 2017 and August 2021. RESULTS: A total of 65 patients diagnosed with IGM at the abscess stage who underwent drainage surgery with VAE or conventional drainage surgery were enrolled. Overall, 38 (58.5%) underwent conventional drainage surgery and 27 (41.5%) underwent drainage with VAE. We found that patients who underwent VAE recovered much faster than those who underwent traditional drainage surgery (30.1 vs. 48.0 days). Nine (33.3%) patients in the VAE group required further surgical intervention after drainage, whereas 33 (86.8%) patients in the control group underwent another surgery to resect residual lesions. Additionally, patients in the VAE group were more satisfied with the breast appearance, mainly due to less influence of the scars and better symmetry of their breasts. CONCLUSION: Compared with conventional drainage surgery, drainage surgery using VAE for IGM patients at the abscess stage improved therapeutic and cosmetic outcomes. Furthermore, postoperative management of IGM is crucial.

Criteria derived from serum markers can precisely evaluate axillary status in breast cancer patients.

BACKGROUND: A noninvasive method to confirm the presence of lymph node metastases (LNM) in breast cancer patients is lacking. This study aimed to identify markers from peripheral blood that have diagnostic value in evaluating axillary LNM. METHODS: We tested 26 factors in serum from 57 patients with resectable breast cancer by the Luminex assay. Differences between node-negative and node-positive patients were assessed. The diagnostic value of the factors was determined by further analyses and a validation test. RESULTS: Matrix metalloproteinase-1, hepatocyte growth factor, and chemokine ligand 5 were independent risk factors for LNM. However, receiver operating characteristic analysis showed that these factors alone were not ideal predictors. The LNM score (LNMS), derived from combining these markers, correlated significantly with numbers of positive lymph nodes. Patients with LNMS of 0 had few LNM, axillary lymph node dissection (ALND) could be avoided, and sentinel lymph node biopsy (SLNB) was unnecessary. Very high accuracy was achieved for patients with LNMS of 1 with SLNB using only methylene blue, patients with LNMS of 3 required ALND, and patients with LNMS of 2 needed SLNB using both a radioactive isotope and methylene blue, and ALND. CONCLUSIONS: The LNMS derived from matrix metalloproteinase-1, hepatocyte growth factor, and chemokine ligand 5 serum levels identified the axillary lymph node status with high accuracy. Patients with higher LNMS had a greater probability of LNM.

Immune pathogenesis of idiopathic granulomatous mastitis: from etiology toward therapeutic approaches.

Idiopathic granulomatous mastitis (IGM) is a noncancerous, chronic inflammatory disorder of breast with unknown causes, posing significant challenges to the quality of life due to its high refractoriness and local aggressiveness. The typical symptoms of this disease involve skin redness, a firm and tender breast mass and mastalgia; others may include swelling, fistula, abscess (often without fever), nipple retraction, and peau d'orange appearance. IGM often mimics breast abscesses or malignancies, particularly inflammatory breast cancer, and is characterized by absent standardized treatment options, inconsistent patient response and unknown mechanism. Definite diagnosis of this disease relies on core needle biopsy and histopathological examination. The prevailing etiological theory suggests that IGM is an autoimmune disease, as some patients respond well to steroid treatment. Additionally, the presence of concurrent erythema nodosum or other autoimmune conditions supports the autoimmune nature of the disease. Based on current knowledge, this review aims to elucidate the autoimmune-favored features of IGM and explore its potential etiologies. Furthermore, we discuss the immune-mediated pathogenesis of IGM using existing research and propose immunotherapeutic strategies for managing this condition.

Evaluation of machine learning algorithms for the prognosis of breast cancer from the Surveillance, Epidemiology, and End Results database.

INTRODUCTION: Many researchers used machine learning (ML) to predict the prognosis of breast cancer (BC) patients and noticed that the ML model had good individualized prediction performance. OBJECTIVE: The cohort study was intended to establish a reliable data analysis model by comparing the performance of 10 common ML algorithms and the the traditional American Joint Committee on Cancer (AJCC) stage, and used this model in Web application development to provide a good individualized prediction for others. METHODS: This study included 63145 BC patients from the Surveillance, Epidemiology, and End Results database. RESULTS: Through the performance of the 10 ML algorithms and 7th AJCC stage in the optimal test set, we found that in terms of 5-year overall survival, multivariate adaptive regression splines (MARS) had the highest area under the curve (AUC) value (0.831) and F1-score (0.608), and both sensitivity (0.737) and specificity (0.772) were relatively high. Besides, MARS showed a highest AUC value (0.831, 95%confidence interval: 0.820-0.842) in comparison to the other ML algorithms and 7th AJCC stage (all P < 0.05). MARS, the best performing model, was selected for web application development (https://w12251393.shinyapps.io/app2/). CONCLUSIONS: The comparative study of multiple forecasting models utilizing a large data noted that MARS based model achieved a much better performance compared to other ML algorithms and 7th AJCC stage in individualized estimation of survival of BC patients, which was very likely to be the next step towards precision medicine.

Bioimpedance spectroscopy can precisely discriminate human breast carcinoma from benign tumors.

Intraoperative frozen pathology is critical when a breast tumor is not diagnosed before surgery. However, frozen tumor tissues always present various microscopic morphologies, leading to a high misdiagnose rate from frozen section examination. Thus, we aimed to identify breast tumors using bioimpedance spectroscopy (BIS), a technology that measures the tissues' impedance. We collected and measured 976 specimens from breast patients during surgery, including 581 breast cancers, 190 benign tumors, and 205 normal mammary gland tissues. After measurement, Cole-Cole curves were generated by a bioimpedance analyzer and parameters R0/R∞, fc, and α were calculated from the curve. The Cole-Cole curves showed a trend to differentiate mammary gland, benign tumors, and cancer. However, there were some curves overlapped with other groups, showing that it is not an ideal model. Subsequent univariate analysis of R0/R∞, fc, and α showed significant differences between benign tumor and cancer. However, receiver operating characteristic (ROC) analysis indicated the diagnostic value of fc and R0/R∞ were not superior to frozen sections (area under curve [AUC] = 0.836 and 0.849, respectively), and α was useless in diagnosis (AUC = 0.596). After further research, we found a scatter diagram that showed a synergistic effect of the R0/R∞ and fc, in discriminating cancer from benign tumors. Thus, we used multivariate analysis, which revealed that these two parameters were independent predictors, to combine them. A simplified equation, RF = 0.2fc + 3.6R0/R∞, based on multivariate analysis was developed. The ROC curve for RF' showed an AUC = 0.939, and the sensitivity and specificity were 82.62% and 95.79%, respectively. To match a clinical setting, the diagnostic criteria were set at 6.91 and 12.9 for negative and positive diagnosis, respectively. In conclusion, RF' derived from BIS can discriminate benign tumor and cancers, and integrated criteria were developed for diagnosis.