[Granulomatous lobular mastitis: a clinicopathological analysis of 300 cases]

Pathological manifestations of granulomatous lobular mastitis

Granulomatous lobular mastitis (GLM) is a rare inflammatory breast disease with unknown etiology, characterized by non-caseous granulomatous inflammation of the lobules, which infiltrate lymphocytes, neutrophils, plasma cells, monocytes, and eosinophils may accompany. GLM is often misdiagnosed as breast cancer due to the lack of specificity in clinical and imaging examinations, and therefore histopathology is the main basis for confirming the diagnosis. This review provides an overview of the pathological features of granulomatous lobular mastitis and cystic neutrophil granulomatous mastitis (CNGM, a pathologic subtype of GLM). As well as pathologic manifestations of other breast diseases that need to be differentiated from granulomatous lobular mastitis such as breast tuberculosis, lymphocytic mastopathy/diabetic mastopathy, IgG4-related sclerosing mastitis (IgG4-RSM), nodular disease, Wegener's granulomatosis, and plasma cell mastitis. Besides, discusses GLM and CNGM, GLM and breast cancer, emphasizing that their relationship deserves further in-depth exploration. The pathogenesis of GLM has not yet been clearly articulated and needs to be further explored, pathology enables direct observation of the microscopic manifestations of the disease and contributes to further investigation of the pathogenesis.

Construction of a Multi-Indicator Model for Abscess Prediction in Granulomatous Lobular Mastitis Using Inflammatory Indicators

Background

Granulomatous lobular mastitis (GLM) is a chronic inflammatory breast disease, and abscess formation is a common complication of GLM. The process of abscess formation is accompanied by changes in multiple inflammatory markers. The present study aimed to construct a diagnosis model for the early of GLM abscess formation based on multiple inflammatory parameters.

Methods

Based on the presence or absence of abscess formation on breast magnetic resonance imaging (MRI), 126 patients with GLM were categorised into an abscess group (85 patients) and a non-abscess group (41 patients). Demographic characteristics and the related laboratory results for the 9 inflammatory markers were collected. Logistics univariate analysis and collinearity test were used for selecting independent variables. A regression model to predict abscess formation was constructed using Logistics multivariate analysis.

Results

The univariate and multivariate analysis showed that the N, ESR, IL-4, IL-10 and INF-α were independent diagnostic factors of abscess formation in GLM (P<0. 05). The nomogram was drawn on the basis of the logistics regression model. The area under the curve (AUC) of the model was 0.890, which was significantly better than that of a single indicator and the sensitivity and specificity of the model were high (81.2% and 85.40%, respectively). These results predicted by the model were highly consistent with the actual diagnostic results. The results of this calibration curve indicated that the model had a good value and stability in predicting abscess formation in GLM. The decision curve analysis (DCA) demonstrated a satisfactory positive net benefit of the model.

Conclusion

A predictive model for abscess formation in GLM based on inflammatory markers was constructed in our study, which may provide a new strategy for early diagnosis and treatment of the abscess stage of GLM.

Differentiating non-lactating mastitis and malignant breast tumors by deep-learning based AI automatic classification system: A preliminary study

Objective

To explore the application values of deep-learning based artificial intelligence (AI) automatic classification system, on the differential diagnosis of non-lactating mastitis (NLM) and malignant breast tumors, via its comparation with traditional ultrasound interpretations and the following interpretation conclusions made by the sonographers with various seniorities.

Methods

A total of 707 patients suffering from breast lesions (475 malignant breast tumors and 232 NLM), were selected from the following three medical centers, including Zhejiang Cancer Hospital, Hebei Province Hospital of Traditional Chinese Medicine, and Yantai Affiliated Hospital of Binzhou Medical University, and the time period was set from April 2020 to September 2021. All selected cases firstly accepted the routine breast ultrasound diagnosis, followed by the interpretations from a senior sonographer with more than 15 years of work experience, and an intermediate-aged sonographer with more than 5 years of work experience, independently. Meanwhile, a third physician also interpreted the same ultrasound images by deep learning–based AI automatic classification system, independent of the interpretation results from the previous two physicians. The kappa test was performed to evaluate the consistency between the conventional ultrasound interpretation results and pathological results interpreted from physicians with different working experiences.

Results

In total, 475 cases of malignant breast tumors (512 nodules) and 232 cases of NLM (255 nodules) were pathologically diagnosed. The accuracy, sensitivity, and specificity of conventional ultrasound interpretations vary from different sonographers with different working experiences. The accuracy, sensitivity, and specificity for intermediate-aged sonographers and senior sonographers were 76.92% (590/767), 84.71% (216/255), and 73.95% (374/512) and 87.35% (670/767), 86.27% (220/255), and 87.89% (450/512), respectively (P<0.001). In contrast, if the threshold was set as 0.5, the accuracy, sensitivity, and specificity from deep learning–based AI automatic classification system were 83.00%, 87.20%, and 85.33%, separately, and the area under the curve was 92.6. The results of the kappa consistency test indicated that the diagnosis results from the image interpretations by senior physicians and deep-learning based AI automatic classification system showed high consistency with postoperative pathological diagnosis results, and the kappa values are 0.72 and 0.71, respectively, with the P-value of less than 0.001. In contrast, the consistency between the image interpretation results from intermediate-aged physicians with less working experience, and postoperative pathological diagnosis results, seemed to be relatively lower, with a kappa value of only 0.53 and P-value of less than 0.001.

Conclusions

The deep learning–based AI automatic classification system is expected to become a reliable auxiliary way to distinguish NLM and malignant breast tumors due to its high sensitivity, accuracy, and specificity.

A clinical observation of stage I implant breast reconstruction for mass-like granulomatous lobular mastitis

Background

Granulomatous lobular mastitis (GLM) is a chronic benign inflammatory breast disease, and mainly mass-like granulomatous lobular mastitis (MGLM) clinically. There are few reports on applications of stage I implant breast reconstruction in GLM. This observational study was conducted to evaluate the safety and efficacy of stage I implant breast reconstruction in the treatment of MGLM.

Methods

Patients suffering from MGLM who visited at hospital from April 2019 to June 2020 were selected and graded according to the magnetic resonance imaging (MRI) examination. Patients with MGLM were grouped into the prosthesis implantation group and the traditional treatment group according to their preferences. Clinical parameters of the two groups were analyzed before and after surgery, such as postoperative infection, recurrence, and satisfaction with appearance and aesthetics were observed. To evaluate the safety and efficacy of the implant breast reconstruction in MGLM.

Results

There were 59 cases of MGLM, 31 cases of grade 3-4 GLM, 11 cases of bilateral metachronous GLM. There were 18 patients in the prosthesis implantation group, including 9 patients with bilateral metachronous GLM, 2 patients with synchronous GLM, and 41 patients in the traditional treatment group. All the patients were followed up with a median of 17.5 months. One patient in the observation group had an infection on the reconstructed side 3 weeks after surgery, and the implant was retained after 2 weeks of conservative treatment such as antibiotics. Two patients in the prosthesis implantation group were not satisfied with size of the reconstructed breast was smaller than the opposite side. In the traditional treatment group, there were 3 cases of postoperative infection or delayed wound healing, and 26 cases of postoperative breast asymmetry.

Conclusions

For patients with MGLM, it is safe to select stage I prosthesis implantation after conservative treatment, with exact effect and high patient satisfaction.

Treatment Effect of Mammary Duct Exploration Combined with Focal Resection on Granulomatous Lobular Mastitis

OBJECTIVE

This study aims to summarize the treatment of granulomatous lobular mastitis with a low recurrence rate and high satisfaction rate.

METHODS

In this study, the epidemiology and treatment effect was analyzed based on the general data and treatment effects. Patients treated for granulomatous lobular mastitis at the Dongzhimen Hospital, Beijing University of Chinese Medicine, and Beijing Contemporary Hospital from October 24, 2016, to May 8, 2019, were selected as subjects for the study.

RESULTS

The results revealed that the common features of granulomatous lobular mastitis were nipple retraction, history of induced abortion, nipple discharge, history of thyroid disease, history of lactation mastitis, and mental illness. The cure rate of the disease was 100%, the recurrence rate was 0.38%, and the patient satisfaction rate was 97.71%.

CONCLUSION

The combination of mammary duct exploration and focal resection has a positive effect on the treatment of this disease and is worthy of clinical popularization.

Bacteria Associated with Granulomatous Lobular Mastitis and the Potential for Personalized Therapy

Granulomatous lobular mastitis (GLM), also known as idiopathic granulomatous mastitis (IGM), is a chronic inflammatory lesion of the breast. The incidence of GLM has been increasing in recent years, especially among young women. The etiologies of GLM have not been fully elucidated but are associated with autoimmunity and bacterial infection. Bacteria, especially Corynebacterium species, play important roles in GLM. In this article, we review research progress regarding the bacteriology of GLM attained with the application of several new high-throughput detection techniques. Accurate detection might be important for deepening our understanding of the pathogenesis of GLM and hold promise for personalized GLM therapy.

Relationship between granulomatous lobular mastitis and methylene tetrahydrofolate reductase gene polymorphism

BACKGROUND

Variations in the methylene tetrahydrofolate reductase (MTHFR) gene have been reported as risk factors for numerous conditions, including cardiovascular disease, thrombophilia, stroke, hypertension and pregnancy-related complications. Moreover, it was reported there is an association between breast cancer and mutations in MTHFR-C677T. However, whether there is an association between MTHFR gene polymorphism and granulomatous lobular mastitis or not has been rarely investigated.

AIM

To analyze the association between MTHFR gene polymorphism and granulomatous lobular mastitis.

METHODS

Fifty-one patients with granulomatous lobular mastitis admitted to The First Hospital of Kunming were selected as study samples. Their hospitalization time ranged from February 2018 to February 2019. The 51 patients were included in the experimental group, and another 51 women who underwent physical examination at The First Hospital of Kunming in the same period were included in the control group. Deoxyribonucleic acid and MTFR genetic polymorphism testing were performed in each group. The association between MTHFR gene polymorphism and granulomatous lobular mastitis was observed.

RESULTS

There were significant differences in genotype frequency and allele frequency of C/C and C/T between the experimental group and the control group (all P < 0.05). However, there was no significant difference in frequency of T/T genotype between the two groups (P > 0.05). In addition, there was no significant difference in genotype frequency and allele frequency of A/A, A/C and C/C between the two groups (P > 0.05).

CONCLUSION

MTHFR gene C677T locus polymorphism is closely related to granulomatous lobular mastitis.