Breast Disease Research Articles

A study on the pharmacovigilance of various SGLT-2 inhibitors

BackgroundSodium-glucose co-transporter two inhibitors (SGLT2is) are widely used in clinical practice due to their proven cardiovascular and renal benefits. However, various adverse drug reactions (ADRs) have been reported. This study aims to systematically update the ADRs associated with SGLT2is and identify the differences among various SGLT2is acovigilance of various SGLT-2 inhibitors.MethodsData from the FAERS database covering Q1 2013 to Q2 2024 were selected for disproportionality analysis. ADRs were defined using the System Organ Classes (SOC) and Preferred Terms (PT) from the MedDRA 27.0 dictionary. Four signal detection metrics—reporting odds ratio (ROR), proportional reporting ratios (PRRs), Bayesian Confidence Propagation Neural Network (BCPNN), and empirical Bayesian geometric mean (EBGM)—were utilized to infer ADRs and assess differences among specific SGLT2i drugs through intersection analysis.ResultsExcept for canagliflozin, both dapagliflozin and empagliflozin showed a general increase in ADRs. Specifically, canagliflozin had 93 ADRs, dapagliflozin had 173, and empagliflozin had 214. Most of these were related to Infections and Infestations, Investigations, and Reproductive System and Breast Disorders, notably manifesting as inflammatory conditions of the urinary and reproductive systems, such as orchitis and testicular abscess, consistent with FDA labeling. Additionally, overlooked ADRs were identified, including bladder cancer, cholangiocarcinoma, and thrombotic strokes, none of which were reported for canagliflozin.DiscussionWhile shared ADRs for SGLT2is are noted in FDA labeling, monitoring for high-risk populations, such as those with cancers or strokes, remains crucial to prevent deterioration. Medication regimens may need adjustment, including selecting canagliflozin or non-SGLT2i alternatives when necessary.

Cystic Breast Disease: What to Do When It's Not So Cystic.

Cystic breast masses are a common entity encountered by breast radiologists. The imaging features of benign and malignant cystic masses may overlap, causing confusion and miscategorization with the potential to produce diagnostic delay and harm. This article provides a review of key differentiating imaging features that help guide appropriate mass characterization and treatment.

THE ROLE OF BREAST IMAGING MODALITIES IN THE DIAGNOSIS AND MANAGEMENT OF BENIGN BREAST DISEASES

Objective: Accurate diagnosis and management of benign breast diseases are crucial aspects of breast healthcare. Breast imaging modalities such as mammography, ultrasound, and magnetic resonance imaging (MRI) play a pivotal role in differentiating benign from malignant lesions and guiding appropriate management strategies. Methods: This retrospective study was conducted at [Institution Name] from January 2018 to December 2022. It involved a review of medical records, imaging results, and management outcomes of patients diagnosed with benign breast diseases. Imaging modalities included mammography, ultrasound, and MRI. The diagnostic accuracy of each modality was assessed by comparing imaging findings with histopathological results. Statistical analyses were performed using SPSS version 26.0. Results: A total of 200 patients were included, with a mean age of 45.3±12.6 y. Mammography showed a sensitivity of 85% and specificity of 78%, while ultrasound had a sensitivity of 92% and specificity of 84%. MRI exhibited the highest accuracy with a sensitivity of 95% and specificity of 90%. Combined imaging modalities achieved the highest diagnostic performance. Management strategies varied, with watchful waiting, medication, minimally invasive procedures, and surgical interventions employed based on the specific condition. Conclusion: Breast imaging modalities are indispensable in diagnosing and managing benign breast diseases. The integration of mammography, ultrasound, and MRI provides comprehensive diagnostic information, guiding optimal management strategies and improving patient outcomes. Future advancements in imaging technology promise to further enhance diagnostic capabilities.

Patients with DCIS Seen at a Specialized High-Risk Breast Clinic Run by Surgical Advanced Practice Providers Have High Rates of Preventive Medication Uptake.

Preventative medication (PM) uptake is low among patients at an elevated risk of breast cancer, largely due to fears of intolerance. This study aimed to investigate whether a new, surgical advanced practice provider (APP)-run clinic was effectively prescribing PM. We hypothesized equivalent rates of PM uptake compared to consultation with medical oncologists (MD). The APP-run clinic and accompanying database were initiated 01/2023, including patients with benign breast complaints and/or an elevated risk of invasive breast cancer. A historic single-institution surgical database and this prospective database were queried for patients with ductal carcinoma in situ between 04/2007-05/2023 and 01/2023-01/2024, respectively. Patients with invasive breast cancer within the prior 5years were excluded. Chart review abstracted PM type/dose. Chi square analysis compared PM uptake rates and dose. A total of 523 patients met study criteria; the MD sample and APP sample were relatively well balanced except fewer hormone receptor positive patients in the MD sample (266/309 [86.1%] versus 202/214 [94.4%] APP, p < 0.01). PM uptake was lower in the MD sample (96/309 [31.1%] compared to the APP sample (86/214 [40.2%], p = 0.03). There was significantly more tamoxifen prescribed among the APP sample (58.2% vs. 35.6% among MD, p = 0.02), and low-dose tamoxifen prescribing increased significantly (47.3% vs. 9.8% MD, p < 0.01). Our surgical APP-run breast health clinic has demonstrated equivalent PM uptake as compared to patients seen previously by medical oncologists. This model should be considered broadly. Additionally, low-dose tamoxifen has become the prescription of choice; thus, long-term studies of tamoxifen 5 mg are warranted.

Paget's Disease of the Male Breast: A Diagnostic Challenge.

Paget's Disease of the Male Breast: A Diagnostic Challenge.

Clinical Profile of Female Patients Presented with Breast Diseases: An Institutional Analysis in Baghdad, Iraq

Clinical Profile of Female Patients Presented with Breast Diseases: An Institutional Analysis in Baghdad, Iraq

Metabolic syndrome is associated with breast cancer mortality: A systematic review and meta-analysis.

This systematic review and meta-analysis assesses the association between metabolic syndrome and breast cancer (BC) outcomes in BC survivors. Systematic searches were carried out in PubMed and Embase using variations of the search terms: breast neoplasms (population), metabolic syndrome (exposure), and survival (outcome). Metabolic syndrome was characterized according to the American Heart Association, which includes the presence of three out of five abnormal findings among the risk factors: high blood pressure, high triglycerides, low high-density lipoprotein, high fasting glucose, and central obesity. Data were obtained from observational studies and randomized controlled trials that utilized survival statistics and reported survival ratios to investigate how the presence of metabolic syndrome at the time of BC diagnosis is associated with BC outcomes. Study data were independently extracted by two authors, and effect sizes were pooled using random-effects models. From the 1019 studies identified in the literature search, 17 were deemed eligible. These encompassed 42,135 BC survivors. The pooled estimates revealed that BC survivors who had metabolic syndrome at the time of their BC diagnosis experienced increased risk of recurrence (HR 1.69, 95% CI: 1.39-2.06), BC mortality (HR 1.83, 95% CI: 1.35-2.49), and shorter disease-free survival (HR 1.57, 95% CI: 1.36-1.81) compared to BC survivors without metabolic syndrome. Among BC survivors, metabolic syndrome was associated with inferior BC outcomes. This necessitates the creation of clinical guidelines that include metabolic screening for BC survivors. Further research should identify effective interventions to reduce the prevalence of metabolic syndrome among BC survivors to improve metabolic health and BC outcomes.

Treatment of idiopathic granulomatous mastitis: a retrospective case series.

Idiopathic granulomatous mastitis (IGM) is a rare inflammatory disease of the breast. Various clinical management approaches have been described, but their efficacy and optimal sequential order remain uncertain. We describe the first Canadian cohort of patients with IGM, discuss treatment outcomes and outline a practical management approach. This retrospective study included patients diagnosed with biopsy-confirmed IGM between 2014 and 2023, aged over 18 years. Based on a scoping review of the literature, a diagnostic and management approach was developed, and we present here the disease course and outcomes using this approach. 22 females were included, with a mean age of 40 (24-65) years, mostly presenting with a breast lump (n =22, 100%) and breast pain (n = 15, 68%). Mean rheumatology follow-up was 28.7 months (range 3-79). Mean time from first symptom to diagnosis was 3.5 months (range 1-13). Corynebacterium kroppenstedtii was found in 8 patients. Treatment including lipophilic antibiotics, corticosteroids and disease modifying anti-rheumatic drugs (DMARDs), led to complete remission in 95% of patients, in a mean time of 11.6 months (range 1-36), and relapse in only 1 patient. 11 patients required DMARDs (50%), most commonly methotrexate (n=9). We highlight the variable severity of IGM and the benefits of a severity-based treatment approach. A diligent evaluation and work-up is essential to manage IGM. The proposed severity-based management approach with medical treatment and less aggressive surgical intervention led to complete remission in 95%.

Hormone Signaling in Breast Development and Cancer.

Hormones control normal breast development and function. They also impinge on breast cancer (BC) development and disease progression in direct and indirect ways. The major ovarian hormones, estrogens and progesterone, have long been established as key regulators of mammary gland development in rodents and linked to human disease. However, their roles have been difficult to disentangle because they act on multiple tissues and can act directly and indirectly on different cell types in the breast, and their receptors interact at different levels within the target cell. Estrogens are well-recognized drivers of estrogen receptor-positive (ER+) breast cancers, and the ER is successfully targeted in ER+ disease. The role of progesterone receptor (PR) as a potential target to be activated or inhibited is debated, and androgen receptor (AR) signaling has emerged as a potentially interesting pathway to target on the stage.In this chapter, we discuss hormone signaling in normal breast development and in cancer, with a specific focus on the key sex hormones: estrogen, progesterone, and testosterone. We will highlight the complexities of endocrine control mechanisms at the organismal, tissue, cellular, and molecular levels. As we delve into the mechanisms of action of hormone receptors, their interplay and their context-dependent roles in breast cancer will be discussed. Drawing insights from new preclinical models, we will describe the lessons learned and the current challenges in understanding hormone action in breast cancer.

Omics based technology application in poultry meat research.

Omics techniques, including genomics, transcriptomics, proteomics, metabolomics, and lipidomics, analyze entire sets of biological molecules to seek comprehensive knowledge on a particular phenotype. These approaches have been extensively utilized to identify both biomarkers and biological mechanisms for various physiological conditions in livestock and poultry. The purpose of this symposium was not only to focus on how recent omics technologies can be used to gather, integrate, and interpret data produced by various methodologies in poultry research, but also to highlight how omics and bioinformatics have increased our understanding of poultry meat quality problems and other complex traits. This Poultry Science Association symposium paper includes 5 sections that cover: 1) functional annotation of cis-regulatory elements in the genome informs genetic control of complex traits in poultry, 2) mass spectrometry for proteomics, metabolomics, and lipidomics, 3) proteomic approaches to investigate meat quality, 4) spatial transcriptomics and metabolomics studies of wooden breast disease, and 5) multiomics analyses on chicken meat quality and spaghetti meat. These topics provide insights into the molecular components that contribute to the structure, function, and dynamics of the underlying mechanisms influencing meat quality traits, including chicken breast myopathies. This information will ultimately contribute to improving the quality and composition of poultry products.

A recurrence model for non-puerperal mastitis patients based on machine learning.

Non-puerperal mastitis (NPM) is an inflammatory breast disease affecting women during non-lactation periods, and it is prone to relapse after being cured. Accurate prediction of its recurrence is crucial for personalized adjuvant therapy, and pathological examination is the primary basis for the classification, diagnosis, and confirmation of non-puerperal mastitis. Currently, there is a lack of recurrence models for non-puerperal mastitis. The aim of this research is to create and validate a recurrence model using machine learning for patients with non-puerperal mastitis. We retrospectively collected laboratory data from 120 NPM patients, dividing them into a non-recurrence group (n = 59) and a recurrence group (n = 61). Through random allocation, these individuals were split into a training cohort and a testing cohort in a 90%:10% ratio for the purpose of building the model. Additionally, data from 25 NPM patients from another center were collected to serve as an external validation cohort for the model. Univariate analysis was used to examine differential indicators, and variable selection was conducted through LASSO regression. A combination of four machine learning algorithms (XGBoost、Logistic Regression、Random Forest、AdaBoost) was employed to predict NPM recurrence, and the model with the highest Area Under the Curve (AUC) in the test set was selected as the best model. The finally selected model was interpreted and evaluated using Receiver Operating Characteristic (ROC) curves, calibration curves, Decision curve analysis (DCA), and Shapley Additive Explanations (SHAP) plots. The logistic regression model emerged as the optimal model for predicting recurrence of NPM with machine learning, primarily utilizing three variables: FIB, bacterial infection, and CD4+ T cell count. The model showed an AUC of 0.846 in the training cohort and 0.833 in the testing cohort. The calibration curve indicated excellent calibration of the model. DCA revealed that the model possessed favorable clinical utility. Furthermore, the model effectively achieved in the external validation group, with an AUC of 0.825. The machine learning model developed in this study, serving as an effective tool for predicting NPM recurrence, aids doctors in making more individualized treatment decisions, thereby enhancing therapeutic efficacy and reducing the risk of recurrence.

Unveiling Rare Breast Neoplasms: Diagnostic Challenges and Insights

Breast neoplasm encompasses a diversified range of different diseases characterized by unique biological and pathological features, clinical presentation, response to treatments, clinical behavior, and outcome. The histological variability has profound prognostic implications, thus playing a pivotal role in diagnosing breast neoplasm. Special histologies can occur rarely, and most information on outcomes and treatments is mainly derived from small series and case reports. Thus, reporting such unusual occurrences is of utmost importance. This is a retrospective study of four years in which 11 cases of breast neoplasm diagnosed on histopathology were assessed. The clinical details were acquired from an electronic search. The hematoxylin and eosin and immunohistochemistry slides were retrieved from the archives to review the histopathological features. The patient’s age ranged from 32 to 72 years (median: 51 years). Eleven unusual cases of breast neoplasms were diagnosed and categorized according to the latest World Health Organisation (WHO) classification. These cases include rare and salivary gland type tumors (2 cases), neuroendocrine neoplasms (1 case), hamartomas of the breast (2 cases), fibroblastic and myofibroblastic tumors of the breast (1 case), peripheral nerve sheath tumor (1 case) and hematolymphoid tumors of the breast (4 cases). The management of unusual breast cancer histotypes represents a real challenge in daily clinical practice. Our data suggest that these variants are distinct clinicopathological entities with unique hormonal receptor statuses. The occurrence of such entities is rare, and they demonstrate different clinical behaviors and responses to treatment, suggesting that a few genomic-specific events might also drive them. Therefore, it is important to indulge in future research on such rare breast neoplasms.

Quantitative Site-Specific Glycoproteomics Reveals Glyco-Signatures for Breast Cancer Diagnosis.

Intact glycopeptide characterization by mass spectrometry has proven to be a versatile tool for site-specific glycoproteomics analysis and biomarker screening. Here, we present a method using a new model of a Q-TOF instrument equipped with a Zeno trap for intact glycopeptide identification and demonstrate its ability to analyze large-cohort glycoproteomes. From 124 clinical serum samples of breast cancer, noncancerous diseases, and nondisease controls, a total of 6901 unique site-specific glycans on 807 glycosites of proteins were detected. Much more differences of glycoproteome were observed in breast diseases than the proteome. By employing machine learning, 15 site-specific glycans were determined as potential glyco-signatures in detecting breast cancer. The results demonstrate that our method provides a powerful tool in glycoproteomic studies.

Changes in Breast Cancer Risk Associated with Benign Breast Disease from 1967 to 2013.

Benign breast disease (BBD) increases breast cancer (BC) risk progressively for women diagnosed with non-proliferative (NP) change, proliferative disease without atypia (PDWA), and atypical hyperplasia (AH). Leveraging data from 18,704 women in the Mayo BBD Cohort (1967-2013), we evaluated temporal trends in BBD diagnoses and how they have influenced associated BC risk over four decades. BC risk trends associated with BBD were evaluated using standardized incidence ratios (SIRs) and age-period-cohort modeling across four eras-pre-mammogram (1967-1981), pre-core needle biopsy (CNB) (1982-1992), transition to CNB (1993-2001), and CNB era (2002-2013). With a median follow-up of 15.8 years, 9.9% of women were diagnosed with BC (invasive and/or DCIS). From the pre-mammogram era to the CNB era, we observed a significant increase in BC risk, rising from an SIR of 1.61 to 1.99. The proportion of proliferative BBD diagnoses (PDWA or AH) increased markedly over time (28.1% to 49.7%), as did the proportion of DCIS events (11% to 28%; chi-square p < .001). Within specific BBD categories, the risk of invasive BC increased modestly. While absolute risk of BC within BBD categories remained stable, the relative risk of BC after BBD increased over time due to notable increases in higher risk BBD lesions, specifically PDWA and AH. These findings illustrate how evolving screening practices have changed the risk profile of BBD and should inform management strategies for patients with BBD in modern clinical settings.

Factors Affecting Presentation Delay to the Hospital Among Breast Cancer Patients: A Retrospective Analysis

Objective: Breast cancer constituted 13.5% of all reported new cancer in India, with a notable mortality rate due to late presentation. The current research aims to address the extent of delay in presentation to the hospital and the factors that contribute to delayed presentation among breast cancer patients. Materials and methods: A descriptive cross-sectional study was conducted among 138 primary breast cancer patients aged 18 to 60 years, with a time lag of more than 2 weeks between the initial recognition of symptoms and the first medical interaction. These patients were attending the oncology Outpatient Department (OPD) of tertiary care center in Central India, where female breast cancer patients receive treatment and follow-up care. The analysis employed binary logistic regression to assess the factors significantly affecting the delayed presentation to the hospital. Results: Most of the participants were aged between 40 and 50 years and did not have a family history of breast cancer. The median delay in presentation to the hospital was 2 months. Women having breast lump as initial breast symptom tend to present late (&gt; 3 months) to the hospital (p=0.037) and if the breast lump was smaller, they are more likely to delay seeking medical attention compared to those with larger breast lump (p=0.012). Conclusion: The study indicates that many patients delayed seeking help due to small breast lumps. Improved screening services are needed for early detection. Future awareness campaigns should emphasize the link between breast lumps and cancer to reduce delays and improve patient quality of life.

From ductal carcinoma in situ to invasive breast cancer: the prognostic value of the extracellular microenvironment.

Ductal carcinoma in situ (DCIS) is a noninvasive breast disease that variably progresses to invasive breast cancer (IBC). Given the unpredictability of this progression, most DCIS patients are aggressively managed similar to IBC patients. Undoubtedly, this treatment paradigm places many DCIS patients at risk of overtreatment and its significant consequences. Historically, prognostic modeling has included the assessment of clinicopathological features and genomic markers. Although these provide valuable insights into tumor biology, they remain insufficient to predict which DCIS patients will progress to IBC. Contemporary work has begun to focus on the microenvironment surrounding the ductal cells for molecular patterns that might predict progression. In this review, extracellular microenvironment alterations occurring with the malignant transformation from DCIS to IBC are detailed. Not only do changes in collagen abundance, organization, and localization mediate the transition to IBC, but also the discrete post-translational regulation of collagen fibers is understood to promote invasion. Other extracellular matrix proteins, such as matrix metalloproteases, decorin, and tenascin C, have been characterized for their role in invasive transformation and further demonstrate the prognostic value of the extracellular matrix. Importantly, these extracellular matrix proteins influence immune cells and fibroblasts toward pro-tumorigenic phenotypes. Thus, the progressive changes in the extracellular microenvironment play a key role in invasion and provide promise for prognostic development.

Complications and patient-reported outcomes after radiotherapy in breast cancer patients undergoing implant-based breast reconstruction: a retrospective study from a large Chinese breast disease center

BackgroundPostmastectomy radiation therapy (PMRT) can influence the outcome of implant-based breast reconstruction (IBBR). This study aims to investigate the complications and patient-reported outcomes (PROs) following PMRT between direct-to-implant (DTI) and tissue expander-to-implant (TEI) reconstruction.MethodsThe retrospective study included breast cancer patients undergoing IBBR and PMRT.Patients were divided into a permanent implant group (PI-PMRT) and a tissue expander group (TE-PMRT). Complications, reconstruction failure, and reoperation were compared between the two groups. PROs were assessed using the BREAST-Q scale.ResultsA total of 203 patients were included: 99 in the PI-PMRT group and 104 in the TE-PMRT group. The incidence of severe capsular contracture was significantly higher in the PI-PMRT group compared to the TE-PMRT group (37.4% vs. 24.0%, p = 0.039). The PI-PMRT group had a significantly lower rate of reconstruction failure (9.1% vs. 19.2%, p = 0.039) and reoperation (13.1% vs. 24.0%, p = 0.046). Multivariate analysis revealed that the absence of mesh (OR = 2.177, p = 0.040) and DTI reconstruction (OR = 1.922, p = 0.046) were independent predictors of severe capsular contracture; the absence of mesh (OR = 4.699, p = 0.015) and TEI reconstruction (OR = 2.429, p = 0.043) were independent predictors of reconstruction failure. BREAST-Q scores indicated greater breast satisfaction in the PI-PMRT group (p = 0.031).ConclusionsAlthough DTI reconstruction resulted in a higher risk of severe capsular contracture, the higher risk of reconstruction failure and reoperation in patients undergoing TEI reconstruction was even more concerning. Furthermore, patients were more likely to report greater breast satisfaction with DTI reconstruction. Therefore, DTI reconstruction may be a more appropriate option for patients anticipating PMRT.

Role of International Academy of Cytology Yokohama reporting system in breast lesions at tertiary care centre in Central India

BackgroundThe International Academy of Cytology (I.A.C.) Yokohama System to report breast cytopathology can effectively categorize breast diseases into different cytological groups. Fine needle aspiration (FNAC) from the lesions in the breast has been regarded as a major method of diagnosing breast cancer, particularly in rural settings. The major purpose of this study was to validate the diagnostic accuracy of breast FNA utilizing the IAC Yokohama system in future endurances. Histopathological examination is considered the gold standard for diagnosing benign as well as malignant breast lesions and is compared with FNA results.Material and methodsResearch on patients getting a core-needle, incisional, or excisional biopsy of breast lesions between January 1st, 2021, and December 31st, 2021, was conducted at a tertiary care center in central India. 216 breast FNAs were recorded utilizing the IAC Yokohama system, and the most appropriate category was assigned for every case and correlated with histopathology to evaluate the effectiveness of IAC system.ResultsThe new "International Academy of Cytology (IAC) Yokohama system" was used to categorize 216 patients into five categories based on the cytologic diagnosis.Those were C1: insufficient material (8.7%), C2: benign (65.7%), C3: atypical (1.8%), C4: suspicious of malignancy (2.7%), and C5: malignant (20.8%). FNACs were associated with ancillary testing and histological diagnosis to examine diagnostic accuracy. The overall Specificity, sensitivity, negative predictive value, positive predictive value, and accuracy were calculated with the risk of malignancy.ConclusionWith high specificity and sensitivity for each type of situation, for all tumors, and for each analyzed BI-RADS category, the IAC Yokohama system provides excellent accuracy for breast FNA .

Prediction of miRNA-disease associations based on PCA and cascade forest

BackgroundAs a key non-coding RNA molecule, miRNA profoundly affects gene expression regulation and connects to the pathological processes of several kinds of human diseases. However, conventional experimental methods for validating miRNA-disease associations are laborious. Consequently, the development of efficient and reliable computational prediction models is crucial for the identification and validation of these associations.ResultsIn this research, we developed the PCACFMDA method to predict the potential associations between miRNAs and diseases. To construct a multidimensional feature matrix, we consider the fusion similarities of miRNA and disease and miRNA-disease pairs. We then use principal component analysis(PCA) to reduce data complexity and extract low-dimensional features. Subsequently, a tuned cascade forest is used to mine the features and output prediction scores deeply. The results of the 5-fold cross-validation using the HMDD v2.0 database indicate that the PCACFMDA algorithm achieved an AUC of 98.56%. Additionally, we perform case studies on breast, esophageal and lung neoplasms. The findings revealed that the top 50 miRNAs most strongly linked to each disease have been validated.ConclusionsBased on PCA and optimized cascade forests, we propose the PCACFMDA model for predicting undiscovered miRNA-disease associations. The experimental results demonstrate superior prediction performance and commendable stability. Consequently, the PCACFMDA is a potent instrument for in-depth exploration of miRNA-disease associations.

The efficacy of norepinephrine application in Mammotome-assisted minimally invasive resection for benign breast neoplasm: A retrospective study

ObjectiveAlthough Mammotome-assisted minimally invasive resection (MAMIR) has been widely accepted for treating breast benign nodules, the procedure remains very technical and challenging. The present study aimed to assess the efficacy of norepinephrine application in MAMIR concerning intraoperative hemorrhage, postoperative pain and postoperative hospitalization.MethodsA total of 306 patients with breast nodules admitted at the Xishan people’s Hospital of Wuxi City between June 2021 and July 2023 were included in this retrospective cohort study. The patient’s age, comorbidities (hypertension and diabetes), and characteristics of the breast nodule (number, unilateral or bilateral nature, inner quadrant volume and total volume) were all meticulously documented. Operation time, intraoperative hemorrhage, postoperative hospitalization and Visual analogue scale(VAS) score (postoperative 6-hour pain score) were recorded. Based on the application of norepinephrine, patients were categorized into non-NPP(norepinephrine) and NPP group. Univariate and multivariate analyses were performed to estimate the odds ratio (OR) and the 95% confidence intervals (CIs) for outcomes.ResultsA total of 155 who accepted MAMIR were included in this study. The NPP group exhibited reduced intraoperative bleeding, diminished postoperative pain, and a shorter duration of hospitalization (p < 0.05). Univariable analysis indicated that usage of norepinephrine during operation helped to reduce intraoperative hemorrhage (OR = 0.14, 95%CI: 0.07–0.31), alleviate postoperative pain(OR = 0.03, 95%CI: 0.01–0.09) and shorten hospital stay(OR = 0.50, 95%CI: 0.26–0.95). Furthermore, multivariate analysis identified the usage of norepinephrine during MAMIR as an independent factor associated with reducing intraoperative hemorrhage (OR = 0.11, 95%CI: 0.05–0.26) and relieving postoperative pain (OR = 0.02, 95%CI: 0.004–0.08) after adjusting for other factories.ConclusionsNorepinephrine could help to reduce postoperative hospitalization and intraoperative hemorrhage, also relieve postoperative pain for breast benign nodules resection, which was an independent protective factor for relieving postoperative pain and reducing intraoperative hemorrhage.