Objective: To investigate the prevalence and risk factors of axillary web syndrome (AWS) in Korean patients. Methods: This retrospective study included a total of 189 women who underwent breast cancer surgery and received physical therapy between September 2019 and August 2020. We analyzed AWS and the correlation between the patients’ demographics, underlying disease, type of surgery and chemotherapy or radiation therapy, and lymphedema. Results: The prevalence of AWS was found to be 30.6%. In the univariable analysis, age, chemotherapy, and hypertension were related to AWS. Finally, the multivariable logistic regression revealed that chemotherapy (odds ratio [OR]=2.84; 95% confidence interval [CI], 1.46–5.53) and HTN (OR=2.72; 95% CI, 1.18–6.30) were the strongest risk factors of AWS. Conclusion To the best of our knowledge, this was the first study that explored the risk factors of AWS in a Korean population after breast cancer surgery. As almost one-third of patients suffer from AWS after breast cancer surgery, it is essential to closely monitor the development of AWS in patients with hypertension or undergoing chemotherapy.

PURPOSE: Elevated serum concentration of fibrinogen and decreased serum concentration of albumin have been reported to be markers of elevated systemic inflammation. We attempted to investigate the prognostic influence of preoperative fibrinogen to albumin ratio (FAR) for breast cancer. METHODS: Data from 793 consecutive primary breast cancer patients were retrospectively analyzed. Serum levels of fibrinogen and albumin were tested before curative surgery. Subjects were grouped into two groups according to the cutoff value determined by performing the receiver operating characteristic curve analysis: the high FAR group (FAR>7.1) and the low FAR group (FAR≤7.1). Overall survival was assessed using the Kaplan-Meier estimator. Independent prognostic significance was analyzed using the Cox proportional hazards model. RESULTS: The high FAR group had a worse prognosis compared to the low FAR group (log-rank test, p<0.001). The prognostic effect of FAR was more significant than that of single markers such as fibrinogen (log-rank test, p=0.001) or albumin (log-rank test, p=0.001). The prognostic effect of FAR was prominent in the stage II/III subgroup (log-rank test, p<0.001) and luminal A-like subtype (log-rank test, p<0.001). FAR was identified as a significant independent factor on both univariate (hazard ratio [HR], 2.722; 95% confidence interval [CI], 1.659–4.468; p<0.001) and multivariate analysis (HR, 2.622; 95% CI, 1.455–4.724; p=0.001). CONCLUSION: Preoperative FAR was a strong independent prognostic factor in breast cancer. Its prognostic effect was more prominent in the stage II/III subgroup and in the luminal A-like subtype. Therefore, preoperative FAR can be utilized as a useful prognosticator for breast cancer patients. Further studies are needed to validate its applications in clinical settings.
Breast Neoplasms* ; Breast* ; Fibrinogen* ; Humans ; Inflammation ; Multivariate Analysis ; Phenobarbital ; Prognosis ; Proportional Hazards Models ; Retrospective Studies ; ROC Curve ; Serum Albumin ; Survival Analysis

PURPOSE: The prognostic influence of 3-dimensional tumor volume (Tv) on breast cancer compared to conventional 1-dimensional tumor size (T) was investigated. METHODS: Analysis was performed on a cohort of 8,996 primary breast cancer patients who were initially diagnosed with TNM stage I–III. Tumor size was defined as the maximum tumor dimension, and Tv was calculated by the equation of (4π× r1 × r2 × r3)/3; r1, r2, and r3 were defined as half of the largest, intermediate, and shortest dimension of the tumor, respectively. Tv was classified into Tv1, Tv2, and Tv3 according to the cut off values of 2.056 cm3 and 20.733 cm3. RESULTS: The survival curves according to both the T and Tv categories were clearly differentiated (all P < 0.001), as were those for staging by T and Tv (all P < 0.001). In T1 and T2 tumors, the Tv1 group showed superior survival over the Tv2 group (T1, P < 0.001; T2, P = 0.001). Univariate and multivariate analysis both indicated that Tv was a significant prognostic factor (both P < 0.001). The receiver operating characteristic curve showed that the area under the curves were 0.712 (P < 0.001) for Tv and 0.699 (P < 0.001) for T. Positive correlations were observed between the number of positive nodes and T (coefficient = 0.325; P < 0.001), and between the number of positive nodes and Tv (coefficient = 0.321; P < 0.001). CONCLUSION: Tv classification works well for predicting the prognosis of breast cancer, and it is a better predictor than conventional T classification in several aspects. Further studies are needed to validate the practical usefulness of Tv classification in clinical settings.
Breast Neoplasms* ; Breast* ; Classification ; Cohort Studies ; Humans ; Multivariate Analysis ; Prognosis ; ROC Curve ; Survival Analysis ; Tumor Burden*

PURPOSE: Neoadjuvant chemotherapy (NCT) is a treatment modality that increases the breast-conserving rate in breast cancer. This prospective study was performed to evaluate the actual breast-conserving rate using NCT in a clinical setting in a single institution. METHODS: Between 2014 and 2015, 265 patients who were scheduled to receive NCT and surgery were enrolled in this study. Patients were classified into three groups based on the immunohistochemical results of estrogen receptor (ER)/progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2): ER or PR positive (luminal), ER/PR negative and HER2 positive (HER2+), and triple-negative breast cancer (TNBC). Before starting and immediately after completing NCT, a surgeon decided if breast-conserving surgery (BCS) or total mastectomy (TM) should be performed. We analyzed the rate of type of surgery performed. RESULTS: Before administering NCT, 107 patients (40.4%) and 158 patients (59.6%) were candidates for BCS and TM, respectively. Of the 158 patients, 61 were eligible for BCS after chemotherapy, with a conversion rate of 38.6%. NCT increased the BCS eligible rate from 40.4% to 62.6%. Of the 61 patients, 53 chose to undergo BCS, and BCS was successful in 46 (86.8%). Of the 107 BCS candidates at baseline, 100 patents finally underwent BCS (93.5%). According to the subtype, the conversion rates were 35.4%, 50.0%, and 40.5% for luminal, HER2+, and TNBC groups, respectively. CONCLUSION: NCT increased the eligibility for BCS from 40.4% to 62.6% in a clinical setting. This benefit is similar to that observed in other clinical trials.
Breast Neoplasms* ; Breast* ; Drug Therapy* ; Estrogens ; Humans ; Mastectomy, Segmental ; Mastectomy, Simple* ; Phenobarbital ; Prospective Studies ; Receptor, Epidermal Growth Factor ; Triple Negative Breast Neoplasms

Purpose: The coronavirus disease 2019 (COVID-19) pandemic has significantly impacted the rates of screening, case identification, and referral for cancer diagnosis. We investigated the diagnosis and surgery status of breast cancer before and after the COVID-19 pandemic at a multi-institutional level. Methods: We collected breast cancer data from the clinical data warehouse which contained the medical records of patients from six academic institutions in South Korea. Patients were divided into two groups: February to April (period A) and May to July (period B). The data from the two groups were then compared against the same periods in 2019 and 2020. The primary objective was to investigate the differences in breast cancer stages before and after the COVID-19 pandemic. Results: Among 3,038 patients, there was a 9.9% reduction in the number of diagnoses in 2020. This decrease was more significant during period A than period B. The breast cancer stage was not statistically different in period A (p = 0.115), but it was in period B (p = 0.001). In the subset analysis according to age, there was a statistical difference between 2019 and 2020 in period B for patients under the age of 65 years (p = 0.002), but no difference was observed in the other groups. Conclusion The number of breast cancer cases declined during the pandemic, and the staging distribution has changed after the pandemic peak.