Purpose: In sentinel lymph node (SLN) biopsy (SLNB) during breast cancer surgery, SLN mapping using dye and isotope (DUAL) may have lower false-negative rates than the dye-only (DYE) method. However, the long-term outcomes of either method are unclear. We aimed to compare long-term oncological outcomes of DYE and DUAL for SLNB in early breast cancer. Materials and Methods: This retrospective single-institution cohort study included 5,795 patients (DYE, 2,323; DUAL, 3,472) with clinically node-negative breast cancer who underwent SLNB and no neoadjuvant therapy. Indigo carmine was used for the dye method and Tc99m-antimony trisulfate for the isotope. To compare long-term outcomes, pathologic N0 patients were selected from both groups, and propensity score matching (PSM), considering age, pT category, breast surgery, and adjuvant treatment, was performed (1,441 patients in each group). Results: The median follow-up duration was 8.7 years. The median number of harvested sentinel nodes was 3.21 and 3.12 in the DYE and DUAL groups, respectively (p=0.112). The lymph node–positive rate was not significantly different between the two groups in subgroups of similar tumor sizes (p > 0.05). Multivariate logistic regression revealed that the mapping method was not significantly associated with the lymph node–positive rate (p=0.758). After PSM, the 5-year axillary recurrence rate (DYE 0.8% vs. DUAL 0.6%, p=0.096), and 5-year disease-free survival (DYE 93.9% vs. DUAL 93.7%, p=0.402) were similar between the two groups. Conclusion Dye alone for SLNB was not inferior to dual mapping regarding long-term oncological outcomes in early breast cancer.

Purpose: Latissimus dorsi mini-flap (LDMF) reconstruction after breast-conserving surgery (BCS) is a useful volume replacement technique when a large tumor is located in the upper or outer portion of the breast. However, few studies have reported the impact of LDMF on patients’ quality of life (QoL) and cosmesis compared with conventional BCS. Methods: We identified patients who underwent BCS with or without LDMF between 2010 and 2020 at a single center. At least 1 year after surgery, we prospectively administered the BREAST-Q to assess QoL and obtained the patients’ breast photographs. The cosmetic outcome was assessed using four panels composed of physicians and the BCCT.core software. Results: A total of 120 patients were enrolled, of whom 62 and 58 underwent LDMF or BCS only, respectively. The LDMF group had significantly larger tumors, shorter nipple-to-tumor distances in preoperative examinations, and larger resected breast volumes than did the BCSonly group (p < 0.001). The questionnaires revealed that QoL was poorer in the LDMF group, particularly in terms of the physical well-being score (40.9 vs. 20.1, p < 0.001). Notably, the level of patients’ cosmetic satisfaction with their breasts was comparable, and the cosmetic evaluation was assessed by panels and the BCCT.core software showed no differences between the groups. Conclusion Our results showed that cosmetic outcomes of performing LDMF are comparable to those of BCS alone while having the advantage of resecting larger volumes of breast tissue. Therefore, for those who strongly wish to preserve the cosmesis of their breasts, LDMF can be considered a favorable surgical option after the patient is oriented toward the potential for physical dysfunction after surgery.

Purpose: Several predictive models have been developed to predict the pathological complete response (pCR) after neoadjuvant chemotherapy (NAC); however, few are broadly applicable owing to radiologic complexity and institution-specific clinical variables, and none have been externally validated. This study aimed to develop and externally validate a machine learning model that predicts pCR after NAC in patients with breast cancer using routinely collected clinical and demographic variables. Methods: The electronic medical records of patients with advanced breast cancer who underwent NAC before surgical resection between January 2017 and December 2020 were reviewed. Patient data from Seoul National University Bundang Hospital were divided into training and internal validation cohorts. Five machine learning techniques, including gradient boosting machine (GBM), support vector machine, random forest, decision tree, and neural network, were used to build predictive models, and the area under the receiver operating characteristic curve (AUC) was compared to select the best model. Finally, the model was validated using an independent cohort from Seoul National University Hospital. Results: A total of 1,003 patients were included in the study: 287, 71, and 645 in the training, internal validation, and external validation cohorts, respectively. Overall, 36.3% of the patients achieved pCR. Among the five machine learning models, the GBM showed the highest AUC for pCR prediction (AUC, 0.903; 95% confidence interval [CI], 0.833–0.972).External validation confirmed an AUC of 0.833 (95% CI, 0.800–0.865). Conclusion Commonly available clinical and demographic variables were used to develop a machine learning model for predicting pCR following NAC. External validation of the model demonstrated good discrimination power, indicating that routinely collected variables were sufficient to build a good prediction model.