In the quest for effective treatment of early-stage breast cancer, this study aimed to compare the clinical efficacy of modified radical mastectomy (MRM) and oncoplastic breast-conserving surgery (OBCS). Breast cancer remains a major health concern globally, where early detection and effective treatment strategies are crucial for improving the outcomes of patients. MRM and OBCS are two primary treatment modalities for breast cancer, each with its distinct benefits and challenges. Through a retrospective analysis, we found that although the patients in the OBCS group experienced a longer operation time, they had significantly less intraoperative bleeding, postoperative drainage, and hospitalization time compared to the MRM group. Furthermore, patients in the OBCS group demonstrated higher subjective satisfaction and quality of life scores, along with better objective outcomes. In terms of postoperative complications and recurrence rates, no significant difference was identified between the two groups. However, our multivariate Cox regression analysis identified lymph node metastasis and molecular type as independent prognostic factors for disease-free survival (DFS). Subsequently, we constructed a risk model based on these variables, which was proven to be effective in predicting recurrence, with an area under the risk score curve for recurrence prediction being 0.852. The group with a lower risk score demonstrated a significantly higher DFS rate. Our study suggests that compared with MRM, OBCS can significantly reduce surgical incision, improve patient satisfaction, and does not increase the risk of complications or recurrence. Our risk model, developed using Cox regression, also demonstrated high clinical value in predicting breast cancer recurrence, thereby aiding in personalized patient management and treatment planning.
Read the full study here: Oncoplastic breast-conserving surgery improves cosmetic outcomes without increasing recurrence risk compared to modified radical mastectomy in early breast cancer patients: development and validation of a recurrence risk prediction model