Xu Xuni, Chen Wanying, Wang Gaoyi, Zhou Yaqin, Pan Wenkai, Zhou Yu, Zhang Wei
Department of Radiology, Shaoxing Central Hospital, Shaoxing, China.
The Central Affiliated Hospital, Shaoxing University, Shaoxing, China.
Gland Surg. 2025 Apr 30;14(4):699-713. doi: 10.21037/gs-24-470. Epub 2025 Apr 25.
Breast cancer (BC) is one of the most common malignancies in women worldwide, with surgical interventions such as mastectomy and implant-based reconstruction playing a key role in management. While implant-based reconstruction offers immediate breast contour restoration, complications such as infection, capsular contracture, and implant failure are influenced by patient-specific factors, including age, body mass index (BMI), smoking, and adjuvant therapies like radiation. This study aimed to develop a predictive model for postoperative skin infections to enhance personalized risk assessment and optimize surgical outcomes in BC patients.
This retrospective study included 166 Chinese female patients with BC who underwent unilateral mastectomy followed by implant-based reconstruction. Univariate and multivariate logistic regression analyses were conducted to identify independent risk factors for postoperative skin infections. A nomogram was constructed based on significant variables, with its accuracy assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).
The 166 patients were divided into training and validation cohorts (6:4). Univariate analysis identified BMI, chemotherapy, radiotherapy, and prosthesis thickness as significant factors for postoperative skin infections. Multivariate analysis confirmed BMI, chemotherapy, and prosthesis thickness as independent risk factors. The predictive model demonstrated strong performance, with area under the curve (AUC) values of 0.87 and 0.812 for the training and validation cohorts, respectively. Calibration curves showed good agreement between predicted and observed outcomes, and DCA confirmed the model's clinical utility. A web-based calculator was developed to estimate infection risk (https://kevinpan.shinyapps.io/InfectionStatus/).
BMI, prosthesis thickness, and chemotherapy are key factors influencing the risk of postoperative skin infections in BC patients undergoing implant-based reconstruction. The predictive model developed in this study provides a valuable tool for clinicians to assess risk and personalize treatment plans. Further studies with larger cohorts are needed to validate and refine the model for broader clinical use.
乳腺癌（BC）是全球女性中最常见的恶性肿瘤之一，乳房切除术和基于植入物的重建等手术干预在其治疗中起着关键作用。虽然基于植入物的重建可立即恢复乳房外形，但感染、包膜挛缩和植入物失败等并发症受患者特定因素影响，包括年龄、体重指数（BMI）、吸烟以及放疗等辅助治疗。本研究旨在开发一种术后皮肤感染的预测模型，以加强对BC患者的个性化风险评估并优化手术效果。
这项回顾性研究纳入了166例接受单侧乳房切除术后进行基于植入物重建的中国女性BC患者。进行单因素和多因素逻辑回归分析以确定术后皮肤感染的独立危险因素。基于显著变量构建列线图，并使用受试者操作特征（ROC）曲线、校准曲线和决策曲线分析（DCA）评估其准确性。
166例患者被分为训练组和验证组（6:4）。单因素分析确定BMI、化疗、放疗和假体厚度是术后皮肤感染的显著因素。多因素分析证实BMI、化疗和假体厚度为独立危险因素。预测模型表现良好，训练组和验证组的曲线下面积（AUC）值分别为0.87和0.812。校准曲线显示预测结果与观察结果之间具有良好的一致性，DCA证实了该模型的临床实用性。开发了一个基于网络的计算器来估计感染风险（https://kevinpan.shinyapps.io/InfectionStatus/）。
BMI、假体厚度和化疗是影响接受基于植入物重建的BC患者术后皮肤感染风险的关键因素。本研究开发的预测模型为临床医生评估风险和制定个性化治疗方案提供了有价值的工具。需要进行更大样本量的进一步研究以验证和完善该模型，使其更广泛地应用于临床。
