Department of Breast, Women's Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital, No. 123 Tianfei Street, Mochou Road, Nanjing, 210004, China.
Department of Breast Disease, the First Affiliated Hospital of Nanjing Medical University, No.300 Guangzhou Road, Nanjing, 210000, China.
Int J Med Sci. 2024 Oct 21;21(14):2714-2724. doi: 10.7150/ijms.101855. eCollection 2024.
This study aimed to develop a combined ultrasound (US)-pathology model to predict the axillary status more accurately after NST in breast cancer. This retrospective study included breast cancer patients who received NST at the First Affiliated Hospital of Nanjing Medical University from 2015 to 2022. Clinical, US, and pathological data were collected. Univariate and multivariate logistic regression analyses were performed to identify independent predictors of axillary pathological complete response (pCR). The model was developed using the predictors and validated. A total of 657 patients were enrolled in this study. Two multivariate logistic analyses were performed before and after the operation. The results showed that the clinical lymph nodes, ER status, HER2 status, chemotherapy response of primary tumor, hilum structure of axillary lymph nodes (ALNs) after NST, blood flow of ALNs after NST, vascular invasion, pathological size, and Miller-Payne grade (all p < 0.05) were independent predictors of axillary pCR. The US-based and combined US-pathology models were developed based on preoperative and postoperative information. The two models had an area under the receiver operating characteristic curve (AUC) of 0.821 and 0.883, respectively, which was significantly higher than that of the fine-needle aspiration model (AUC: 0.735). In this study, based on the US-based model, a combined model incorporating the characteristics of ALNs under US and breast pathology was developed and validated to predict axillary pCR.
本研究旨在建立一种联合超声(US)-病理学模型,以更准确地预测乳腺癌新辅助化疗(NST)后的腋窝状态。本回顾性研究纳入了 2015 年至 2022 年在南京医科大学第一附属医院接受 NST 的乳腺癌患者。收集了临床、US 和病理学数据。进行了单因素和多因素逻辑回归分析,以确定腋窝病理完全缓解(pCR)的独立预测因素。使用这些预测因素建立并验证了模型。共有 657 例患者纳入本研究。在手术前后进行了两次多因素逻辑分析。结果显示,临床淋巴结、ER 状态、HER2 状态、原发病灶化疗反应、NST 后腋窝淋巴结(ALNs)门结构、NST 后 ALNs 血流、血管侵犯、病理大小和 Miller-Payne 分级(均 p<0.05)是腋窝 pCR 的独立预测因素。基于术前和术后信息建立了基于 US 的和联合 US-病理学模型。两个模型的受试者工作特征曲线下面积(AUC)分别为 0.821 和 0.883,均显著高于细针穿刺模型(AUC:0.735)。在本研究中,基于基于 US 的模型,建立并验证了一种联合模型,该模型结合了 US 和乳腺病理学下 ALNs 的特征,以预测腋窝 pCR。