Department of Medical Ultrasound, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, PR China.
Department of Medical Ultrasound, Fujian Medical University Cancer Hospital, Fujian Cancer Hospital, Fuzhou, PR China.
Clin Breast Cancer. 2024 Aug;24(6):e452-e463.e4. doi: 10.1016/j.clbc.2024.03.005. Epub 2024 Mar 13.
To develop a convenient modality to predict axillary response to neoadjuvant chemotherapy (NAC) in breast cancer patients.
In this multi-center study, a total of 1019 breast cancer patients with biopsy-proven positive lymph node (LN) receiving NAC were randomly assigned to the training and validation groups at a ratio of 7:3. Clinicopathologic and ultrasound (US) characteristics of both primary tumors and LNs were used to develop corresponding prediction models, and a nomogram integrating clinicopathologic and US predictors was generated to predict the axillary response to NAC.
Axillary pathological complete response (pCR) was achieved in 47.79% of the patients. The expression of estrogen receptor, human epidermal growth factor receptor -2, Ki-67 score, and clinical nodal stage were independent predictors for nodal response to NAC. Location and radiological response of primary tumors, cortical thickness and shape of LNs on US were also significantly associated with nodal pCR. In the validation cohort, the discrimination of US model (area under the curve [AUC], 0.76) was superior to clinicopathologic model (AUC, 0.68); the combined model (AUC, 0.85) demonstrates strong discriminatory power in predicting nodal pCR. Calibration curves of the nomogram based on the combined model demonstrated that substantial agreement can be observed between the predictions and observations. This nomogram showed a false-negative rates of 16.67% in all patients and 10.53% in patients with triple negative breast cancer.
Nomogram incorporating routine clinicopathologic and US characteristics can predict nodal pCR and represents a tool to aid in treatment decisions for the axilla after NAC in breast cancer patients.
为了开发一种方便的方法来预测乳腺癌患者新辅助化疗（NAC）后的腋窝反应。
在这项多中心研究中，共有 1019 例经活检证实阳性淋巴结（LN）的乳腺癌患者接受 NAC，随机分为训练组和验证组，比例为 7:3。使用原发肿瘤和 LN 的临床病理和超声（US）特征来开发相应的预测模型，并生成一个包含临床病理和 US 预测因子的列线图来预测 NAC 后的腋窝反应。
47.79%的患者达到了腋窝病理完全缓解（pCR）。雌激素受体、人表皮生长因子受体-2、Ki-67 评分和临床淋巴结分期是淋巴结对 NAC 反应的独立预测因子。原发肿瘤的位置和放射学反应、LN 的皮质厚度和形状在 US 上也与淋巴结 pCR 显著相关。在验证队列中，US 模型的判别力（曲线下面积[AUC]，0.76）优于临床病理模型（AUC，0.68）；联合模型（AUC，0.85）在预测淋巴结 pCR 方面具有较强的判别能力。基于联合模型的列线图校准曲线表明，预测值与观察值之间存在显著一致性。该列线图在所有患者中的假阴性率为 16.67%，在三阴性乳腺癌患者中的假阴性率为 10.53%。
纳入常规临床病理和 US 特征的列线图可以预测淋巴结 pCR，是一种辅助乳腺癌患者 NAC 后腋窝治疗决策的工具。
