Department of Ultrasound Medicine, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
J Ultrasound Med. 2022 Sep;41(9):2191-2201. doi: 10.1002/jum.15900. Epub 2021 Dec 10.
To explore whether conventional elastography and contrast-enhanced ultrasound (CEUS) combined with histopathology can monitor the efficacy of neoadjuvant chemotherapy (NAC) for breast cancer (BC), and develop a Nomogram prediction model monitoring response to NAC.
From February 2010 to November 2015, 91 BC patients who received NAC were recruited. The maximum diameter, stiffness, and CEUS features were assessed. Core biopsy, surgical pathology immunophenotype, and Miller-Payne (MP) evaluation were documented. Univariate and multivariate analysis was performed using receiver operating characteristic (ROC) analysis and logistic regression analysis.
There were 37 cases showing pathological complete response (pCR) and 54 of non-pCR. The changes of maximal diameter were correlated with MP (P < .05). The sensitivity (SEN), specificity (SPE), and area under the ROC curve (AUC) of baseline size predicting pCR were 57.40%, 70.30%, and 0.64 (P = .024). Baseline Ki-67 index of pCR group is significantly higher than that of non-pCR group (P = .029), and the ROC analysis of baseline Ki-67 indicates the SEN, SPE, and AUC of 51.70%, 78.00%, and 0.638 (P = .050). When combined with size, CEUS features, stiffness, and Ki-67 of baseline, the ROC curve shows good performance with SEN, SPE, and AUC of 70.00%, 76.19%, 0.821 (P = .004). Incorporating the change of characteristics into multivariate regression analysis, the results demonstrate excellent performance (SEN 100.00%, SPE 95.24%, AUC 0.986, P = .000).
The change of the maximum size was correlated with MP score, which can provide reference to predict efficacy of NAC and evaluate residual lesions. When combining with elastography, CEUS, and Ki-67, better performance in predicting pathological response was shown.
探讨常规超声弹性成像及超声造影(CEUS)联合组织病理学检查能否监测乳腺癌(BC)新辅助化疗(NAC)的疗效,并建立预测 NAC 疗效的诺莫图预测模型。
选取 2010 年 2 月至 2015 年 11 月间接受 NAC 治疗的 91 例 BC 患者,评估其最大直径、硬度及 CEUS 特征,记录核心活检、手术病理免疫表型及 Miller-Payne(MP)评分,并采用受试者工作特征(ROC)曲线分析和逻辑回归分析进行单因素和多因素分析。
共有 37 例患者达到病理完全缓解(pCR),54 例患者未达到 pCR。最大直径变化与 MP 评分相关(P < .05)。基线大小预测 pCR 的灵敏度(SEN)、特异度(SPE)和 ROC 曲线下面积(AUC)分别为 57.40%、70.30%和 0.64(P=0.024)。pCR 组患者的基线 Ki-67 指数明显高于非 pCR 组(P=0.029),Ki-67 基线的 ROC 分析表明其 SEN、SPE 和 AUC 分别为 51.70%、78.00%和 0.638(P=0.050)。当结合大小、CEUS 特征、基线硬度和 Ki-67 时,ROC 曲线显示出良好的性能,SEN、SPE 和 AUC 分别为 70.00%、76.19%和 0.821(P=0.004)。将特征变化纳入多变量回归分析,结果表明其具有优异的性能(SEN 100.00%、SPE 95.24%、AUC 0.986,P=0.000)。
最大直径的变化与 MP 评分相关,可提供预测 NAC 疗效和评估残留病灶的参考。当与弹性成像、CEUS 和 Ki-67 联合使用时,对预测病理反应的性能更好。