超声联合免疫组化及ADC值构建列线图预测乳腺癌新辅助化疗疗效  

Ultrasound Combined with Immunohistochemistry and ADC Value to Construct Nomogram and Predict the Efficacy of Neoadjuvant Chemotherapy in Breast Cancer

作　　者：王文娟 吴萌 杜瑶 冯晓丹 杨洁[1] 王倩倩[1] 刘菲菲[1] Wang Wenjuan;Wu Meng;Du Yao;Feng Xiaodan;Yang Jie;Wang Qianqian;Liu Feifei(Department of Ultrasound Medicine,Binzhou Medical University Hospital,Binzhou,Shandong 256603,China)

机构地区：[1]滨州医学院附属医院超声医学科,山东省滨州市256603

出　　处：《中国超声医学杂志》2024年第6期637-641,共5页Chinese Journal of Ultrasound in Medicine

基　　金：山东省自然科学基金项目(No.ZR2023QH231)。

摘　　要：目的 探讨乳腺癌原发灶超声图像特征、免疫组化指标联合磁共振图像的表观弥散系数(ADC)预测乳腺癌新辅助化疗(NAC)疗效的临床价值。方法 回顾性分析72例乳腺癌NAC患者的治疗前乳腺癌原发灶超声图像、磁共振图像及病理信息。分析乳腺癌原发灶超声图像特征(病灶最大径、形状、边缘、内部回声、后方回声、钙化、侧方声影、方向、血流分级)与免疫组化指标(雌激素受体、孕激素受体、Ki-67、人表皮生长因子受体-2),经单因素与多因素分析筛选与NAC疗效相关的变量,测量ADC值,采用多因素Logistic回归算法构建超声模型、超声联合免疫组化模型、超声联合免疫组化及ADC值的列线图模型,通过受试者工作特征曲线下面积(AUC)和决策曲线评估3种模型对NAC疗效的预测性能,采用综合判别改善指数(IDI)定量分析ADC值对模型的改善情况,并采用Bootstrap法迭代1 000次进行模型内部验证。结果 超声图像特征中形状(OR值:2.272,P值:0.021)、后方回声(OR值:0.551,P值:0.050),免疫组化指标中人表皮生长因子受体-2(OR值:11.158,P值:0.001)为NAC疗效的独立预测因子。基于以上3个变量及ADC值构建的列线图模型取得了最佳的预测性能,AUC为0.885,高于超声模型(AUC:0.759)和超声联合免疫组化模型(AUC:0.863),决策曲线显示其在广泛阈值范围内临床效益最大。纳入ADC值后,IDI显示列线图模型的性能提高了6.58%。采用Bootstrap法迭代1 000次对模型进行内部验证,平均AUC为0.883,模型稳定性良好。结论 基于超声图像特征、免疫组化指标及ADC值构建的列线图模型可较好预测NAC疗效,未来有望辅助临床决策。Objective To investigate the clinical value of ultrasonographic features and immunohistochemical in-dex combined with apparent diffusion coefficient(ADC)of magnetic resonance image in predicting the efficacy of neo-adjuvant chemotherapy(NAC)in primary breast cancer.Methods The ultrasonographic images,magnetic resonance images and pathological information of primary breast cancer in 72 patients with NAC before treatment were retro-spectively analyzed.Ultrasonic features(maximum diameter,shape,edge,internal echogenicity,rear echogenicity,calcification,lateral sound shadow,direction,blood flow grade)and immunohistochemical indexes(estrogen recep-tor,progesterone receptor,Ki-67,human epidermal growth factor receptor-2)of primary breast cancer were ana-lyzed.Variable related to NAC efficacy were screened by univariate and multivariate analysis.After ADC value meas-ured,multivariate logistic regression algorithm was used to construct ultrasound model,ultrasound combined immu-nohistochemistry model,and ultrasound,immunohistochemistry combined ADC value model.The predictive perform-ance of the three models on NAC efficacy was evaluated by area under the curve(AUC)and decision curve analysis.The integrated discriminant improvement(IDI)index was used to quantitatively analyze the improvement of ADC val-ue on the model,and the Bootstrap method was used for 1 ooo iterations for internal verification.Results Shape(OR value:2.272,P value:0.021)and rear echogenicity(OR value:0.551,P value:0.050)in ultrasound image,human epidermal growth factor receptor-2(OR value:11.158,P value:0.001)in immunohistochemical index was the inde-pendent predictor of NAC efficacy.The nomogram model based on the above three variables and ADC value achieved the best predictive performance,with an AUC of 0.885,higher than that of ultrasound model(AUC:O.759)and ultrasound combined immunohistochemistry model(AUC:0.863).The decision curve shows the greatest clinical benefit across a broad threshold range.After ADC value included,IDI showed that the perfor

关 键 词：超声 磁共振成像 乳腺癌 新辅助化疗 表观弥散系数值 

分 类 号：R445.1[医药卫生—影像医学与核医学] R737.9[医药卫生—诊断学]