基于乳腺癌患者新辅助化疗（NAC）前临床病理特征和超声影像组学特征构建预测病理完全缓解（pCR）的临床模型、影像组学模型及两者联合模型，探讨3种模型预测NAC疗效的临床价值。

回顾性收集2019年1月至2024年1月在黄石市中心医院行乳腺癌NAC并有疗效病理评估结果的女性患者202例，将两个不同院区的病例分为训练集（107例）和验证集（95例）。训练集中，采用单因素和多因素Logistic回归筛选有统计学意义的临床病理特征并构建临床模型；采用最小绝对收缩和选择算子（LASSO）回归筛选影像组学特征建立影像组学模型，并根据特征参数及其回归系数计算每例患者的影像组学评分（Radscore）；采用多因素Logistic回归构建临床病理特征和Radscore的联合预测模型。采用ROC曲线及决策曲线评价并验证3种模型的预测效能及临床应用价值。

训练集中，激素受体（HR）状态和人表皮生长因子受体2（HER2）状态纳入多因素Logistic回归建立临床模型；LASSO回归筛选出对比度、相关度、熵、灰度不均匀性、游程百分比这5个最佳影像组学特征建立影像组学模型；单因素Logistic回归显示Radscore有统计学意义。纳入HR状态（OR值：0.31，95%CI：0.15~0.64，P < 0.01）、HER2状态（OR值：2.96，95%CI：1.43~6.12，P < 0.01）和Radscore（OR值：1.19，95%CI：1.07~1.33，P < 0.01）这3个指标构建联合模型。训练集中临床模型、影像组学模型、联合模型预测pCR的AUC分别为0.68、0.75、0.82，验证集中临床模型、影像组学模型、联合模型预测pCR的AUC分别为0.68、0.72、0.79。联合模型的决策曲线净获益最高，影像组学模型次之，临床模型最低。

临床模型、影像组学模型和联合模型对乳腺癌NAC疗效（是否pCR）预测均有一定的临床价值，其中联合模型的效能最优，具有最佳的预测性能和临床适用性。

To construct a clinical model, an imaging model, and a combined model for predicting pathological complete response (pCR) based on the clinicopathological characteristics and ultrasound imaging characteristics of breast cancer patients before neoadjuvant chemotherapy (NAC), and to compare the clinical value of the three models in predicting the efficacy of NAC.

A total of 202 female patients who underwent NAC for breast cancer at Huangshi Central Hospital from January 2019 to January 2024 and had the results of pathologic evaluation were retrospectively collected. The cases from two different hospital districts were classified into a training set (107 cases) and a validation set (95 cases). In the training set, univariate and multivariate logistic regression analyses were performed to identify clinically significant features and construct a clinical model. The minimum absolute contraction and selection operator regression were used to screen the radiomics features and establish a radiomics model. The Radscore of each patient was calculated according to the characteristic parameters and their regression coefficients. Multivariate logistic regression was then used to construct a combined prediction model based on statistically significant clinicopathological features and Radscore. Receiver operating characteristic (ROC) curve and decision curve were used to evaluate and verify the predictive efficacy and clinical value of the three models.

In the training set, hormone receptor (HR) status and human epidermal growth factor receptor 2 (HER2) status were included in the multivariate logistic regression to establish a clinical model. Lasso regression was used to select five best radiomics features, namely, contrast, correlation, entropy, gray unevenness, and percentage of run, to establish a radiomics model. Univariate logistic regression showed that Radscore was significantly different between patients with and without pCR. HR status (odds ratio [OR]: 0.31, 95% confidence interval [CI]: 0.15-0.64, P < 0.01), HER2 status (OR: 2.96, 95%CI: 1.43-6.12, P < 0.01), and Radscore (OR: 1.19, 95%CI: 1.07-1.33, P < 0.01) were included to build a combined model. In the training set, the area under the ROC curve (AUC) of the clinical model, radiomics model, and combined model in predicting pCR was 0.68, 0.75, and 0.82, respectively. In the validation set, the AUC of the clinical model, radiomics model, and combined model in predicting pCR was 0.68, 0.72, and 0.79, respectively. The combined model had the highest net benefit value in the decision curve analysis, followed by the radiomics model and the clinical model.

The clinical model, radiomics model, and combined model are all clinically valuable for predicting NAC efficacy in breast cancer, with the combined model having the best efficacy, predictive performance, and clinical applicability.