Ye Xiaolu, Zhang Xiaoxue, Lin Zhuangteng, Liang Ting, Liu Ge, Zhao Ping
Guangzhou University of Traditional Chinese Medicine First Affiliated Hospital Guangzhou 510405, Guangdong, China.
Guangzhou University of Chinese Medicine Guangzhou 510006, Guangdong, China.
Am J Transl Res. 2024 Jun 15;16(6):2398-2410. doi: 10.62347/KEPZ9726. eCollection 2024.
To develop a nomogram for predicting axillary lymph node metastasis (ALNM) in patients with invasive breast cancer.
We included 307 patients with clinicopathologically confirmed invasive breast cancer. The cohort was divided into a training group (n=215) and a validation group (n=92). Ultrasound images were used to extract radiomics features. The least absolute shrinkage and selection operator (LASSO) algorithm helped select pertinent features, from which Radiomics Scores (Radscores) were calculated using the LASSO regression equation. We developed three logistic regression models based on Radscores and 2D image features, and assessed the models' performance in the validation group. A nomogram was created from the best-performing model.
In the training set, the area under the curve (AUC) for the Radscore model, 2D feature model, and combined model were 0.76, 0.85, and 0.88, respectively. In the validation set, the AUCs were 0.71, 0.78, and 0.83, respectively. The combined model demonstrated good calibration and promising clinical utility.
Our ultrasound-based radiomics nomogram can accurately and non-invasively predict ALNM in breast cancer, suggesting potential clinical applications to optimize surgical and medical strategies.
建立预测浸润性乳腺癌患者腋窝淋巴结转移（ALNM）的列线图。
我们纳入了307例经临床病理证实的浸润性乳腺癌患者。该队列被分为训练组（n = 215）和验证组（n = 92）。利用超声图像提取影像组学特征。最小绝对收缩和选择算子（LASSO）算法有助于选择相关特征，并使用LASSO回归方程计算影像组学评分（Radscores）。我们基于Radscores和二维图像特征建立了三个逻辑回归模型，并在验证组中评估了这些模型的性能。从表现最佳的模型创建了列线图。
在训练集中，Radscore模型、二维特征模型和联合模型的曲线下面积（AUC）分别为0.76、0.85和0.88。在验证集中，AUC分别为0.71、0.78和0.83。联合模型显示出良好的校准和有前景的临床实用性。
我们基于超声的影像组学列线图能够准确且无创地预测乳腺癌中的ALNM，提示其在优化手术和医疗策略方面具有潜在的临床应用价值。
