Department of Ultrasound, Affiliated Dongyang Hospital of Wenzhou Medical University (Dongyang People's Hospital), Dongyang, Zhejiang, China.
Department of Ultrasound, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer, Chinese Academy of Sciences, Hangzhou, China.
Ann Med. 2024 Dec;56(1):2395061. doi: 10.1080/07853890.2024.2395061. Epub 2024 Aug 28.
The tumor burden within the axillary lymph nodes (ALNs) constitutes a pivotal factor in breast cancer, serving as the primary determinant for treatment decisions and exhibiting a close correlation with prognosis.
This study aimed to investigate the potential of ultrasound-based radiomics and clinical characteristics in non-invasively distinguishing between low tumor burden (1-2 positive nodes) and high tumor burden (more than 2 positive nodes) in patients with node-positive breast cancer.
A total of 215 patients with node-positive breast cancer, who underwent preoperative ultrasound examinations, were enrolled in this study. Among these patients, 144 cases were allocated to the training set, 37 cases to the validation set, and 34 cases to the testing set. Postoperative histopathology was used to determine the status of ALN tumor burden. The region of interest for breast cancer was delineated on the ultrasound image. Nine models were developed to predict high ALN tumor burden, employing a combination of three feature screening methods and three machine learning classifiers. Ultimately, the optimal model was selected and tested on both the validation and testing sets. In addition, clinical characteristics were screened to develop a clinical model. Furthermore, Shapley additive explanations (SHAP) values were utilized to provide explanations for the machine learning model.
During the validation and testing sets, the models demonstrated area under the curve (AUC) values ranging from 0.577 to 0.733 and 0.583 to 0.719, and accuracies ranging from 64.9% to 75.7% and 64.7% to 70.6%, respectively. Ultimately, the Boruta_XGB model, comprising five radiomics features, was selected as the final model. The AUC values of this model for distinguishing low from high tumor burden were 0.828, 0.715, and 0.719 in the training, validation, and testing sets, respectively, demonstrating its superiority over the clinical model.
The developed radiomics models exhibited a significant level of predictive performance. The Boruta_XGB radiomics model outperformed other radiomics models in this study.
腋窝淋巴结（ALN）中的肿瘤负担是乳腺癌的一个关键因素，是决定治疗决策的主要因素，并与预后密切相关。
本研究旨在探讨基于超声的放射组学和临床特征在无创鉴别腋窝淋巴结阳性乳腺癌患者低肿瘤负担（1-2 个阳性淋巴结）和高肿瘤负担（超过 2 个阳性淋巴结）中的潜力。
共纳入 215 例术前接受超声检查的腋窝淋巴结阳性乳腺癌患者。其中 144 例患者被分配到训练集，37 例患者被分配到验证集，34 例患者被分配到测试集。术后组织病理学用于确定 ALN 肿瘤负担状态。在超声图像上勾画乳腺癌感兴趣区。采用三种特征筛选方法和三种机器学习分类器相结合，构建了 9 种预测高 ALN 肿瘤负担的模型。最终，选择最优模型并在验证集和测试集上进行测试。此外，还筛选了临床特征以建立临床模型。此外，还利用 Shapley 加法解释（SHAP）值为机器学习模型提供解释。
在验证集和测试集中，模型的曲线下面积（AUC）值分别为 0.577 至 0.733 和 0.583 至 0.719，准确率分别为 64.9%至 75.7%和 64.7%至 70.6%。最终，选择包含五个放射组学特征的 Boruta_XGB 模型作为最终模型。该模型在训练集、验证集和测试集中区分低肿瘤负担和高肿瘤负担的 AUC 值分别为 0.828、0.715 和 0.719，优于临床模型。
所开发的放射组学模型具有显著的预测性能。在本研究中，Boruta_XGB 放射组学模型优于其他放射组学模型。
