Yang Chunmei, Dong Jing, Liu Ziyi, Guo Qingxi, Nie Yue, Huang Deqing, Qin Na, Shu Jian
Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, China.
Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, The Affiliated Hospital of Southwest Medical University, Luzhou, China.
Front Oncol. 2021 Sep 20;11:726240. doi: 10.3389/fonc.2021.726240. eCollection 2021.
The use of traditional techniques to evaluate breast cancer is restricted by the subjective nature of assessment, variation across radiologists, and limited data. Radiomics may predict axillary lymph node metastasis (ALNM) of breast cancer more accurately.
The aim was to evaluate the diagnostic performance of a radiomics model based on ALNs themselves that used contrast-enhanced computed tomography (CECT) to detect ALNM of breast cancer.
We retrospectively enrolled 402 patients with breast cancer confirmed by pathology from January 2016 to October 2019. Three hundred and ninety-six features were extracted for all patients from axial CECT images of 825 ALNs using Artificial Intelligent Kit software (GE Medical Systems, Version V3.1.0.R). Next, the radiomics model was trained, validated, and tested for predicting ALNM in breast cancer by using a support vector machine algorithm. Finally, the performance of the radiomics model was evaluated in terms of its classification accuracy and the value of the area under the curve (AUC).
The radiomics model yielded the best classification accuracy of 89.1% and the highest AUC of 0.92 (95% CI: 0.91-0.93, =0.002) for discriminating ALNM in breast cancer in the validation cohorts. In the testing cohorts, the model also demonstrated better performance, with an accuracy of 88.5% and an AUC of 0.94 (95% CI: 0.93-0.95, =0.005) for predicting ALNM in breast cancer.
The radiomics model based on CECT images can be used to predict ALNM in breast cancer and has significant potential in clinical noninvasive diagnosis and in the prediction of breast cancer metastasis.
使用传统技术评估乳腺癌受到评估主观性、放射科医生之间的差异以及数据有限的限制。放射组学可能更准确地预测乳腺癌腋窝淋巴结转移（ALNM）。
旨在评估基于腋窝淋巴结（ALN）本身的放射组学模型的诊断性能，该模型使用对比增强计算机断层扫描（CECT）检测乳腺癌的ALNM。
我们回顾性纳入了2016年1月至2019年10月间经病理确诊的402例乳腺癌患者。使用人工智能套件软件（GE医疗系统，版本V3.1.0.R）从825个ALN的轴向CECT图像中为所有患者提取396个特征。接下来，使用支持向量机算法对放射组学模型进行训练、验证和测试，以预测乳腺癌中的ALNM。最后，根据其分类准确性和曲线下面积（AUC）值评估放射组学模型的性能。
在验证队列中，放射组学模型在鉴别乳腺癌ALNM方面的最佳分类准确率为89.1%，最高AUC为0.92（95%CI：0.91 - 0.93，P = 0.002）。在测试队列中，该模型也表现出更好的性能，预测乳腺癌ALNM的准确率为88.5%，AUC为0.94（95%CI：0.93 - 0.95，P = 0.005）。
基于CECT图像的放射组学模型可用于预测乳腺癌的ALNM，在临床无创诊断和乳腺癌转移预测方面具有显著潜力。
