人工智能
乳腺超声检查
接收机工作特性
计算机科学
模式识别(心理学)
乳腺癌
支持向量机
超声波
人工神经网络
模态(人机交互)
医学
放射科
癌症
机器学习
乳腺摄影术
内科学
作者
Xu Zhou,Yuqun Wang,Man Chen,Qi Zhang
标识
DOI:10.1016/j.compbiomed.2022.105920
摘要
The ultrasound (US) diagnosis of breast cancer is usually based on a single-region of a whole breast tumor from a single ultrasonic modality, which limits the diagnostic performance. Multiple regions on multimodal US images of breast tumors may all have useful information for diagnosis. This study aimed to propose a multi-region radiomics approach with multimodal US for artificially intelligent diagnosis of malignant and benign breast tumors.Firstly, radiomics features were extracted from five regions of interest (ROIs) on B-mode US and contrast-enhanced ultrasound (CEUS) images, including intensity statistics, gray-level co-occurrence matrix texture features and binary texture features. The multiple ROIs included the whole tumor region, strongest perfusion region, marginal region and surrounding region. Secondly, a deep neural network, composed of the point-wise gated Boltzmann machine and the restricted Boltzmann machine, was adopted to comprehensively learn and select features. Thirdly, the support vector machine was used for classification between benign and malignant breast tumors. Finally, five single-region classification models were generated from five ROIs, and they were fused to form an integrated classification model.Experimental evaluation was conducted on multimodal US images of breast from 187 patients with breast tumors (68 malignant and 119 benign). Under five-fold cross-validation, the classification accuracy, sensitivity, specificity, Youden's index and area under the receiver operating characteristic curve (AUC) with our model were 87.1% ± 3.3%, 77.4% ± 11.8%, 92.4% ± 7.2%, 69.8% ± 8.6% and 0.849 ± 0.043, respectively. Our model was significantly better than single-region single-modal methods in terms of the AUC and accuracy (p < 0.05).In addition to the whole tumor region, the other regions including the strongest perfusion region, marginal region and surrounding region on US images can assist breast cancer diagnosis. The multi-region multimodal radiomics model achieved the best classification results. Our artificially intelligent model would be potentially useful for clinical diagnosis of breast cancer.
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