PDF(Pubmed)
Abstract：
UNASSIGNED: Accurate classification of breast nodules into benign and malignant types is critical for the successful treatment of breast cancer. Traditional methods rely on subjective interpretation, which can potentially lead to diagnostic errors. Artificial intelligence (AI)-based methods using the quantitative morphological analysis of ultrasound images have been explored for the automated and reliable classification of breast cancer. This study aimed to investigate the effectiveness of AI-based approaches for improving diagnostic accuracy and patient outcomes.
UNASSIGNED: In this study, a quantitative analysis approach was adopted, with a focus on five critical features for evaluation: degree of boundary regularity, clarity of boundaries, echo intensity, and uniformity of echoes. Furthermore, the classification results were assessed using five machine learning methods: logistic regression (LR), support vector machine (SVM), decision tree (DT), naive Bayes, and K-nearest neighbor (KNN). Based on these assessments, a multifeature combined prediction model was established.
UNASSIGNED: We evaluated the performance of our classification model by quantifying various features of the ultrasound images and using the area under the receiver operating characteristic (ROC) curve (AUC). The moment of inertia achieved an AUC value of 0.793, while the variance and mean of breast nodule areas achieved AUC values of 0.725 and 0.772, respectively. The convexity and concavity achieved AUC values of 0.988 and 0.987, respectively. Additionally, we conducted a joint analysis of multiple features after normalization, achieving a recall value of 0.98, which surpasses most medical evaluation indexes on the market. To ensure experimental rigor, we conducted cross-validation experiments, which yielded no significant differences among the classifiers under 5-, 8-, and 10-fold cross-validation (P>0.05).
UNASSIGNED: The quantitative analysis can accurately differentiate between benign and malignant breast nodules.
UNASSIGNED: In this study, a quantitative analysis approach was adopted, with a focus on five critical features for evaluation: degree of boundary regularity, clarity of boundaries, echo intensity, and uniformity of echoes. Furthermore, the classification results were assessed using five machine learning methods: logistic regression (LR), support vector machine (SVM), decision tree (DT), naive Bayes, and K-nearest neighbor (KNN). Based on these assessments, a multifeature combined prediction model was established.
UNASSIGNED: We evaluated the performance of our classification model by quantifying various features of the ultrasound images and using the area under the receiver operating characteristic (ROC) curve (AUC). The moment of inertia achieved an AUC value of 0.793, while the variance and mean of breast nodule areas achieved AUC values of 0.725 and 0.772, respectively. The convexity and concavity achieved AUC values of 0.988 and 0.987, respectively. Additionally, we conducted a joint analysis of multiple features after normalization, achieving a recall value of 0.98, which surpasses most medical evaluation indexes on the market. To ensure experimental rigor, we conducted cross-validation experiments, which yielded no significant differences among the classifiers under 5-, 8-, and 10-fold cross-validation (P>0.05).
UNASSIGNED: The quantitative analysis can accurately differentiate between benign and malignant breast nodules.
摘要：
■将乳腺结节准确分类为良性和恶性类型对于成功治疗乳腺癌至关重要。传统方法依赖于主观解释，这可能会导致诊断错误。已经探索了使用超声图像的定量形态学分析的基于人工智能（AI）的方法来对乳腺癌进行自动化和可靠的分类。这项研究旨在调查基于AI的方法在提高诊断准确性和患者预后方面的有效性。
■在这项研究中，采用了定量分析的方法，重点关注五个关键特征进行评估：边界规则性程度，界限的清晰度，回波强度,回声的均匀性。此外，使用五种机器学习方法评估分类结果：逻辑回归(LR)，支持向量机(SVM)，决策树(DT)，天真的贝叶斯,和K最近邻(KNN)。基于这些评估,建立了多特征组合预测模型。
■我们通过量化超声图像的各种特征并使用接收器工作特征（ROC）曲线（AUC）下的面积来评估我们的分类模型的性能。惯性矩的AUC值为0.793，而乳腺结节区域的方差和平均值分别为0.725和0.772。凸度和凹度分别达到0.988和0.987的AUC值。此外,我们对归一化后的多个特征进行了联合分析，达到0.98的召回值，超过了市场上大多数医疗评估指标。为了确保实验的严谨性，我们进行了交叉验证实验,在5-，8-,和10倍交叉验证(P>0.05)。
■定量分析可以准确区分良性和恶性乳腺结节。
■在这项研究中，采用了定量分析的方法，重点关注五个关键特征进行评估：边界规则性程度，界限的清晰度，回波强度,回声的均匀性。此外，使用五种机器学习方法评估分类结果：逻辑回归(LR)，支持向量机(SVM)，决策树(DT)，天真的贝叶斯,和K最近邻(KNN)。基于这些评估,建立了多特征组合预测模型。
■我们通过量化超声图像的各种特征并使用接收器工作特征（ROC）曲线（AUC）下的面积来评估我们的分类模型的性能。惯性矩的AUC值为0.793，而乳腺结节区域的方差和平均值分别为0.725和0.772。凸度和凹度分别达到0.988和0.987的AUC值。此外,我们对归一化后的多个特征进行了联合分析，达到0.98的召回值，超过了市场上大多数医疗评估指标。为了确保实验的严谨性，我们进行了交叉验证实验,在5-，8-,和10倍交叉验证(P>0.05)。
■定量分析可以准确区分良性和恶性乳腺结节。
