Instituto de Biofísica e Engenharia Biomédica, Faculdade de Ciências, Universidade de Lisboa, Campo Grande, Lisbon, 1749-016, Portugal.
Departamento de Física, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Lisbon, Portugal.
Med Phys. 2020 Apr;47(4):1860-1870. doi: 10.1002/mp.14064. Epub 2020 Mar 1.
The assessment of the size and shape of breast tumors is of utter importance to the correct diagnosis and staging of breast cancer. In this paper, we classify breast tumor models of varying sizes and shapes using signals collected with a monostatic ultra-wideband radar microwave imaging prototype system with machine learning algorithms specifically tailored to the collected data.
A database comprising 13 benign and 13 malignant tumor models with sizes between 13 and 40 mm was created using dielectrically representative tissue mimicking materials. These tumor models were placed inside two breast phantoms: a homogeneous breast phantom and a breast phantom with clusters of fibroglandular mimicking tissue, accounting for breast heterogeneity. The breast phantoms with tumors were imaged with a monostatic microwave imaging prototype system, over a 1-6 GHz frequency range. The classification of benign and malignant tumors embedded in the two breast phantoms was completed, and tumor classification was evaluated with Principal Component Analysis as a feature extraction method, and tuned Naïve Bayes (NB), decision trees (DT), and k-nearest neighbours (kNN) as classifiers. We further study which antenna positions are better placed to classify tumors, discuss the feature extraction method and optimize classification algorithms, by tuning their hyperparameters, to improve sensitivity, specificity and the receiver operating characteristic curve, while ensuring maximum generalization and avoiding overfitting and data contamination. We also added a realistic synthetic skin response to the collected signals and examined its global effect on classification of benign vs malignant tumors.
In terms of global classification performance, kNN outperformed DT and NB machine learning classifiers, achieving a classification accuracy of 96.2% when classifying between benign and malignant tumor phantoms in a homogeneous breast phantom (both when the skin artifact is and is not considered).
We experimentally classified tumor models as benign or malignant with a microwave imaging system, and we showed a methodology that can potentially assess the shape of breast tumors, which will give further insight into the correct diagnosis and staging of breast cancer.
评估乳房肿瘤的大小和形状对于乳腺癌的正确诊断和分期至关重要。在本文中，我们使用具有机器学习算法的单基地超宽带雷达微波成像原型系统对不同大小和形状的乳房肿瘤模型进行分类，这些算法专门针对所收集的数据进行了调整。
使用具有介电代表性的组织模拟材料创建了一个包含 13 个良性和 13 个恶性肿瘤模型的数据库，这些肿瘤模型的尺寸在 13 至 40mm 之间。这些肿瘤模型被放置在两个乳房体模内：一个均匀的乳房体模和一个具有纤维腺体模拟组织簇的乳房体模，以模拟乳房的异质性。带有肿瘤的乳房体模使用单基地微波成像原型系统进行成像，频率范围为 1-6GHz。对两个乳房体模中嵌入的良性和恶性肿瘤进行分类，并使用主成分分析作为特征提取方法对肿瘤分类进行评估，使用调谐朴素贝叶斯（NB）、决策树（DT）和 k-最近邻（kNN）作为分类器。我们进一步研究了更好的天线位置，以实现肿瘤分类，讨论了特征提取方法，并优化了分类算法，通过调整其超参数，以提高敏感性、特异性和接收器工作特性曲线，同时确保最大的泛化能力，避免过拟合和数据污染。我们还在收集的信号中添加了现实的合成皮肤响应，并研究了其对良性与恶性肿瘤分类的全局影响。
在全局分类性能方面，kNN 优于 DT 和 NB 机器学习分类器，在均匀乳房体模中对良性和恶性肿瘤体模进行分类时，分类准确率达到 96.2%（考虑和不考虑皮肤伪影时均如此）。
我们使用微波成像系统对肿瘤模型进行了良性或恶性分类实验，并展示了一种潜在的评估乳房肿瘤形状的方法，这将进一步深入了解乳腺癌的正确诊断和分期。
