Osapoetra Laurentius O, Chan William, Tran William, Kolios Michael C, Czarnota Gregory J
Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada.
Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada.
PLoS One. 2020 Dec 31;15(12):e0244965. doi: 10.1371/journal.pone.0244965. eCollection 2020.
Accurate and timely diagnosis of breast carcinoma is very crucial because of its high incidence and high morbidity. Screening can improve overall prognosis by detecting the disease early. Biopsy remains as the gold standard for pathological confirmation of malignancy and tumour grading. The development of diagnostic imaging techniques as an alternative for the rapid and accurate characterization of breast masses is necessitated. Quantitative ultrasound (QUS) spectroscopy is a modality well suited for this purpose. This study was carried out to evaluate different texture analysis methods applied on QUS spectral parametric images for the characterization of breast lesions.
Parametric images of mid-band-fit (MBF), spectral-slope (SS), spectral-intercept (SI), average scatterer diameter (ASD), and average acoustic concentration (AAC) were determined using QUS spectroscopy from 193 patients with breast lesions. Texture methods were used to quantify heterogeneities of the parametric images. Three statistical-based approaches for texture analysis that include Gray Level Co-occurrence Matrix (GLCM), Gray Level Run-length Matrix (GRLM), and Gray Level Size Zone Matrix (GLSZM) methods were evaluated. QUS and texture-parameters were determined from both tumour core and a 5-mm tumour margin and were used in comparison to histopathological analysis in order to classify breast lesions as either benign or malignant. We developed a diagnostic model using different classification algorithms including linear discriminant analysis (LDA), k-nearest neighbours (KNN), support vector machine with radial basis function kernel (SVM-RBF), and an artificial neural network (ANN). Model performance was evaluated using leave-one-out cross-validation (LOOCV) and hold-out validation.
Classifier performances ranged from 73% to 91% in terms of accuracy dependent on tumour margin inclusion and classifier methodology. Utilizing information from tumour core alone, the ANN achieved the best classification performance of 93% sensitivity, 88% specificity, 91% accuracy, 0.95 AUC using QUS parameters and their GLSZM texture features.
A QUS-based framework and texture analysis methods enabled classification of breast lesions with >90% accuracy. The results suggest that optimizing method for extracting discriminative textural features from QUS spectral parametric images can improve classification performance. Evaluation of the proposed technique on a larger cohort of patients with proper validation technique demonstrated the robustness and generalization of the approach.
由于乳腺癌的高发病率和高患病率，准确及时的诊断至关重要。筛查可通过早期发现疾病来改善总体预后。活检仍是恶性肿瘤病理确诊及肿瘤分级的金标准。因此有必要开发诊断成像技术，作为快速准确鉴别乳腺肿块的替代方法。定量超声（QUS）光谱学是非常适合此目的的一种方法。本研究旨在评估应用于QUS光谱参数图像的不同纹理分析方法，以鉴别乳腺病变。
使用QUS光谱学对193例乳腺病变患者测定中带拟合（MBF）、光谱斜率（SS）、光谱截距（SI）、平均散射体直径（ASD）和平均声学浓度（AAC）的参数图像。采用纹理方法量化参数图像的异质性。评估了三种基于统计的纹理分析方法，包括灰度共生矩阵（GLCM）、灰度游程长度矩阵（GRLM）和灰度尺寸区域矩阵（GLSZM）方法。从肿瘤核心及5mm肿瘤边缘测定QUS和纹理参数，并与组织病理学分析进行比较，以将乳腺病变分类为良性或恶性。我们使用不同的分类算法开发了一个诊断模型，包括线性判别分析（LDA）、k近邻（KNN）、带径向基函数核的支持向量机（SVM-RBF）和人工神经网络（ANN）。使用留一法交叉验证（LOOCV）和留出验证评估模型性能。
根据肿瘤边缘纳入情况和分类器方法，分类器的准确率在73%至91%之间。仅利用肿瘤核心的信息，ANN使用QUS参数及其GLSZM纹理特征实现了最佳分类性能，灵敏度为93%，特异性为88%，准确率为91%，AUC为0.95。
基于QUS的框架和纹理分析方法能够以>90%的准确率对乳腺病变进行分类。结果表明，优化从QUS光谱参数图像中提取判别性纹理特征的方法可提高分类性能。在更大队列的患者中使用适当的验证技术对所提出的技术进行评估，证明了该方法的稳健性和通用性。
