Background

Our aims were to determine if features derived from texture analysis (TA) can distinguish normal, benign, and malignant tissue on automated breast ultrasound (ABUS); to evaluate whether machine learning (ML) applied to TA can categorise ABUS findings; and to compare ML to the analysis of single texture features for lesion classification.

Methods

This ethically approved retrospective pilot study included 54 women with benign (n = 38) and malignant (n = 32) solid breast lesions who underwent ABUS. After manual region of interest placement along the lesions’ margin as well as the surrounding fat and glandular breast tissue, 47 texture features (TFs) were calculated for each category. Statistical analysis (ANOVA) and a support vector machine (SVM) algorithm were applied to the texture feature to evaluate the accuracy in distinguishing (i) lesions versus normal tissue and (ii) benign versus malignant lesions.

Results

Skewness and kurtosis were the only TF significantly different among all the four categories (p < 0.000001). In subsets (i) and (ii), a maximum area under the curve of 0.86 (95% confidence interval [CI] 0.82–0.88) for energy and 0.86 (95% CI 0.82–0.89) for entropy were obtained. Using the SVM algorithm, a maximum area under the curve of 0.98 for both subsets was obtained with a maximum accuracy of 94.4% in subset (i) and 90.7% in subset (ii).

Conclusions

TA in combination with ML might represent a useful diagnostic tool in the evaluation of breast imaging findings in ABUS. Applying ML techniques to TFs might be superior compared to the analysis of single TF.

中文翻译：

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机器学习的诊断性能应用于基于纹理分析的特征，用于在自动乳房超声检查中表征乳房病变：一项初步研究。

背景

我们的目标是确定通过纹理分析（TA）得出的特征是否可以在自动乳房超声（ABUS）上区分正常，良性和恶性组织；评估应用于TA的机器学习（ML）是否可以对ABUS结果进行分类；并将ML与单个纹理特征的分析进行病变分类的比较。

方法

这项经伦理学批准的回顾性先导研究包括54例接受ABUS治疗的良性（n = 38）和恶性（n = 32）实体乳腺病变的妇女。在沿病灶边缘以及周围的脂肪和腺体乳房组织手动放置感兴趣区域后，为每个类别计算了47个纹理特征（TF）。将统计分析（ANOVA）和支持向量机（SVM）算法应用于纹理特征，以评估区分（i）病变与正常组织以及（ii）良性与恶性病变的准确性。

结果

在所有四个类别中，偏度和峰度是唯一显着不同的TF（p <0.000001）。在子集（i）和（ii）中，曲线下的最大能量面积为0.86（95％置信区间[CI] 0.82-0.88），熵为0.86（95％CI 0.82-0.89）。使用SVM算法，两个子集的曲线下面积最大为0.98，子集（i）的最大准确度为94.4％，子集（ii）的最大准确度为90.7％。

结论

TA与ML结合可能是评估ABUS乳腺影像学表现的有用诊断工具。与单个TF的分析相比，将ML技术应用于TF可能更好。