Chen Chuan, Turco Simona, Kapetas Panagiotis, Mann Ritse, Wijkstra Hessel, de Korte Chris, Mischi Massimo
Eindhoven University of Technology, Eindhoven, Netherlands.
Southeast University, Nanjing, China.
Eur Radiol. 2024 Jul;34(7):4764-4773. doi: 10.1007/s00330-023-10500-x. Epub 2023 Dec 19.
The aim of this study was to apply spatiotemporal analysis of contrast-enhanced ultrasound (CEUS) loops to quantify the enhancement heterogeneity for improving the differentiation between benign and malignant breast lesions.
This retrospective study included 120 women (age range, 18-82 years; mean, 52 years) scheduled for ultrasound-guided biopsy. With the aid of brightness-mode images, the border of each breast lesion was delineated in the CEUS images. Based on visual evaluation and quantitative metrics, the breast lesions were categorized into four grades of different levels of contrast enhancement. Grade-1 (hyper-enhanced) and grade-2 (partly-enhanced) breast lesions were included in the analysis. Four parameters reflecting enhancement heterogeneity were estimated by spatiotemporal analysis of neighboring time-intensity curves (TICs). By setting the threshold on mean parameter, the diagnostic performance of the four parameters for differentiating benign and malignant lesions was evaluated.
Sixty-four of the 120 patients were categorized as grade 1 or 2 and used for estimating the four parameters. At the pixel level, mutual information and conditional entropy present significantly different values between the benign and malignant lesions (p < 0.001 in patients of grade 1, p = 0.002 in patients of grade 1 or 2). For the classification of breast lesions, mutual information produces the best diagnostic performance (AUC = 0.893 in patients of grade 1, AUC = 0.848 in patients of grade 1 or 2).
The proposed spatiotemporal analysis for assessing the enhancement heterogeneity shows promising results to aid in the diagnosis of breast cancer by CEUS.
The proposed spatiotemporal method can be developed as a standardized software to automatically quantify the enhancement heterogeneity of breast cancer on CEUS, possibly leading to the improved diagnostic accuracy of differentiation between benign and malignant lesions.
• Advanced spatiotemporal analysis of ultrasound contrast-enhanced loops for aiding the differentiation of malignant or benign breast lesions. • Four parameters reflecting the enhancement heterogeneity were estimated in the hyper- and partly-enhanced breast lesions by analyzing the neighboring pixel-level time-intensity curves. • For the classification of hyper-enhanced breast lesions, mutual information produces the best diagnostic performance (AUC = 0.893).
本研究旨在应用超声造影（CEUS）动态图像的时空分析来量化增强异质性，以提高乳腺良恶性病变的鉴别能力。
本回顾性研究纳入了120例计划接受超声引导下活检的女性患者（年龄范围18 - 82岁，平均52岁）。借助灰阶图像，在CEUS图像中勾勒出每个乳腺病变的边界。基于视觉评估和定量指标，将乳腺病变分为四个不同增强程度等级。分析纳入1级（高增强）和2级（部分增强）乳腺病变。通过对相邻时间 - 强度曲线（TIC）进行时空分析，估计反映增强异质性的四个参数。通过设定平均参数阈值，评估这四个参数对鉴别良恶性病变的诊断性能。
120例患者中有64例被分类为1级或2级，并用于估计四个参数。在像素水平上，良性和恶性病变之间的互信息和条件熵存在显著差异（1级患者中p < 0.001，1级或2级患者中p = 0.002）。对于乳腺病变的分类，互信息具有最佳的诊断性能（1级患者中AUC = 0.893，1级或2级患者中AUC = 0.848）。
所提出的用于评估增强异质性的时空分析显示出有前景的结果，有助于通过CEUS诊断乳腺癌。
所提出的时空方法可开发为标准化软件，以自动量化CEUS上乳腺癌的增强异质性，可能提高良恶性病变鉴别的诊断准确性。
• 先进的超声造影动态图像时空分析有助于鉴别乳腺恶性或良性病变。• 通过分析相邻像素水平的时间 - 强度曲线，在高增强和部分增强的乳腺病变中估计反映增强异质性的四个参数。• 对于高增强乳腺病变的分类，互信息具有最佳的诊断性能（AUC = 0.893）。
