Ioannidis Georgios S, Goumenakis Michalis, Stefanis Ioannis, Karantanas Apostolos, Marias Kostas
Computational BioMedicine Laboratory (CBML), Foundation for Research and Technology-Hellas (FORTH), 70013 Heraklion, Greece.
Department of Radiology, Medical School, University of Crete, 71003 Heraklion, Greece.
Diagnostics (Basel). 2022 Feb 6;12(2):425. doi: 10.3390/diagnostics12020425.
This study aimed to investigate which of the two frequently adopted perfusion models better describes the contrast enhanced ultrasound (CEUS) perfusion signal in order to produce meaningful imaging markers with the goal of developing a machine-learning model that can classify perfusion curves as benign or malignant in breast cancer data. Twenty-five patients with high suspicion of breast cancer were analyzed with exponentially modified Gaussian (EMG) and gamma variate functions (GVF). The adjusted R metric was the criterion for assessing model performance. Various classifiers were trained on the quantified perfusion curves in order to classify the curves as benign or malignant on a voxel basis. Sensitivity, specificity, geometric mean, and AUROC were the validation metrics. The best quantification model was EMG with an adjusted R of 0.60 ± 0.26 compared to 0.56 ± 0.25 for GVF. Logistic regression was the classifier with the highest performance (sensitivity, specificity, G, and AUROC = 89.2 ± 10.7, 70.0 ± 18.5, 77.1 ± 8.6, and 91.0 ± 6.6, respectively). This classification method obtained similar results that are consistent with the current literature. Breast cancer patients can benefit from early detection and characterization prior to biopsy.
本研究旨在探究两种常用灌注模型中哪一种能更好地描述超声造影（CEUS）灌注信号，以便生成有意义的成像标志物，目标是开发一种机器学习模型，能够在乳腺癌数据中将灌注曲线分类为良性或恶性。对25例高度怀疑患有乳腺癌的患者采用指数修正高斯（EMG）和伽马变异函数（GVF）进行分析。调整后的R指标是评估模型性能的标准。在量化的灌注曲线上训练各种分类器，以便在体素基础上将曲线分类为良性或恶性。敏感性、特异性、几何均值和受试者工作特征曲线下面积（AUROC）是验证指标。最佳量化模型是EMG，调整后的R为0.60±0.26，而GVF为0.56±0.25。逻辑回归是性能最高的分类器（敏感性、特异性、几何均值和AUROC分别为89.2±10.7、70.0±18.5、77.1±8.6和91.0±6.6）。这种分类方法获得了与当前文献一致的相似结果。乳腺癌患者可从活检前的早期检测和特征分析中受益。
