Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria.
Department of Radiology, University of Cambridge, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK.
Mol Imaging Biol. 2018 Dec;20(6):1053-1060. doi: 10.1007/s11307-018-1187-x.
We hypothesized that different quantitative ultrasound (US) parameters may be used as complementary diagnostic criteria and aimed to develop a simple classification algorithm to distinguish benign from malignant breast lesions and aid in the decision to perform biopsy or not.
One hundred twenty-four patients, each with one biopsy-proven, sonographically evident breast lesion, were included in this prospective, IRB-approved study. Each lesion was examined with B-mode US, Color/Power Doppler US and elastography (Acoustic Radiation Force Impulse-ARFI). Different quantitative parameters were recorded for each technique, including pulsatility (PI) and resistive Index (RI) for Doppler US and lesion maximum, intermediate, and minimum shear wave velocity (SWV, SWV, and SWV) as well as lesion-to-fat SWV ratio for ARFI. Receiver operating characteristic curve (ROC) analysis was used to evaluate the diagnostic performance of each quantitative parameter. Classification analysis was performed using the exhaustive chi-squared automatic interaction detection method. Results include the probability for malignancy for every descriptor combination in the classification algorithm.
Sixty-five lesions were malignant and 59 benign. Out of all quantitative indices, maximum SWV (SWV), and RI were included in the classification algorithm, which showed a depth of three ramifications (SWV ≤ or > 3.16; if SWV ≤ 3.16 then RI ≤ 0.66, 0.66-0.77 or > 0.77; if RI ≤ 0.66 then SWV ≤ or > 2.71). The classification algorithm leads to an AUC of 0.887 (95 % CI 0.818-0.937, p < 0.0001), a sensitivity of 98.46 % (95 % CI 91.7-100 %), and a specificity of 61.02 % (95 % CI 47.4-73.5 %). By applying the proposed algorithm, a false-positive biopsy could have been avoided in 61 % of the cases.
A simple classification algorithm incorporating two quantitative US parameters (SWV and RI) shows a high diagnostic performance, being able to accurately differentiate benign from malignant breast lesions and lower the number of unnecessary breast biopsies in up to 60 % of all cases, avoiding any subjective interpretation bias.
我们假设不同的定量超声（US）参数可作为补充诊断标准，并旨在开发一种简单的分类算法，以区分良性和恶性乳腺病变，并辅助决定是否进行活检。
本前瞻性、IRB 批准的研究纳入了 124 名患者，每位患者均有经活检证实的超声可见乳腺病变。对每个病变均进行了 B 型超声、彩色/能量多普勒超声和弹性成像（声辐射力脉冲-ARFI）检查。记录了每种技术的不同定量参数，包括多普勒 US 的搏动指数（PI）和阻力指数（RI）以及 ARFI 的病变最大、中间和最小剪切波速度（SWV、SWV 和 SWV）以及病变与脂肪 SWV 比值。使用受试者工作特征曲线（ROC）分析评估每个定量参数的诊断性能。使用穷尽的卡方自动交互检测方法进行分类分析。结果包括分类算法中每个描述符组合的恶性概率。
65 个病变为恶性，59 个为良性。在所有定量指标中，最大 SWV（SWV）和 RI 被纳入分类算法，该算法显示有三个分支的深度（SWV≤或>3.16；如果 SWV≤3.16，则 RI≤0.66、0.66-0.77 或>0.77；如果 RI≤0.66，则 SWV≤或>2.71）。分类算法的 AUC 为 0.887（95%CI 0.818-0.937，p<0.0001），灵敏度为 98.46%（95%CI 91.7-100%），特异性为 61.02%（95%CI 47.4-73.5%）。应用所提出的算法，可以避免 61%的病例进行假阳性活检。
纳入两个定量 US 参数（SWV 和 RI）的简单分类算法具有较高的诊断性能，能够准确地区分良性和恶性乳腺病变，并将所有病例中不必要的乳腺活检数量降低多达 60%，避免任何主观解释偏见。
