Department of Radiology, Weill Medical College of Cornell University, New York, NY, United States of America; Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY, United States of America.
Department of Radiology, Weill Medical College of Cornell University, New York, NY, United States of America.
Magn Reson Imaging. 2022 Feb;86:86-93. doi: 10.1016/j.mri.2021.10.039. Epub 2021 Nov 6.
To test the feasibility of using quantitative transport mapping (QTM) method, which is based on the inversion of transport equation using spatial deconvolution without any arterial input function, for automatically postprocessing dynamic contrast enhanced MRI (DCE-MRI) to differentiate malignant and benign breast tumors.
Breast DCE-MRI data with biopsy confirmed malignant (n = 13) and benign tumors (n = 13) was used to assess QTM velocity (|u|) and diffusion coefficient (D), volume transfer constant (K), volume fraction of extravascular extracellular space (V) from kinetics method, and traditional enhancement curve characteristics (ECC: amplitude A, wash-in rate α, wash-out rate β). A Mann-Whitney U test and receiver operating characteristic curve (ROC) analysis were performed to assess the diagnostic performance of these parameters for distinguishing between benign and malignant tumors.
Between malignant and benign tumors, there was a significant difference in |u| and K, (p = 0.0066, 0.0274, respectively), but not in D, V, A, α and β (p = 0.1119, 0.2382, 0.4418,0.2592 and 0.9591, respectively). ROC area-under-the-curve was 0.82, 0.75 (95% confidence level 0.60-0.95, 0.51-0.90) for |u| and K, respectively.
QTM postprocesses DCE-MRI automatically through deconvolution in space and time to solve the inverse problem of the transport equation. Comparing with traditional kinetics method and ECC, QTM method showed better diagnostic accuracy in differentiating benign from malignant breast tumors in this study.
测试基于空间解卷积的定量转运映射(QTM)方法的可行性,该方法无需任何动脉输入函数即可反转转运方程,用于自动处理动态对比增强 MRI(DCE-MRI)以区分恶性和良性乳腺肿瘤。
使用经活检证实为恶性(n=13)和良性肿瘤(n=13)的乳腺 DCE-MRI 数据来评估 QTM 速度(|u|)和扩散系数(D)、容积转移常数(K)、血管外细胞外空间容积分数(V),采用动力学方法和传统增强曲线特征(ECC:幅度 A、上升斜率α、下降斜率β)。采用 Mann-Whitney U 检验和受试者工作特征曲线(ROC)分析评估这些参数用于区分良恶性肿瘤的诊断性能。
恶性和良性肿瘤之间,|u|和 K 存在显著差异(p=0.0066,0.0274),但 D、V、A、α和β无差异(p=0.1119,0.2382,0.4418,0.2592 和 0.9591)。ROC 曲线下面积为 0.82,0.75(95%置信区间 0.60-0.95,0.51-0.90),用于|u|和 K。
QTM 通过空间和时间的解卷积自动处理 DCE-MRI,以解决转运方程的反问题。与传统动力学方法和 ECC 相比,QTM 方法在本研究中区分良恶性乳腺肿瘤的诊断准确性更高。