Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Venusberg-Campus 1, 53127, Bonn, Germany.
Department of Radiotherapy and Radiation Oncology, University Hospital Bonn, Venusberg-Campus 1, 53127, Bonn, Germany.
Sci Rep. 2021 Nov 23;11(1):22752. doi: 10.1038/s41598-021-01108-6.
This study investigated the impact of different ROI placement and analysis methods on the diagnostic performance of simplified IVIM-DWI for differentiating liver lesions. 1.5/3.0-T DWI data from a respiratory-gated MRI sequence (b = 0, 50, 250, 800 s/mm) were analyzed in patients with malignant (n = 74/54) and benign (n = 35/19) lesions. Apparent diffusion coefficient ADC = ADC(0,800) and IVIM parameters D' = ADC(50,800), D' = ADC(250,800), f' = f(0,50,800), f' = f(0,250,800), and D*' = D*(0,50,250,800) were calculated voxel-wise. For each lesion, a representative 2D-ROI, a 3D-ROI whole lesion, and a 3D-ROI from "good" slices were placed, including and excluding centrally deviating areas (CDA) if present, and analyzed with various histogram metrics. The diagnostic performance of 2D- and 3D-ROIs was not significantly different; e.g. AUC (ADC/D'/f') were 0.958/0.902/0.622 for 2D- and 0.942/0.892/0.712 for whole lesion 3D-ROIs excluding CDA at 1.5 T (p > 0.05). For 2D- and 3D-ROIs, AUC (ADC/D'/D') were significantly higher, when CDA were excluded. With CDA included, AUC (ADC/D'/D'/f'/D*') improved when low percentiles were used instead of averages, and was then comparable to the results of average ROI analysis excluding CDA. For lesion differentiation the use of a representative 2D-ROI is sufficient. CDA should be excluded from ROIs by hand or automatically using low percentiles of diffusion coefficients.
本研究旨在探讨不同 ROI 放置和分析方法对简化 IVIM-DWI 鉴别肝脏病变的诊断性能的影响。对呼吸门控 MRI 序列（b=0、50、250、800 s/mm）的 1.5/3.0-T DWI 数据进行分析，纳入恶性病变（n=74/54）和良性病变（n=35/19）患者。计算表观扩散系数 ADC=ADC(0,800) 和 IVIM 参数 D'=ADC(50,800)、D'=ADC(250,800)、f'=f(0,50,800)、f'=f(0,250,800) 和 D*'=D*(0,50,250,800)。对每个病灶，放置一个有代表性的 2D-ROI、一个全病灶 3D-ROI 和一个“良好”层面的 3D-ROI，如有中央偏离区（CDA）则包括或排除 CDA，并采用各种直方图指标进行分析。2D-和 3D-ROI 的诊断性能无显著差异；例如，在 1.5 T 时，排除 CDA 后，2D-ROI 的 AUC（ADC/D'/f'）为 0.958/0.902/0.622，全病灶 3D-ROI 为 0.942/0.892/0.712（p>0.05）。对于 2D-和 3D-ROI，当排除 CDA 时，AUC（ADC/D'/D'）更高。当使用低百分位数代替平均值时，纳入 CDA 后，AUC（ADC/D'/D'/f'/D*'）提高，与排除 CDA 的平均 ROI 分析结果相当。对于病变鉴别，使用有代表性的 2D-ROI 即可。应通过手动或自动使用扩散系数的低百分位数从 ROI 中排除 CDA。
