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简化体素内不相干运动(IVIM)用于肝脏病变特征分析的不同 ROI 分析方法比较。

Comparison of different ROI analysis methods for liver lesion characterization with simplified intravoxel incoherent motion (IVIM).

机构信息

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.

出版信息

Sci Rep. 2021 Nov 23;11(1):22752. doi: 10.1038/s41598-021-01108-6.

Abstract

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.

摘要

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。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/21ea/8610969/7eabd386011e/41598_2021_1108_Fig1_HTML.jpg

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