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多参数MRI纹理分析在区域特异性高级别前列腺癌诊断中的应用
作者:丁怀军1  柏根基2  李虎1  赵志勇1  杨巨欢1 
单位:1. 淮安市洪泽区人民医院 影像科, 江苏 淮安 223100;
2. 淮安市第一人民医院 影像科, 江苏 淮安 223000
关键词:前列腺癌 多参数磁共振成像 纹理分析 前列腺成像报告 数据系统 
分类号:R445.21;R737.25
出版年·卷·期(页码):2022·50·第五期(543-548)
摘要:

目的:探讨基于多参数磁共振成像(MRI)纹理分析在区域特异性高级别前列腺癌(PCa)诊断中的应用价值。方法:选择2018年10月至2020年10月入院经穿刺活检病理确诊PCa患者共55例,其中高级别25例、非高级别30例,移行带22例、周围带33例。所有患者接受1.5 T MRI扫描序列包括轴向T2加权成像、扩散加权成像(DWI)和动态对比增强(DCE),定量参数包括前列腺成像报告和数据系统(PI-RADSv2)及纹理分析,其中纹理分析包括一阶参数(偏度和峰度)和二阶参数(能量、熵、相关性和惯性)。结果:在所有患者和高级别患者中,PI-RADSv2(移行带和周围带)和纹理分析参数均有较好的一致性(P>0.05)。高级别患者比非高级别患者PI-RADSv2评分升高,峰度和能量降低,偏度、熵和惯性增加(P<0.05)。单因素和多因素Logistic回归分析显示,纹理分析参数偏度、能量和惯性是诊断高级别PCa的重要因素(P<0.05)。受试者工作特征(ROC)曲线分析显示,PI-RADSv2评分和纹理分析Logistic模型诊断高级别PCa、鉴别移行带与周围带有较高的效能(P<0.05)。结论:基于多参数MRI纹理分析在诊断高级别PCa、鉴别移行带与周围带中有重要的应用价值。

Objective:To explore the application value of multiparameter magnetic resonance imaging(MRI) texture analysis in the diagnosis of regional specific high-grade prostate cancer(PCa). Methods:From October 2018 to October 2020,55 patients with PCa confirmed by biopsy were chosen, including 25 patients of high grade and 30 patients of non-high grade PCa,22 patients in the transitional zone and 33 patients in the peripheral zone. All patients underwent 1.5T MRI scanning sequences, including axial T2 weighted imaging, diffusion weighted imaging(DWI) and dynamic contrast enhancement(DCE). Quantitative parameters included prostate imaging reporting and data system(PI-RADSv2) and texture analysis. Texture analysis included first-order parameters(skewness and kurtosis) and second-order parameters(energy, entropy, correlation and inertia). Results:In all the patients and high-grade PCa, PI-RADSv2(transitional zone and peripheral zone) and texture analysis parameters had a good consistency(P>0.05). Compared with non-high grade patients, high grade patients had higher PI-RADSv2 score, lower kurtosis and energy, higher skewness, entropy and inertia(P<0.05). Univariate and multivariate logistic regression analysis showed that skewness, energy and inertia of texture analysis were important factors in the diagnosis of high-grade PCa(P<0.05). Receiver operating characteristic(ROC) curve analysis showed that PI-RADSv2 score and texture analysis logistic model had higher efficiency in diagnosing high-grade PCa and distinguishing transitional zone from peripheral zone(P<0.05). Conclusion:Multiparameter MRI texture analysis is of great application value in the diagnosis of high-grade PCa and the differentiation of transitional zone from peripheral zone.

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