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-现代医学

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

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

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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