在3T时用于检测和评估前列腺癌的严重程度的Intravoxel不连贯运动和扩散曲解成像及其基于机器学习的纹理分析
Chandan J Das1, Archana Vadiraj Malagi2, Raju Sharma1
1Department of Radio-Diagnosis and Interventional Radiology, All India Institute of Medical Sciences, New Delhi, India.
NMR in biomedicine
|April 1, 2024
概括
通过使用Intravoxel的不连贯运动和扩散曲解成像 (IVIM-DKI) 和纹理分析,有效地检测前列腺癌 (PCa) 并区分其严重程度. 综合的IVIM-DKI参数显示PCa,良性前列腺增生和正常组织的分类具有很高的准确性.
科学领域:
- 放射学 放射学是一门学科.
- 在瘤学瘤学.
- 医疗成像医学成像
背景情况:
- 前列腺癌 (PCa) 检测和严重程度评估对于患者管理至关重要.
- 传统的成像方法在准确表征PCa时存在局限性.
- 像IVIM-DKI这样的先进的MRI技术为改善诊断性能提供了潜力.
研究的目的:
- 评估intravoxel联合不连贯运动和扩散曲解成像 (IVIM-DKI) 和基于机器学习的纹理分析的作用.
- 评估IVIM-DKI参数在检测前列腺癌 (PCa) 和区分其严重程度方面的能力.
- 为了探索PCa分类的IVIM-DKI衍生的纹理特征的诊断准确性.
主要方法:
- 88名患者接受了3TMRI,在13b值中采用IVIM-DKI采集.
- 用IVIM-DKI模型与总变化 (TV) 方法分析数据.
- 机器学习,包括人工神经网络,用于使用选定的纹理特征检测PCa.
主要成果:
- IVIM-DKI参数 (ADC,D,k) 显著区分PCa与良性前列腺增生症 (BPH) 和正常外周区域 (PZ) 具有高AUC (0.88-0.92).
- 较低的ADC和D值,较高的皮质变化 (k),与较高的格里森分数 (GS) 相相关.
- 结合IVIM-DKI纹理分析实现了0.83的高AUC,用于分类PCa,BPH和正常PZ.
结论:
- 使用TV方法的IVIM-DKI模型有效地将PCa与BPH和正常PZ区分开来.
- 来自IVIM-DKI参数组合的纹理特征表明PCa检测的高精度和AUC.
- 这种方法有望改善前列腺癌的非侵入性评估.
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