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前列腺多参数磁共振成像的病理与纹理特征之间的关联

Association between pathology and texture features of multi parametric MRI of the prostate.

作者信息

Kuess Peter, Andrzejewski Piotr, Nilsson David, Georg Petra, Knoth Johannes, Susani Martin, Trygg Johan, Helbich Thomas H, Polanec Stephan H, Georg Dietmar, Nyholm Tufve

机构信息

Division of Medical Radiation Physics, Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria. Christian Doppler Laboratory for Medical Radiation Research for Radiation Oncology, Vienna, Austria.

出版信息

Phys Med Biol. 2017 Sep 21;62(19):7833-7854. doi: 10.1088/1361-6560/aa884d.

Abstract

The role of multi-parametric (mp)MRI in the diagnosis and treatment of prostate cancer has increased considerably. An alternative to visual inspection of mpMRI is the evaluation using histogram-based (first order statistics) parameters and textural features (second order statistics). The aims of the present work were to investigate the relationship between benign and malignant sub-volumes of the prostate and textures obtained from mpMR images. The performance of tumor prediction was investigated based on the combination of histogram-based and textural parameters. Subsequently, the relative importance of mpMR images was assessed and the benefit of additional imaging analyzed. Finally, sub-structures based on the PI-RADS classification were investigated as potential regions to automatically detect maligned lesions. Twenty-five patients who received mpMRI prior to radical prostatectomy were included in the study. The imaging protocol included T2, DWI, and DCE. Delineation of tumor regions was performed based on pathological information. First and second order statistics were derived from each structure and for all image modalities. The resulting data were processed with multivariate analysis, using PCA (principal component analysis) and OPLS-DA (orthogonal partial least squares discriminant analysis) for separation of malignant and healthy tissue. PCA showed a clear difference between tumor and healthy regions in the peripheral zone for all investigated images. The predictive ability of the OPLS-DA models increased for all image modalities when first and second order statistics were combined. The predictive value reached a plateau after adding ADC and T2, and did not increase further with the addition of other image information. The present study indicates a distinct difference in the signatures between malign and benign prostate tissue. This is an absolute prerequisite for automatic tumor segmentation, but only the first step in that direction. For the specific identified signature, DCE did not add complementary information to T2 and ADC maps.

摘要

多参数(mp)MRI在前列腺癌诊断和治疗中的作用已显著增强。mpMRI视觉检查的一种替代方法是使用基于直方图的(一阶统计)参数和纹理特征(二阶统计)进行评估。本研究的目的是探讨前列腺良性和恶性子体积与从mpMR图像获得的纹理之间的关系。基于基于直方图的参数和纹理参数的组合研究了肿瘤预测的性能。随后,评估了mpMR图像的相对重要性并分析了额外成像的益处。最后,研究了基于PI-RADS分类的子结构作为自动检测恶性病变的潜在区域。本研究纳入了25例在根治性前列腺切除术前行mpMRI检查的患者。成像方案包括T2、DWI和DCE。根据病理信息进行肿瘤区域的勾画。从每个结构和所有图像模态中得出一阶和二阶统计量。使用主成分分析(PCA)和正交偏最小二乘判别分析(OPLS-DA)对所得数据进行多变量分析,以分离恶性和健康组织。PCA显示,在所有研究图像中,外周区肿瘤和健康区域之间存在明显差异。当一阶和二阶统计量结合时,OPLS-DA模型对所有图像模态的预测能力均有所提高。在添加ADC和T2后,预测值达到平台期,添加其他图像信息后未进一步增加。本研究表明恶性和良性前列腺组织的特征存在明显差异。这是自动肿瘤分割的绝对先决条件,但只是朝着这个方向迈出的第一步。对于特定识别的特征,DCE并未为T2和ADC图添加补充信息。

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