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T 加权 MRI 纹理分析在评估外周区前列腺癌侵袭性中的应用:一项单臂、多中心研究。

Utility of T-weighted MRI texture analysis in assessment of peripheral zone prostate cancer aggressiveness: a single-arm, multicenter study.

机构信息

Department of Circulation and Medical Imaging, NTNU-Norwegian University of Science and Technology, Trondheim, Norway.

Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway.

出版信息

Sci Rep. 2021 Jan 22;11(1):2085. doi: 10.1038/s41598-021-81272-x.

DOI:10.1038/s41598-021-81272-x
PMID:33483545
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7822867/
Abstract

T-weighted (TW) MRI provides high spatial resolution and tissue-specific contrast, but it is predominantly used for qualitative evaluation of prostate anatomy and anomalies. This retrospective multicenter study evaluated the potential of TW image-derived textural features for quantitative assessment of peripheral zone prostate cancer (PCa) aggressiveness. A standardized preoperative multiparametric MRI was performed on 87 PCa patients across 6 institutions. TW intensity and apparent diffusion coefficient (ADC) histogram, and TW textural features were computed from tumor volumes annotated based on whole-mount histology. Spearman correlations were used to evaluate association between textural features and PCa grade groups (i.e. 1-5). Feature utility in differentiating and classifying low-(grade group 1) vs. intermediate/high-(grade group ≥ 2) aggressive cancers was evaluated using Mann-Whitney U-tests, and a support vector machine classifier employing "hold-one-institution-out" cross-validation scheme, respectively. Textural features indicating image homogeneity and disorder/complexity correlated significantly (p < 0.05) with PCa grade groups. In the intermediate/high-aggressive cancers, textural homogeneity and disorder/complexity were significantly lower and higher, respectively, compared to the low-aggressive cancers. The mean classification accuracy across the centers was highest for the combined ADC and TW intensity-textural features (84%) compared to ADC histogram (75%), TW histogram (72%), TW textural (72%) features alone or TW histogram and texture (77%), TW and ADC histogram (79%) combined. Texture analysis of TW images provides quantitative information or features that are associated with peripheral zone PCa aggressiveness and can augment their classification.

摘要

T 加权(TW)MRI 提供了高空间分辨率和组织特异性对比,但主要用于前列腺解剖结构和异常的定性评估。本回顾性多中心研究评估了 TW 图像衍生纹理特征在定量评估外周带前列腺癌(PCa)侵袭性方面的潜力。在 6 家机构对 87 例 PCa 患者进行了标准化的术前多参数 MRI 检查。从基于全组织学的肿瘤体积标注中计算了 TW 强度和表观扩散系数(ADC)直方图以及 TW 纹理特征。使用 Spearman 相关分析评估纹理特征与 PCa 分级组(即 1-5 级)之间的相关性。使用 Mann-Whitney U 检验评估了纹理特征在区分和分类低(分级组 1)与中/高(分级组≥2)侵袭性癌症方面的作用,并使用支持向量机分类器,采用“一机构外留一机构”交叉验证方案。指示图像均匀性和无序/复杂性的纹理特征与 PCa 分级组显著相关(p<0.05)。在中/高侵袭性癌症中,与低侵袭性癌症相比,纹理均匀性和无序/复杂性分别显著降低和升高。跨中心的平均分类准确率以 ADC 和 TW 强度纹理特征的组合最高(84%),其次是 ADC 直方图(75%)、TW 直方图(72%)、TW 纹理(72%)特征单独或 TW 直方图和纹理(77%)、TW 和 ADC 直方图(79%)组合。TW 图像的纹理分析提供了与外周带 PCa 侵袭性相关的定量信息或特征,可增强其分类。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c8f4/7822867/79c63c1d9d28/41598_2021_81272_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c8f4/7822867/a810c6ae1a0f/41598_2021_81272_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c8f4/7822867/7aef4f349bfe/41598_2021_81272_Fig2_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c8f4/7822867/fa7931124a13/41598_2021_81272_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c8f4/7822867/79c63c1d9d28/41598_2021_81272_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c8f4/7822867/a810c6ae1a0f/41598_2021_81272_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c8f4/7822867/7aef4f349bfe/41598_2021_81272_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c8f4/7822867/0afa5d2fdb7d/41598_2021_81272_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c8f4/7822867/fa7931124a13/41598_2021_81272_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c8f4/7822867/79c63c1d9d28/41598_2021_81272_Fig5_HTML.jpg

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2
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Radiology. 2020 May;295(2):328-338. doi: 10.1148/radiol.2020191145. Epub 2020 Mar 10.
3
Radiomics and machine learning of multisequence multiparametric prostate MRI: Towards improved non-invasive prostate cancer characterization.
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4
Combining multi-parametric MRI radiomics features with tumor abnormal protein to construct a machine learning-based predictive model for prostate cancer.结合多参数MRI影像组学特征与肿瘤异常蛋白构建基于机器学习的前列腺癌预测模型。
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5
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6
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7
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