Suppr超能文献

利用杂交多维 MRI 对前列腺组织成分进行验证:与组织学发现的相关性。

Validation of Prostate Tissue Composition by Using Hybrid Multidimensional MRI: Correlation with Histologic Findings.

机构信息

From the Department of Radiology (A.C., C.M., A.Y., A.O., G.S.K.), Sanford J. Grossman Center of Excellence in Prostate Imaging and Image Guided Therapy (A.C., A.Y., A.O., G.S.K.), Human Tissue Resource Center (B.H.), Department of Pathology (T.A.), and Section of Urology, Department of Surgery (S.E.), University of Chicago, 5841 S Maryland Ave, MC 2026, Chicago, IL 60637; and Discipline of Medical Imaging Science, Sydney School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia (R.M.B.).

出版信息

Radiology. 2022 Feb;302(2):368-377. doi: 10.1148/radiol.2021204459. Epub 2021 Nov 9.

Abstract

Background Tissue estimates obtained by using microstructure imaging techniques, such as hybrid multidimensional (HM) MRI, may improve prostate cancer diagnosis but require histologic validation. Purpose To validate prostate tissue composition measured by using HM MRI, with quantitative histologic evaluation from whole-mount prostatectomy as the reference standard. Materials and Methods In this HIPAA-compliant study, from December 2016 to July 2018, prospective participants with biopsy-confirmed prostate cancer underwent 3-T MRI before radical prostatectomy. Axial HM MRI was performed with all combinations of echo times (57, 70, 150, and 200 msec) and values (0, 150, 750, and 1500 sec/mm). Data were fitted by using a three-compartment signal model to generate volumes for each tissue component (stroma, epithelium, lumen). Quantitative histologic evaluation was performed to calculate volume fractions for each tissue component for regions of interest corresponding to MRI. Tissue composition measured by using HM MRI and quantitative histologic evaluation were compared (paired test) and correlated (Pearson correlation coefficient), and agreement (concordance correlation) was assessed. Receiver operating characteristic curve analysis for cancer diagnosis was performed. Results Twenty-five participants (mean age, 60 years ± 7 [standard deviation]; 30 cancers and 45 benign regions of interest) were included. Prostate tissue composition measured with HM MRI and quantitative histologic evaluation did not differ (stroma, 45% ± 11 vs 44% ± 11 [ = .23]; epithelium, 31% ± 15 vs 34% ± 15 [ = .08]; and lumen, 24% ± 13 vs 22% ± 11 [ = .80]). Between HM MRI and histologic evaluation, there was excellent correlation (Pearson : overall, 0.91; stroma, 0.82; epithelium, 0.93; lumen, 0.90 [all < .05]) and agreement (concordance correlation coefficient: overall, 0.91; stroma, 0.81; epithelium, 0.90; and lumen, 0.87). High areas under the receiver operating characteristic curve obtained with HM MRI (0.96 for epithelium and 0.94 for lumen, < .001) and histologic evaluation (0.94 for epithelium and 0.88 for lumen, < .001) were found for differentiation between benign tissue and prostate cancer. Conclusion Tissue composition measured by using hybrid multidimensional MRI had excellent correlation with quantitative histologic evaluation as the reference standard. © RSNA, 2021 See also the editorial by Muglia in this issue.

摘要

背景 使用微观结构成像技术(如混合多维 MRI)获得的组织估计值可能会提高前列腺癌的诊断水平,但需要组织学验证。目的 利用全器官前列腺切除术的定量组织学评估作为参考标准,验证通过混合多维 MRI 测量的前列腺组织成分。 材料与方法 在这项符合 HIPAA 规定的研究中,2016 年 12 月至 2018 年 7 月,经活检证实患有前列腺癌的前瞻性参与者在接受根治性前列腺切除术前接受了 3-T MRI 检查。采用所有组合的回波时间(57、70、150 和 200 msec)和 值(0、150、750 和 1500 sec/mm)进行轴向混合多维 MRI。使用三房室信号模型拟合数据,以生成每个组织成分(基质、上皮、管腔)的体积。进行定量组织学评估,以计算与 MRI 相对应的感兴趣区域中每个组织成分的体积分数。(配对 t 检验)比较(配对 t 检验)和相关性(皮尔逊相关系数),并评估一致性(一致性相关)。对癌症诊断进行接收器工作特征曲线分析。结果 共纳入 25 名参与者(平均年龄 60 岁±7[标准差];30 个癌症和 45 个良性感兴趣区域)。用混合多维 MRI 测量的前列腺组织成分与定量组织学评估结果无差异(基质,45%±11 与 44%±11[ =.23];上皮,31%±15 与 34%±15[ =.08];管腔,24%±13 与 22%±11[ =.80])。HM MRI 与组织学评估之间具有极好的相关性(Pearson:总体,0.91;基质,0.82;上皮,0.93;管腔,0.90[均<0.05])和一致性(一致性相关系数:总体,0.91;基质,0.81;上皮,0.90;管腔,0.87)。HM MRI(上皮为 0.96,管腔为 0.94,均<0.001)和组织学评估(上皮为 0.94,管腔为 0.88,均<0.001)的接收器工作特征曲线下面积均较高,可区分良性组织和前列腺癌。结论 用混合多维 MRI 测量的组织成分与定量组织学评估作为参考标准具有极好的相关性。 请参阅本期杂志 Muglia 的社论。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/046f/8805656/d84d2f81de29/radiol.2021204459.va.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验