论著摘要 |【Radiomics-MR】使用背景实质增强异质性对动态对比增强MRI识别三重阴性乳腺癌:先导式放射学研究(双语版)

2018-05-14 19:32:00 admin
标签:   影像组学 乳腺癌 三阴性癌症

Identifying Triple-Negative Breast Cancer Using Background Parenchymal Enhancement Heterogeneity on Dynamic Contrast-Enhanced MRI: A Pilot Radiomics Study.

发表日期: 2015.11.24   来源:PLoS One. 2015 Nov 24;10(11):e0143308.

作者:

Wang J1,2, Kato F3, Oyama-Manabe N2,3, Li R2,4, Cui Y2, Tha KK1,2, Yamashita H5, Kudo K2,3, Shirato H1,2.

作者介绍:

1. Department of Radiation Medicine, Hokkaido University Graduate School of Medicine, North 15 West 7 Kita-ku, Sapporo, Hokkaido, 060-8638, Japan.

2. Global Station for Quantum Medical Science and Engineering, Global Institution for Collaborative Research and Education (GI-CoRE), Hokkaido University, Proton Beam Therapy Center, North 14 West 5 Kita-ku, Sapporo, Hokkaido, 060-8648, Japan.

3. Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, North 14 West 5 Kita-ku, Sapporo, Hokkaido, 060-8648, Japan.

4. Department of Radiation Oncology, Stanford University School of Medicine, 291 Campus Drive, Li Ka Shing Building, Stanford, CA 94305, United States of America.

5. Department of Breast Surgery, Hokkaido University Hospital, North 14 West 5 Kita-ku, Sapporo, Hokkaido, 060-8648, Japan.

摘要

Abstact

目标

在“三阴性”乳腺癌的鉴定过程中,除了肿瘤本身大小对3.0特斯拉对比度增强(DCE)的MRI的影响,我们还想鉴定背景实质增强的详细定量表征的添加的分类价值。

Objective

To determine the added discriminative value of detailed quantitative characterization of background parenchymal enhancement in addition to the tumor itself on dynamic contrast-enhanced (DCE) MRI at 3.0 Tesla in identifying “triple-negative" breast cancers.

材料和方法

在该机构审查委员会批准的回顾性研究中,放射科医师评估了84名患有88例侵袭性癌症的妇女的DCE-MRI,并使用定量计算机辅助技术进行分析。 每个肿瘤及其周围的实质在3-D中半自动分割。从两个地区总共提取了85个成像特征,包括增强的形态学,光密度和统计学的纹理测量量。使用有效的连续前向浮动搜索算法选择最优特征的一小部分。为了区分三阴性癌与其他亚型,我们构建了基于支持向量机的预测模型。使用交叉验证,用受试者工作特征曲线下的面积(AUC)评估其分类性能。

Material and Methods

In this Institutional Review Board-approved retrospective study, DCE-MRI of 84 women presenting 88 invasive carcinomas were evaluated by a radiologist and analyzed using quantitative computer-aided techniques. Each tumor and its surrounding parenchyma were segmented semi-automatically in 3-D. A total of 85 imaging features were extracted from the two regions, including morphologic, densitometric, and statistical texture measures of enhancement. A small subset of optimal features was selected using an efficient sequential forward floating search algorithm. To distinguish triple-negative cancers from other subtypes, we built predictive models based on support vector machines. Their classification performance was assessed with the area under receiver operating characteristic curve (AUC) using cross-validation.

结果

根据现有的现有技术,基于肿瘤区域的成像特征在区别其他三阴性癌症时,AUC达到了0.782。当包括背景实质增强特征时,AUC显着增加至0.878(p < 0.01)。在几乎所有进行的亚型分类任务中都看到类似的改进。值得注意的是,预测三阴性癌症的最具特色的特征是背景实质增强的纹理。

Results

Imaging features based on the tumor region achieved an AUC of 0.782 in differentiating triple-negative cancers from others, in line with the current state of the art. When background parenchymal enhancement features were included, the AUC increased significantly to 0.878 (p < 0.01). Similar improvements were seen in nearly all subtype classification tasks undertaken. Notably, amongst the most discriminating features for predicting triple-negative cancers were textures of background parenchymal enhancement.

结论

考虑到肿瘤及其周围DCE-MRI上实质,用于放射图像表型,为鉴定三阴性乳腺癌提供了有用的信息。背景实质增强的异质性,其特征在于DCE-MRI上的定量纹理特征,为这种分化模型增加价值,因为它们与三阴性亚型强烈相关。预期的验证研究有助于确认这些发现并确定潜在的影响。

Conclusions

Considering the tumor as well as its surrounding parenchyma on DCE-MRI for radiomic image phenotyping provides useful information for identifying triple-negative breast cancers. Heterogeneity of background parenchymal enhancement, characterized by quantitative texture features on DCE-MRI, adds value to such differentiation models as they are strongly associated with the triple-negative subtype. Prospective validation studies are warranted to confirm these findings and determine potential implications.

阅读原文:PMID: 26600392  PMCID: PMC4658011  DOI: 10.1371/journal.pone.0143308


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