MR Imaging Radiomics Signatures for Predicting the Risk of Breast Cancer Recurrence as Given by Research Versions of MammaPrint, Oncotype DX, and PAM50 Gene Assays.
MR Imaging Radiomics Signatures for Predicting the Risk of Breast Cancer Recurrence as Given by Research Versions of MammaPrint, Oncotype DX, and PAM50 Gene Assays.
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DOI：
10.1148/radiol.2016152110
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发表时间：
2016-11
期刊：
影响因子：
19.7
通讯作者：
Giger ML
中科院分区：
文献类型：
--
作者：
Li H;Zhu Y;Burnside ES;Drukker K;Hoadley KA;Fan C;Conzen SD;Whitman GJ;Sutton EJ;Net JM;Ganott M;Huang E;Morris EA;Perou CM;Ji Y;Giger ML
研究计算机提取的乳房磁共振（MR）成像表型与多基因测定的MammaPrint、Oncotype DX和PAM50之间的关系，以评估放射组学在评估乳腺癌复发风险中的作用。对来自美国国家癌症研究所癌症影像档案的84例未识别的多机构乳腺MR检查的回顾性数据集以及来自癌症基因组图谱的临床、组织病理学和基因组数据进行了分析。活检证实的浸润性乳腺癌包括74例（88%）导管癌，8例（10%）小叶癌和2例（2%）混合性癌。其中，雌激素受体阳性73例（87%），孕激素受体阳性67例（80%），人表皮生长因子受体2阳性19例（23%）。对于每个病例，计算机放射组学的MR图像产生计算机提取的肿瘤表型的大小，形状，边缘形态，增强纹理和动力学评估。进行回归和受试者操作特征分析，以评估相对于多基因分析分类的MR放射组学特征的预测能力。多元线性回归分析显示放射组学特征与多基因检测复发评分之间存在显著相关性（R2 = 0.25-0.32, r = 0.5-0.56, P < 0.0001）。重要的放射组学特征包括肿瘤大小和增强纹理，表明肿瘤的异质性。在区分预后好坏的任务中，放射组学对MammaPrint、Oncotype DX、基于亚型的PAM50复发风险、基于亚型和增殖的PAM50复发风险的受试者工作特征曲线下面积分别为0.88（标准误差，0.05）、0.76（标准误差，0.06）、0.68（标准误差，0.08）和0.55（标准误差，0.09），除后者外，均有统计学差异。定量乳腺磁共振成像放射组学显示了基于图像的表型评估乳腺癌复发风险的希望。
To investigate relationships between computer-extracted breast magnetic resonance (MR) imaging phenotypes with multigene assays of MammaPrint, Oncotype DX, and PAM50 to assess the role of radiomics in evaluating the risk of breast cancer recurrence. Analysis was conducted on an institutional review board–approved retrospective data set of 84 deidentified, multi-institutional breast MR examinations from the National Cancer Institute Cancer Imaging Archive, along with clinical, histopathologic, and genomic data from The Cancer Genome Atlas. The data set of biopsy-proven invasive breast cancers included 74 (88%) ductal, eight (10%) lobular, and two (2%) mixed cancers. Of these, 73 (87%) were estrogen receptor positive, 67 (80%) were progesterone receptor positive, and 19 (23%) were human epidermal growth factor receptor 2 positive. For each case, computerized radiomics of the MR images yielded computer-extracted tumor phenotypes of size, shape, margin morphology, enhancement texture, and kinetic assessment. Regression and receiver operating characteristic analysis were conducted to assess the predictive ability of the MR radiomics features relative to the multigene assay classifications. Multiple linear regression analyses demonstrated significant associations (R2 = 0.25–0.32, r = 0.5–0.56, P < .0001) between radiomics signatures and multigene assay recurrence scores. Important radiomics features included tumor size and enhancement texture, which indicated tumor heterogeneity. Use of radiomics in the task of distinguishing between good and poor prognosis yielded area under the receiver operating characteristic curve values of 0.88 (standard error, 0.05), 0.76 (standard error, 0.06), 0.68 (standard error, 0.08), and 0.55 (standard error, 0.09) for MammaPrint, Oncotype DX, PAM50 risk of relapse based on subtype, and PAM50 risk of relapse based on subtype and proliferation, respectively, with all but the latter showing statistical difference from chance. Quantitative breast MR imaging radiomics shows promise for image-based phenotyping in assessing the risk of breast cancer recurrence.
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DOI：
10.1186/s13058-014-0424-8
发表时间：
2014
期刊：
Breast cancer research : BCR
影响因子：
--
作者：
Gierach GL;Li H;Loud JT;Greene MH;Chow CK;Lan L;Prindiville SA;Eng-Wong J;Soballe PW;Giambartolomei C;Mai PL;Galbo CE;Nichols K;Calzone KA;Olopade OI;Gail MH;Giger ML
通讯作者：
Giger ML
影响因子：
2.7
作者：
Fan C;Prat A;Parker JS;Liu Y;Carey LA;Troester MA;Perou CM
通讯作者：
Perou CM
影响因子：
19.7
作者：
Bhooshan, Neha;Giger, Maryellen L.;Newstead, Gillian M.
通讯作者：
Newstead, Gillian M.
影响因子：
4.4
作者：
Clark, Kenneth;Vendt, Bruce;Prior, Fred
通讯作者：
Prior, Fred
影响因子：
3.8
作者：
Li, Hui;Giger, Maryellen L.;Di Rienzo, Anna
通讯作者：
Di Rienzo, Anna