Quantitative MRI Radiomics in the Prediction of Molecular Classifications of Breast Cancer Subtypes in the TCGA/TCIA Data Set.
发表日期： 2016.05.11 来源：Npj Breast Cancer, 2016, 2:16012.
Li H1, Zhu Y2, Burnside ES3, Huang E4, Drukker K1, Hoadley KA5, Fan C5, Conzen SD6, Zuley M7, Net JM8, Sutton E9, Whitman GJ10, Morris E9, Perou CM5, Ji Y11, Giger ML1.
1. Department of Radiology, The University of Chicago, Chicago, IL, USA.
2. Program of Computational Genomics & Medicine, NorthShore University HealthSystem, Evanston, IL, USA.
3. Department of Radiology, University of Wisconsin-Madison, Madison, WI, USA.
4. National Cancer Institute, Cancer Imaging Program, Bethesda, MA, USA.
5. Department of Genetics, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA.
6. Department of Medicine, The University of Chicago, Chicago, IL, USA.
7. Department of Radiology, University of Pittsburgh, Pittsburgh, PA, USA.
8. Department of Radiology, University of Miami Health System, Miami, FL, USA.
9. Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
10. Department of Radiology, MD Anderson Cancer Center, Houston, TX, USA.
11. Program of Computational Genomics & Medicine, NorthShore University HealthSystem, Evanston, IL, USA; Department of Public Health Sciences, University of Chicago, Chicago, IL, USA.
使用定量辐射计，我们证明基于肿瘤表型的计算机提取的磁共振（MR）图像可以预测侵袭性乳腺癌的分子分类。 对来自国家癌症研究所多机构TCGA / TCIA的91例经活检证实浸润性乳腺癌的MRI进行放射学分析。进行免疫组织化学分子分类，包括雌激素受体，孕酮受体，人表皮生长因子受体2，84例分子亚型（正常样，管腔A，管腔B，富含HER2和基底样）。计算机化定量图像分析包括：三维病变分割，表型提取以及涉及逐步特征选择和线性判别分析的离开一案例交叉验证。 使用接收器工作特性分析评估分子亚型分类器模型的性能。计算机提取的肿瘤表型能够区分分子预后指标; 在区分ER +与ER-，PR +与PR-，HER2 +对HER2-和三阴性对照的任务中ROC曲线下的区域分别为0.89,0.69,0.65和0.67。 观察到肿瘤表型与受体状态之间的统计学显著相关。更具侵略性的癌症可能在尺寸上更大，对比度增强具有更多的异质性。即使在控制肿瘤大小后，就整体数据集（P=0.006）而言，对于增强纹理（熵）和分子亚型（正常样，腔A，腔B，富HER2，基底样）的关系，在每个大小的组中（（P = 0.04，病变≤2cm；P = 0.02，病变> 2〜≤5cm）观察到统计学上显着的趋势。总之，计算机提取的图像表型显示了对乳腺癌亚型的高通量鉴别的希望，并且可能产生推进精密医学的定量预测特征。
Using quantitative radiomics, we demonstrate that computer-extracted magnetic resonance (MR) image-based tumor phenotypes can be predictive of the molecular classification of invasive breast cancers. Radiomics analysis was performed on 91 MRIs of biopsy-proven invasive breast cancers from National Cancer Institute’s multi-institutional TCGA/TCIA. Immunohistochemistry molecular classification was performed including estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2, and for 84 cases, the molecular subtype (normal-like, luminal A, luminal B, HER2-enriched, and basal-like). Computerized quantitative image analysis included: three-dimensional lesion segmentation, phenotype extraction, and leave-one-case-out cross validation involving stepwise feature selection and linear discriminant analysis. The performance of the classifier model for molecular subtyping was evaluated using receiver operating characteristic analysis. The computer-extracted tumor phenotypes were able to distinguish between molecular prognostic indicators; area under the ROC curve values of 0.89, 0.69, 0.65, and 0.67 in the tasks of distinguishing between ER+ versus ER−, PR+ versus PR−, HER2+ versus HER2−, and triple-negative versus others, respectively. Statistically significant associations between tumor phenotypes and receptor status were observed. More aggressive cancers are likely to be larger in size with more heterogeneity in their contrast enhancement. Even after controlling for tumor size, a statistically significant trend was observed within each size group (P = 0.04 for lesions ≤ 2 cm; P = 0.02 for lesions >2 to ≤5 cm) as with the entire data set (P-value = 0.006) for the relationship between enhancement texture (entropy) and molecular subtypes (normal-like, luminal A, luminal B, HER2-enriched, basal-like). In conclusion, computer-extracted image phenotypes show promise for high-throughput discrimination of breast cancer subtypes and may yield a quantitative predictive signature for advancing precision medicine.