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.

中文翻译：

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定量MRI放射学可预测TCGA / TCIA数据集中乳腺癌亚型的分子分类。

使用定量放射组学，我们证明基于计算机提取的磁共振（MR）图像的肿瘤表型可以预测浸润性乳腺癌的分子分类。对来自国家癌症研究所多机构TCGA / TCIA的经活检证实的浸润性乳腺癌的91 MRI进行了放射学分析。进行了免疫组织化学分子分类，包括雌激素受体，孕激素受体，人表皮生长因子受体2，并且对于84例患者，进行了分子亚型（正常样，管腔A，管腔B，富含HER2的和基底样的）。计算机化的定量图像分析包括：三维病变分割，表型提取以及一站式交叉验证，包括逐步特征选择和线性判别分析。使用接收器操作特征分析评估了用于分子亚型的分类器模型的性能。计算机提取的肿瘤表型能够区分分子预后指标。在分别区分ER +与ER-，PR +与PR-，HER2 +与HER2-，三负与其他的任务中，ROC曲线值分别为0.89、0.69、0.65和0.67的面积。观察到肿瘤表型和受体状态之间的统计学显着关联。更具侵略性的癌症可能会变得更大，并在对比度增强方面具有更多的异质性。即使在控制了肿瘤大小之后，在每个大小组内也观察到了统计学上的显着趋势（对于≤2 cm的病变，P = 0.04；对于>> 2的病变，P = 0.02 2到≤5cm）以及整个数据集（P值= 0.006），以了解增强质感（熵）与分子亚型之间的关系（正常型，管腔A，管腔B，富含HER2的基底型） 。总之，计算机提取的图像表型显示出对乳腺癌亚型的高通量区分的希望，并可能为先进的精确医学产生定量的预测特征。