使用临床和放射学成像指标进行高级机器学习信息学建模，以通过OncotypeDX基因阵列表征乳腺肿瘤特征
Advanced machine learning informatics modeling using clinical and radiological imaging metrics for characterizing breast tumor characteristics with the OncotypeDX gene array
摘要：
目的-最佳使用已建立的成像方法，如多参数磁共振成像（mpMRI），可同时识别关键功能参数并提供乳腺癌的独特成像表型。因此，我们开发并实施了一种新的机器学习信息系统，该系统整合了来自成像和临床健康记录的临床变量，以与21基因阵列检测OncotypeDX进行比较。材料和方法-我们在一组ER+患者（n=81）中测试了我们的信息学模型，这些患者接受了OncotypeDX基因表达和乳腺mpMRI检测。机器学习信息学方法被称为集成放射组学信息系统-IRIS，应用于mpMRI、临床和病理描述符以及基因阵列分析。IRIS方法使用先进的图论模型和定量指标。获得定量成像参数的汇总统计量（平均值和标准差）。计算患者分类的灵敏度和特异性以及曲线下面积。结果：IRIS模型对OncotypeDX分类的敏感性为95%，特异性为89%，AUC为0.92。高风险组的乳腺病变尺寸较大，低风险组和中等风险组的乳腺病变尺寸较低。各组PK-DCE和ADC图值差异有统计学意义。高、中危组的ADC图值显著低于低危组。结论-这些初步研究提供了对影像学特征和分子基因阵列OncotypeDX评分的更深入的理解。这一见解为将这些成像特征与治疗反应评估联系起来以改善个性化医疗提供了基础。
英文摘要：
Purpose-Optimal use of established and imaging methods, such as multiparametric magnetic resonance imaging(mpMRI) can simultaneously identify key functional parameters and provide unique imaging phenotypes of breast cancer. Therefore, we have developed and implemented a new machine-learning informatic system that integrates clinical variables, derived from imaging and clinical health records, to compare with the 21-gene array assay, OncotypeDX. Materials and methods-We tested our informatics modeling in a subset of patients (n=81) who had ER+ disease and underwent OncotypeDX gene expression and breast mpMRI testing. The machine-learning informatic method is termed Integrated Radiomic Informatic System-IRIS was applied to the mpMRI, clinical and pathologic descriptors, as well as a gene array analysis. The IRIS method using an advanced graph theoretic model and quantitative metrics. Summary statistics (mean and standard deviations) for the quantitative imaging parameters were obtained. Sensitivity and specificity and Area Under the Curve were calculated for the classification of the patients. Results-The OncotypeDX classification by IRIS model had sensitivity of 95% and specificity of 89% with AUC of 0.92. The breast lesion size was larger for the high-risk groups and lower for both low risk and intermediate risk groups. There were significant differences in PK-DCE and ADC map values in each group. The ADC map values for high- and intermediate-risk groups were significantly lower than the low-risk group. Conclusion-These initial studies provide deeper understandings of imaging features and molecular gene array OncotypeDX score. This insight provides the foundation to relate these imaging features to the assessment of treatment response for improved personalized medicine.
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