The Impact of Normalization Approaches to Automatically Detect Radiogenomic Phenotypes Characterizing Breast Cancer Receptors Status
The Impact of Normalization Approaches to Automatically Detect Radiogenomic Phenotypes Characterizing Breast Cancer Receptors Status
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DOI：
10.3390/cancers12020518
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发表时间：
2020-02-01
期刊：
影响因子：
5.2
通讯作者：
Franzese, Monica
中科院分区：
文献类型：
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作者：
Castaldo, Rossana;Pane, Katia;Franzese, Monica
在乳腺癌研究中，将定量放射组学与基因组特征相结合可以帮助识别和表征分子受体状态的放射基因组表型。生物医学成像处理缺乏放射组学特征归一化方法的标准，忽略特征归一化会使整体分析产生很大偏差。本研究评估了几种归一化技术预测四种临床表型的效果，如雌激素受体（ER），孕激素受体（PR），人表皮生长因子受体2（HER 2），和三阴性（TN）状态，通过定量特征。研究了91例浸润性乳腺癌T1加权动态对比增强MRI的癌症成像档案（TCIA）放射组学特征与癌症基因组图谱（TCGA）中乳腺浸润性癌miRNA表达谱的相关性。研究了三种先进的机器学习技术（支持向量机、随机森林和朴素贝叶斯），以区分分子预后指标，并分别实现了86%、93%、91%和91%的ROC曲线下面积（AUC）值，用于预测ER+与ER-、PR+与PR-、HER 2+与HER 2-和三阴性。总之，放射组学特征能够区分主要的乳腺癌分子亚型，并可能为推进精准医学提供潜在的成像生物标志物。
In breast cancer studies, combining quantitative radiomic with genomic signatures can help identifying and characterizing radiogenomic phenotypes, in function of molecular receptor status. Biomedical imaging processing lacks standards in radiomic feature normalization methods and neglecting feature normalization can highly bias the overall analysis. This study evaluates the effect of several normalization techniques to predict four clinical phenotypes such as estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and triple negative (TN) status, by quantitative features. The Cancer Imaging Archive (TCIA) radiomic features from 91 T1-weighted Dynamic Contrast Enhancement MRI of invasive breast cancers were investigated in association with breast invasive carcinoma miRNA expression profiling from the Cancer Genome Atlas (TCGA). Three advanced machine learning techniques (Support Vector Machine, Random Forest, and Naive Bayesian) were investigated to distinguish between molecular prognostic indicators and achieved an area under the ROC curve (AUC) values of 86%, 93%, 91%, and 91% for the prediction of ER+ versus ER-, PR+ versus PR-, HER2+ versus HER2-, and triple-negative, respectively. In conclusion, radiomic features enable to discriminate major breast cancer molecular subtypes and may yield a potential imaging biomarker for advancing precision medicine.