Combining Molecular and Radiomic Features for Risk Assessment in Breast Cancer.

IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Alex A Nguyen, Anne Marie McCarthy, Despina Kontos
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引用次数: 0

Abstract

Breast cancer risk is highly variable within the population and current research is leading the shift toward personalized medicine. By accurately assessing an individual woman's risk, we can reduce the risk of over/undertreatment by preventing unnecessary procedures or by elevating screening procedures. Breast density measured from conventional mammography has been established as one of the most dominant risk factors for breast cancer; however, it is currently limited by its ability to characterize more complex breast parenchymal patterns that have been shown to provide additional information to strengthen cancer risk models. Molecular factors ranging from high penetrance, or high likelihood that a mutation will show signs and symptoms of the disease, to combinations of gene mutations with low penetrance have shown promise for augmenting risk assessment. Although imaging biomarkers and molecular biomarkers have both individually demonstrated improved performance in risk assessment, few studies have evaluated them together. This review aims to highlight the current state of the art in breast cancer risk assessment using imaging and genetic biomarkers.

结合分子和放射学特征评估乳腺癌的风险。
乳腺癌的风险在人群中是高度可变的,目前的研究正引领着向个性化医疗的转变。通过准确评估女性个体的风险,我们可以通过避免不必要的手术或提高筛查程序来减少过度/治疗不足的风险。传统乳房x光检查测量的乳腺密度已被确定为乳腺癌最主要的危险因素之一;然而,它目前的局限性在于其表征更复杂的乳腺实质模式的能力,这些模式已被证明为加强癌症风险模型提供了额外的信息。从高外显率(或突变极有可能表现出疾病的体征和症状)到低外显率的基因突变组合等分子因素显示出增加风险评估的希望。虽然成像生物标志物和分子生物标志物都单独证明了在风险评估方面的性能提高,但很少有研究将它们结合起来进行评估。本文综述了利用影像学和遗传生物标志物进行乳腺癌风险评估的最新进展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
11.10
自引率
1.70%
发文量
0
期刊介绍: The Annual Review of Biomedical Data Science provides comprehensive expert reviews in biomedical data science, focusing on advanced methods to store, retrieve, analyze, and organize biomedical data and knowledge. The scope of the journal encompasses informatics, computational, artificial intelligence (AI), and statistical approaches to biomedical data, including the sub-fields of bioinformatics, computational biology, biomedical informatics, clinical and clinical research informatics, biostatistics, and imaging informatics. The mission of the journal is to identify both emerging and established areas of biomedical data science, and the leaders in these fields.
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