Precision diagnostics based on machine learning-derived imaging signatures
Precision diagnostics based on machine learning-derived imaging signatures
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DOI：
10.1016/j.mri.2019.04.012
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发表时间：
2019-12-01
影响因子：
2.5
通讯作者：
Kontos, Despina
中科院分区：
文献类型：
--
作者：
Davatzikos, Christos;Sotiras, Aristeidis;Kontos, Despina
现代多参数MRI的复杂性日益挑战这种图像的传统解释。机器学习已经成为一种强大的方法，可以将多样化和复杂的成像数据整合到诊断和预测价值的签名中。它还使我们能够从组比较进展到提供个体价值的成像生物标志物。我们回顾了围绕这一主题的几个研究方向，强调机器学习在个性化临床结果预测中的应用，将广泛的伞形诊断类别分解为更详细和精确的亚型，以及非侵入性地估计癌症分子特征。这些方法和研究通过引入临床结果的更特异性诊断和预测生物标志物，从而为精准医学领域做出了贡献，从而为患者提供了更好的治疗匹配。
The complexity of modern multi-parametric MRI has increasingly challenged conventional interpretations of such images. Machine learning has emerged as a powerful approach to integrating diverse and complex imaging data into signatures of diagnostic and predictive value. It has also allowed us to progress from group comparisons to imaging biomarkers that offer value on an individual basis. We review several directions of research around this topic, emphasizing the use of machine learning in personalized predictions of clinical outcome, in breaking down broad umbrella diagnostic categories into more detailed and precise subtypes, and in non-invasively estimating cancer molecular characteristics. These methods and studies contribute to the field of precision medicine, by introducing more specific diagnostic and predictive biomarkers of clinical outcome, therefore pointing to better matching of treatments to patients.