Data Analysis Strategies in Medical Imaging.
Data Analysis Strategies in Medical Imaging.
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DOI：
10.1158/1078-0432.ccr-18-0385
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发表时间：
2018-08-01
期刊：
影响因子：
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通讯作者：
Aerts HJWL
中科院分区：
文献类型：
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作者：
Parmar C;Barry JD;Hosny A;Quackenbush J;Aerts HJWL
放射成像仍然是肿瘤学中最有效和临床上最有用的工具之一。人工智能(AI)的成熟使得使用预定义的工程算法或深度学习方法来详细量化组织的放射学特征成为可能。放射学的先例以及大量的研究都暗示了这些特征的临床相关性。然而，与医学成像数据的分析相关的关键挑战。虽然其中一些挑战是成像领域特有的，但许多其他挑战，如重复性和批次效应是通用的，已经在其他量化领域如基因组学中得到了解决。在这里，我们发现了这些缺陷，并为医学影像数据的分析策略提供了建议，包括数据标准化、稳健模型的开发和严格的统计分析。遵循这些建议不仅将提高分析质量，还将通过允许成像数据与其他生物医学数据源更好地集成来增强精准医学。
Radiographic imaging continues to be one of the most effective and clinically useful tools within oncology. Sophistication of artificial intelligence (AI) has allowed for detailed quantification of radiographic characteristics of tissues using predefined engineered algorithms or deep learning methods. Precedents in radiology as well as a wealth of research studies hint at the clinical relevance of these characteristics. However, there are critical challenges associated with the analysis of medical imaging data. While some of these challenges are specific to the imaging field, many others like reproducibility and batch effects are generic and have already been addressed in other quantitative fields such as genomics. Here, we identify these pitfalls and provide recommendations for analysis strategies of medical imaging data including data normalization, development of robust models, and rigorous statistical analyses. Adhering to these recommendations will not only improve analysis quality, but will also enhance precision medicine by allowing better integration of imaging data with other biomedical data sources.