Faria Andreia V, Oishi Kenichi, Yoshida Shoko, Hillis Argye, Miller Michael I, Mori Susumu
The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Department of Neurology, Johns Hopkins University, Baltimore, MD, USA ; Department of Physical Medicine & Rehabilitation Medicine, Johns Hopkins University, Baltimore, MD, USA ; Department of Cognitive Science, Johns Hopkins University, Baltimore, MD, USA.
Neuroimage Clin. 2015 Jan 15;7:367-76. doi: 10.1016/j.nicl.2015.01.008. eCollection 2015.
Radiological diagnosis is based on subjective judgment by radiologists. The reasoning behind this process is difficult to document and share, which is a major obstacle in adopting evidence-based medicine in radiology. We report our attempt to use a comprehensive brain parcellation tool to systematically capture image features and use them to record, search, and evaluate anatomical phenotypes. Anatomical images (T1-weighted MRI) were converted to a standardized index by using a high-dimensional image transformation method followed by atlas-based parcellation of the entire brain. We investigated how the indexed anatomical data captured the anatomical features of healthy controls and a population with Primary Progressive Aphasia (PPA). PPA was chosen because patients have apparent atrophy at different degrees and locations, thus the automated quantitative results can be compared with trained clinicians' qualitative evaluations. We explored and tested the power of individual classifications and of performing a search for images with similar anatomical features in a database using partial least squares-discriminant analysis (PLS-DA) and principal component analysis (PCA). The agreement between the automated z-score and the averaged visual scores for atrophy (r = 0.8) was virtually the same as the inter-evaluator agreement. The PCA plot distribution correlated with the anatomical phenotypes and the PLS-DA resulted in a model with an accuracy of 88% for distinguishing PPA variants. The quantitative indices captured the main anatomical features. The indexing of image data has a potential to be an effective, comprehensive, and easily translatable tool for clinical practice, providing new opportunities to mine clinical databases for medical decision support.
放射学诊断基于放射科医生的主观判断。这一过程背后的推理难以记录和分享，这是放射学采用循证医学的主要障碍。我们报告了我们尝试使用一种全面的脑部分割工具来系统地捕捉图像特征，并利用这些特征来记录、搜索和评估解剖表型。通过使用高维图像变换方法，然后对整个大脑进行基于图谱的分割，将解剖图像（T1加权磁共振成像）转换为标准化指数。我们研究了索引后的解剖数据如何捕捉健康对照者和原发性进行性失语症（PPA）患者群体的解剖特征。选择PPA是因为患者在不同程度和位置有明显萎缩，因此可以将自动定量结果与训练有素的临床医生的定性评估进行比较。我们使用偏最小二乘判别分析（PLS-DA）和主成分分析（PCA）探索并测试了个体分类以及在数据库中搜索具有相似解剖特征图像的能力。自动z评分与萎缩平均视觉评分之间的一致性（r = 0.8）与评估者间的一致性几乎相同。PCA图分布与解剖表型相关，PLS-DA生成了一个区分PPA变体的准确率为88%的模型。定量指标捕捉了主要解剖特征。图像数据的索引有可能成为临床实践中一种有效、全面且易于转化的工具，为挖掘临床数据库以提供医疗决策支持提供新机会。
