Özcan Alpay, Türkbey Barış, Choyke Peter L, Akin Oguz, Aras Ömer, Mun Seong K
Arlington Innovation Center: Health Research, Virginia Polytechnic Institute and State University, 900 N. Glebe Road, Arlington VA 22203, USA.
Molecular Imaging Program, National Cancer Institute, National Institutes of Health, 10 Center Dr., MSC 1182, Bldg. 10, Rm. 1B40, Bethesda, MD 20892-1088, USA.
Magn Reson Imaging. 2015 Jul;33(6):804-15. doi: 10.1016/j.mri.2015.03.007. Epub 2015 Apr 11.
Wider information content of multi-modal biomedical imaging is advantageous for detection, diagnosis and prognosis of various pathologies. However, the necessity to evaluate a large number images might hinder these advantages and reduce the efficiency. Herein, a new computer aided approach based on the utilization of feature space (FS) with reduced reliance on multiple image evaluations is proposed for research and routine clinical use. The method introduces the physician experience into the discovery process of FS biomarkers for addressing biological complexity, e.g., disease heterogeneity. This, in turn, elucidates relevant biophysical information which would not be available when automated algorithms are utilized. Accordingly, the prototype platform was designed and built for interactively investigating the features and their corresponding anatomic loci in order to identify pathologic FS regions. While the platform might be potentially beneficial in decision support generally and specifically for evaluating outlier cases, it is also potentially suitable for accurate ground truth determination in FS for algorithm development. Initial assessments conducted on two different pathologies from two different institutions provided valuable biophysical perspective. Investigations of the prostate magnetic resonance imaging data resulted in locating a potential aggressiveness biomarker in prostate cancer. Preliminary findings on renal cell carcinoma imaging data demonstrated potential for characterization of disease subtypes in the FS.
多模态生物医学成像更广泛的信息内容有利于各种病变的检测、诊断和预后评估。然而，评估大量图像的必要性可能会阻碍这些优势并降低效率。在此，提出了一种新的计算机辅助方法，该方法基于利用特征空间（FS），减少对多图像评估的依赖，用于研究和常规临床应用。该方法将医生经验引入FS生物标志物的发现过程，以应对生物复杂性，例如疾病异质性。这反过来又阐明了使用自动算法时无法获得的相关生物物理信息。因此，设计并构建了原型平台，用于交互式研究特征及其相应的解剖位点，以识别病理性FS区域。虽然该平台一般可能对决策支持有潜在益处，特别是用于评估异常病例，但它也可能适用于在FS中为算法开发准确确定真实情况。对来自两个不同机构的两种不同病变进行的初步评估提供了有价值的生物物理视角。对前列腺磁共振成像数据的研究在前列腺癌中找到了一种潜在的侵袭性生物标志物。肾细胞癌成像数据的初步研究结果表明在FS中对疾病亚型进行特征化的潜力。
