Shaikh Faiq A, Kolowitz Brian J, Awan Omer, Aerts Hugo J, von Reden Anna, Halabi Safwan, Mohiuddin Sohaib A, Malik Sana, Shrestha Rasu B, Deible Christopher
Faiq A. Shaikh, Brian J. Kolowitz, Anna von Reden, Rasu B. Shrestha, and Christopher Deible, University of Pittsburgh Medical Center Enterprises, Pittsburgh; Omer Awan, Temple University, Philadelphia, PA; Hugo J. Aerts, Dana-Farber Cancer Institute, Boston, MA; Safwan Halabi, Stanford University, Stanford, CA; Sohaib A. Mohiuddin, University of Miami, Miami, FL; and Sana Malik, University of Chicago, Chicago, IL.
JCO Clin Cancer Inform. 2017 Nov;1:1-8. doi: 10.1200/CCI.17.00004.
Radiomics is a quantitative approach to medical image analysis targeted at deciphering the morphologic and functional features of a lesion. Radiomic methods can be applied across various malignant conditions to identify tumor phenotype characteristics in the images that correlate with their likelihood of survival, as well as their association with the underlying biology. Identifying this set of characteristic features, called tumor signature, holds tremendous value in predicting the behavior and progression of cancer, which in turn has the potential to predict its response to various therapeutic options. We discuss the technical challenges encountered in the application of radiomics, in terms of methodology, workflow integration, and user experience, that need to be addressed to harness its true potential.
放射组学是一种医学图像分析的定量方法，旨在解读病变的形态学和功能特征。放射组学方法可应用于各种恶性疾病，以识别图像中与生存可能性相关的肿瘤表型特征，以及它们与潜在生物学特性的关联。识别这组称为肿瘤特征的特征集，对于预测癌症的行为和进展具有巨大价值，而这反过来又有可能预测其对各种治疗方案的反应。我们讨论了在放射组学应用中遇到的技术挑战，包括方法学、工作流程整合和用户体验等方面，要充分发挥其真正潜力就需要解决这些挑战。
