Shoemaker Katherine, Ger Rachel, Court Laurence E, Aerts Hugo, Vannucci Marina, Peterson Christine B
Department of Mathematics and Statistics, University of Houston-Downtown, Houston, TX, United States.
Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, United States.
Front Genet. 2023 Mar 8;14:1112914. doi: 10.3389/fgene.2023.1112914. eCollection 2023.
Imaging of tumors is a standard step in diagnosing cancer and making subsequent treatment decisions. The field of radiomics aims to develop imaging based biomarkers using methods rooted in artificial intelligence applied to medical imaging. However, a challenging aspect of developing predictive models for clinical use is that many quantitative features derived from image data exhibit instability or lack of reproducibility across different imaging systems or image-processing pipelines. To address this challenge, we propose a Bayesian sparse modeling approach for image classification based on radiomic features, where the inclusion of more reliable features is favored a probit prior formulation. We verify through simulation studies that this approach can improve feature selection and prediction given correct prior information. Finally, we illustrate the method with an application to the classification of head and neck cancer patients by human papillomavirus status, using as our prior information a reliability metric quantifying feature stability across different imaging systems.
肿瘤成像在癌症诊断及后续治疗决策中是一个标准步骤。放射组学领域旨在利用基于人工智能并应用于医学成像的方法来开发基于成像的生物标志物。然而，开发临床应用预测模型的一个具有挑战性的方面是，从图像数据中导出的许多定量特征在不同成像系统或图像处理流程中表现出不稳定性或缺乏可重复性。为应对这一挑战，我们提出一种基于放射组学特征的用于图像分类的贝叶斯稀疏建模方法，其中通过概率单位先验公式来优先纳入更可靠的特征。我们通过模拟研究验证，给定正确的先验信息时，该方法可改善特征选择和预测。最后，我们以人乳头瘤病毒状态对头颈部癌症患者进行分类的应用为例来说明该方法，使用量化不同成像系统间特征稳定性的可靠性指标作为我们的先验信息。
