Department of Radiology, Molecular Imaging Program at Stanford, School of Medicine, Stanford University, Stanford, CA, USA.
Department of Radiology, Integrative Biomedical Imaging Informatics at Stanford, School of Medicine, Stanford University, Stanford, CA, USA.
Sci Rep. 2020 Apr 24;10(1):6996. doi: 10.1038/s41598-020-63810-1.
There is a need for noninvasive repeatable biomarkers to detect early cancer treatment response and spare non-responders unnecessary morbidities and costs. Here, we introduce three-dimensional (3D) dynamic contrast enhanced ultrasound (DCE-US) perfusion map characterization as inexpensive, bedside and longitudinal indicator of tumor perfusion for prediction of vascular changes and therapy response. More specifically, we developed computational tools to generate perfusion maps in 3D of tumor blood flow, and identified repeatable quantitative features to use in machine-learning models to capture subtle multi-parametric perfusion properties, including heterogeneity. Models were developed and trained in mice data and tested in a separate mouse cohort, as well as early validation clinical data consisting of patients receiving therapy for liver metastases. Models had excellent (ROC-AUC > 0.9) prediction of response in pre-clinical data, as well as proof-of-concept clinical data. Significant correlations with histological assessments of tumor vasculature were noted (Spearman R > 0.70) in pre-clinical data. Our approach can identify responders based on early perfusion changes, using perfusion properties correlated to gold-standard vascular properties.
需要非侵入性、可重复的生物标志物来检测癌症治疗的早期反应，避免非应答者遭受不必要的发病和治疗费用。在这里，我们引入了三维（3D）动态对比增强超声（DCE-US）灌注图特征作为一种廉价、床边和纵向的肿瘤灌注指标，用于预测血管变化和治疗反应。更具体地说，我们开发了计算工具来生成肿瘤血流的 3D 灌注图，并确定了可重复的定量特征，用于机器学习模型中，以捕捉细微的多参数灌注特性，包括异质性。模型在小鼠数据中进行了开发和训练，并在另一个小鼠队列中进行了测试，以及早期验证的包含接受肝转移治疗的患者的临床数据。在临床前数据中，模型具有出色的（ROC-AUC>0.9）反应预测能力，并且在临床验证数据中也得到了证实。在临床前数据中，与肿瘤血管的组织学评估显著相关（Spearman R>0.70）。我们的方法可以基于早期灌注变化来识别应答者，使用与金标准血管特性相关的灌注特性。
