Spatial Characterization of Tumor Perfusion Properties from 3D DCE-US Perfusion Maps are Early Predictors of Cancer Treatment Response

Ahmed El Kaffas, Assaf Hoogi, Jianhua Zhou, Isabelle Durot, Huaijun Wang, Jarrett Rosenberg, Albert Tseng, Hersh Sagreiya, Alireza Akhbardeh, Daniel L. Rubin, Aya Kamaya, Dimitre Hristov, Jurgen K. Willmann

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

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.

Original languageEnglish
Article number6996
JournalScientific Reports
Volume10
Issue number1
DOIs
StatePublished - 1 Dec 2020
Externally publishedYes

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