A Machine Learning Approach That Measures pH Using acidoCEST MRI of Iopamidol

Authors

Tianzhe Li
Julio Cárdenas-Rodríguez
Priya N Trakru
Mark D Pagel

Publication Date

10-1-2023

Journal

NMR Biomedicine

Abstract

Tumor acidosis is an important biomarker for aggressive tumors, and extracellular pH (pHe) of the tumor microenvironment can be used to predict and evaluate tumor responses to chemotherapy and immunotherapy. AcidoCEST MRI measures tumor pHe by exploiting the pH-dependent chemical exchange saturation transfer (CEST) effect of iopamidol, an exogenous CT agent repurposed for CEST MRI. However, all pH fitting methodologies for acidoCEST MRI data have limitations. Herein we present results of the application of machine learning for extracting pH values from CEST Z-spectra of iopamidol. We acquired 36,000 experimental CEST spectra from 200 phantoms of iopamidol prepared at five concentrations, five T

Keywords

Humans, Iopamidol, Hydrogen-Ion Concentration, Neoplasms, Magnetic Resonance Imaging, Machine Learning, Tumor Microenvironment, CEST MRI, iopamidol, machine learning, molecular imaging, quantitative MRI, tumor pH

Comments

PMID: 37280721