A Machine-Learning Approach to Measuring Tumor pH Using MRI

• Main Authors: DeGrandchamp, Joseph B., Cárdenas-Rodríguez, Julio
• Language: en_US
• Published: The University of Arizona. 2017
• Online Access: http://hdl.handle.net/10150/624957; http://arizona.openrepository.com/arizona/handle/10150/624957

Tumor pH can become an important consideration in diagnosis and choosing an effective therapy. Measuring pH currently requires invasive procedures or has problems with sensitivity. Previous methods for the measurement of pH by MRI are dependent on knowing the concentration of contrast agents or are useful at a limited pH range. Determining the concentration of a contrast agent in vivo is a very difficult task. We propose to use machine learning to decouple the estimation of pH from requiring knowledge of contrast agent concentration. This approach makes it possible to use new contrast agents and extends the ranges of pH than can be studied. In addition, this technique uses the entirety of the data instead of fitting parameters in order to make pH predictions. We evaluated the performance of this new method to measure pH by repurposing a clinically approved X-ray contrast agent, ioxilan, as an MRI agent. The pH was successfully measured in vitro with a small margin of error (RMSE = 0.0515), and the method was able to produce reliable parametric maps of pH for acquired image sets. We will extend this new method to measure pH in mouse models of cancer.