A Machine-Learning Approach to Measuring Tumor pH Using MRI Item Type text; Electronic Thesis Authors DeGrandchamp, Joseph B.; Cárdenas-Rodríguez, Julio Publisher The University of Arizona. Rights Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author. Download date 04/10/2021 14:00:38 Item License http://rightsstatements.org/vocab/InC/1.0/ Link to Item http://hdl.handle.net/10150/624957 A MACHINE-LEARNING APPROACH TO MEASURING TUMOR pH USING MRI Author: Joseph B. DeGrandchamp Project Advisor: Dr. Julio Cárdenas-Rodríguez A Thesis Submitted to the Department of Chemistry and Biochemistry and the Honors College in Partial Fulfillment of the Bachelors of Science degree with Honors in Chemistry. Department of Chemistry & Biochemistry University of Arizona Spring 2017 Approved by: Dr. Julio Cárdenas-Rodríguez Faculty Thesis Advisor Date A Machine-Learning Approach to Measuring Tumor pH using MRI J. DeGrandchamp*,1,2, J. Cárdenas-Rodríguez*,1 1. Department of Medical Imaging, University of Arizona, Tucson, AZ. 2. Department of Chemistry and Biochemistry, University of Arizona, Tucson, AZ. *To whom correspondence should be addressed. E-mail: [email protected], [email protected] ABSTRACT 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. 1 Introduction Clinicians are constantly in search of better tools to diagnose cancer and aid in its treatment. Cancerous tissues typically exhibit lower extracellular pH than their surroundings due to their altered metabolic activity. Knowledge of the exact level of pH adjustment in tumors could aid clinicians in numerous ways. Acidic tumor pH is correlated with higher incidence of metastasis, and thus with higher mortality.1 Furthermore, the effectiveness of many chemotherapies has shown a dependence on pH.2 Doxorubicin for example is weakly basic, and its uptake into acidic tissues is decreased.2 Clinicians could therefore use tumor pH in choosing the most effective treatment from the multitudes of therapies available. However, we currently do not have a general method to measure the pH of tumors in vivo in a non-invasive manner. One method for measuring tumor pH consists of inserting a physical probe into the tissue.3 This can be useful during surgery where the extracellular pH can be key in differentiating healthy tissue from cancerous tissue.3 However in the case of surgery, the tissue is already exposed and readily accessible. Measuring pH in this manner prior to surgery or in cases where surgery is not required becomes a very invasive process. Non-invasive methods have been developed to address this issue. pH measurement using MRI has seen some success using Chemical exchange saturation transfer (CEST).4,5 CEST MRI is a method that is dependent on the exchange of protons between a contrast agent and bulk water. A CEST spectra is constructed by applying a saturation pulse at different frequencies relative to water (Figure 1). The shape of this CEST is a function of the experimental parameters used in its collection, the exchange rate of the protons under study, the number of protons, and their concentration.4,5 Depending on the agent used, the exchange rate could be either acid or base catalyzed. In ioxilan, the observed signal is from a group of amides with an offset ~4.2 ppm (Figure 1), the proton exchange of 2 which is base catalyzed. Faster exchange rates result in increased peak amplitude and width. This pH dependency makes CEST MRI a suitable method to measure pH in vivo. However, this methods still suffers from low sensitivity, variance, and requires knowledge of contrast agent concentration.4,5 Determining concentration is a difficult, time-consuming, and often inviable process.6 By using machine learning, the pH and concentration dependencies can be decoupled. Figure 1. The (A) structure of ioxilan and (B) corresponding CEST spectra (normalized and corrected for B0 inhomogeneity) for a range of 9 concentrations at pH 7.02. Here, the y-axis is displayed as the signal (MZ) divided by the initial signal (M0). It is often interpreted as a percentage of the water signal, where 1 would be 100%. Bulk water appears as a broad peak at 0 ppm (B). The amide groups (circled in (A)) appear as a single peak at ~4.2 ppm (B) due to their chemical similarity. As ioxilan concentration increases, the amplitude of the 4.2 ppm peak increases as well (B). CEST MRI is currently used to measure pH in vivo using two approaches: 1) Estimate the concentration of a contrast agent and the tissue pH using advanced non-linear curve fitting methods using the Bloch-McConnell equations (Bloch fitting method).5 2) Estimate the pH using an empirical calibration curve based on the ratio of two CEST signals of exchangeable protons in the same molecule and at the same pH (ratiometric method).4 The Bloch fitting method requires substantial expertise, taking more than 45 minutes to analyze a single CEST experiment, and it is sensitive to noise. The ratiometric method does not require substantial fitting, but it only works 3 at pH values less than 7.0 and requires agents with two exchangeable protons discernable by CEST.4,6 We propose an entirely new approach to measure pH using CEST MRI. Instead of doing Bloch fitting or creating an empirical calibration curve, we create a model between the observed CEST spectra at multiple pHs and concentrations using machine learning. Machine learning refers to a variety of computational techniques which are grouped based on their use of training data to create useful models as in Figure 2. Machine learning has seen use in diagnosis of disease and is useful for modeling phenomena where the model is not well understood.7 In this study, we used the machine learning technique known as partial least-squares (PLS) regression.8 PLS regression uses many of the same principles as principal component analysis, but prioritizes prediction instead of data reconstruction. Herein, PLS regression was used to find a model for pH measurement in vitro using a repurposed X-ray contrast agent, ioxilan, at a range of concentrations and pH. This model for ioxilan will in the future be used for pH measurement in vivo by injecting this agent. Figure 2. A simple flowchart representation of a machine learning workflow 4 Methods Sample Preparation Solutions of ioxilan at 8 different pHs and 9 different concentrations were prepared from a clinical stock formulation (Oxilan® 350 mgI/mL, Guerbet LLC). The pH was adjusted using hydrochloric acid and sodium hydroxide. The pH values used were 6.02, 6.25, 6.60, 6.76, 7.02, 7.19, 7.40, and 8.00, and each pH sample was diluted to achieve the following concentrations: 166, 133, 106, 85, 68, 54, 44, 35, and 28 mM. In total, there were 72 samples. Samples were stored and imaged in 200 µL Eppendorf tubes. Image Acquisition To acquire images, samples were separated by pH (sets of 9) and inserted into a cradle containing 2% (w/v) agar gel. The gel was kept at 37.0±0.2 °C during scans using a thermostatic heating system equipped with a temperature probe. CEST acquisitions were performed for each pH set. The CEST MRI protocol consisted of CEST-Fast imaging with steady-state precession (FISP) MR images using 3 µT saturation powers and a saturation time of 5 s.9 The FISP protocol utilized a 35.7461 ms repetition time (TR); 12.6781 ms echo time (TE); 10 µs bp32 excitation pulses; 60° excitation angle; 1 mm slice thickness; 4.0 x 4.0 cm field of view (FOV); 0.0997 mm2 plane resolution; 128 x 128 resolution matrix; centric encoding order; unbalanced “FID” mode; and 500 ms scan time. The protocol was run with acquisitions of 4 and 101 CEST excitation frequencies ranging from -50 to -47 ppm and -8 to 15 ppm respectively. Data Analysis Image analysis was conducted in MATLAB® 2016b (Mathworks, Natick, MA). Analysis consisted of first selecting regions of interest (ROIs) for each sample in each set. Average CEST signals of all voxels in the ROIs were then extracted from the image sets. The first four 5 saturation frequencies (which allow the system to reach steady state) were discarded. Spectra were corrected for B0 inhomogeneity by setting the minimum signal to 0 ppm and then normalized to the first value (at -8 ppm). Spectra were then centered and denoised using a Lorentzian fitting with the following function: ∗/ (1) , , , 1 / In Eq.