Enhanced glioblastoma detection through 13C hyperpolarized MRI (hpMRI) and two-dimensional statistics

Abstract

Purpose: To develop a method for statistically significant tumor delineation. Glioblastoma (GBM) remains resistant to current therapeutic strategies and is unequivocally associated with a dismal prognosis. Hyperpolarized 13C MRI (hpMRI) provides unique insights into tissue metabolism, enabling outlining of tumor boundaries. Statistical confidence of this outline may help guide surgical resection, inform therapeutic decisions, and evaluate treatment efficacy. Materials and Methods: The new method was applied to time-resolved hyperpolarized [1-13C]pyruvate data from a previous study in a rat glioma model as well as from a new clinical acquisition from a patient with brain cancer. MATLAB R2021a was used for the comprehensive extraction and analysis of metabolite profiles. Time-points corresponding to high pyruvate and lactate signal intensities were combined, smoothed using a two-dimensional (2D) moving average, and analyzed with a sliding window to identify regions statistically distinct from manually segmented normal tissue. The negative predictive value (NPV) and the positive predictive value (PPV) of the detection method were evaluated by comparison with cancer regions delineated using standard radiological criteria. Results: The new approach enabled glioblastoma delineation with the NPV of 99% and the PPV of 59% in small animals. While in the larger brain of the clinical patient, with bicarbonate taken into account, NPV and PPV obtained 95% and 92% respectively. Conclusions: Combining time-points of high pyruvate and lactate signal intensities increases the statistical power of two-dimensional testing. This promising technique requires further evaluation in a larger patient cohort.