Simple SummaryQualitative assessments represent the current mainstay for neuro-oncologic MRI. Additionally, PET provides important information about metabolic aspects, which is of utmost importance for treatment monitoring. While PET provides quantitative data based on tracer uptake, qualitative MRI is currently limited by subjective judgments and low inter-scanner reliability. MR fingerprinting is considered a promising approach to overcome these limitations by generating imaging data for both qualitative and quantitative assessments, based on a single sequence acquisition. This novel approach characterizes the investigated tissue by retrieving quantitative MR metrics, based on each voxels unique signal fingerprint. In this investigation, PET- and MR fingerprinting were compared to enhance the predictability of tumor-related characteristics. While both modalities revealed high predictability, the results differed, reflecting their diverse underlying mechanisms. However, the combined use of PET- and MR fingerprinting provides promising potential to retrieve tissue-specific properties, complemented by metabolic information, which, therefore, increases the value of hybrid imaging in neuro-oncology.AbstractObjectives: Advanced MR imaging of brain tumors is still mainly based on qualitative imaging. PET imaging offers additive metabolic information, and MR fingerprinting (MRF) offers a novel approach to quantitative data acquisition. The purpose of this study was to evaluate the ability of MRF to predict tumor regions and grading in combination with PET. Methods: Seventeen patients with histologically verified infiltrating gliomas and available amino-acid PET data were enrolled. ROIs for solid tumor parts (SPo), perifocal edema (ED1), and normal-appearing white matter (NAWM) were selected on conventional MRI sequences and aligned to the MRF and PET images. The predictability of gliomas by region and grading as well as intermodal correlations were assessed. Results: For MRF, we calculated an overall predictability by region (SPo, ED1, and NAWM) for all of the MRF parameters of 76.5%, 47.1%, and 94.1%, respectively. The overall ability to distinguish low- from high-grade gliomas using MRF was 88.9% for LGG and 75% for HGG, with an accuracy of 82.4%, a ppV of 85.71%, and an npV of 80%. PET positivity was found in 13/17 patients for solid tumor parts, and in 3/17 patients for the edema region. However, there was no significant difference in region-specific MRF values between PET positive and PET negative patients. Conclusions: MRF and PET provide quantitative measurements of the tumor tissue characteristics of gliomas, with good predictability. Nonetheless, the results are dissimilar, reflecting the different underlying mechanisms of each method.