AI Enhanced Magnetic Resonance Fingerprinting in Neuro-Oncology: A Narrative Review of Diagnostic Performance and Clinical Readiness

Abstract

Magnetic resonance fingerprinting (MRF) is a quantitative magnetic resonance imaging technique that generates tissue signals that vary based on chemical, physical, and biological properties called “fingerprints.” MRF enables simultaneous estimation of multiple tissue properties, distinguishing it from classic MRI. As AI assistance within radiology expands, it’s important to understand techniques like MRF that address limitations of imaging in clinical settings. MRF bridges the gap between diagnostic accuracy and speed, allowing faster reading times and differentiation of tissue markers in neuro-oncology. This narrative review analyzes literature on MRF within neuro-oncology to improve understanding of diagnostic accuracy, machine learning integration, and clinical implementation, with the goal of evaluating MRF performance before clinical adoption. Searches utilized PubMed, identifying peer-reviewed studies published within the last thirteen years. The search using the keyword “MRF” provided 1,716 studies. Studies were included if they reported clinically relevant MRF applications, 22 studies met inclusion criteria. Literature shows MRF can distinguish between normal tissues and neurologic tumors by providing quantitative relaxometry values, enhancing diagnostic precision. MRF grades gliomas with 88.9% and 75% accuracy for low- versus high-grade gliomas, respectively. MRF also shows promise when paired with machine learning to improve efficiency compared to standard dictionary matching methods. Machine learning–integrated MRF enables faster, more accurate tissue mapping, reducing acquisition time while maintaining imaging quality. Studies showed that MRF can be applied across specialties but remains concentrated in neurology, with limited reproducibility in non-neurologic fields. Many studies lacked large population sizes, limiting conclusions about clinical readiness. Limitations include technological constraints, interpretation variability, and restricted applications. This review extends existing evidence on AI-enhanced MRF for neurologic tumors and emphasizes the need for larger, reproducible studies and further AI integration before clinical adoption.