Advancements in magnetic resonance imaging and magnetic resonance-driven techniques for evaluating pituitary macroadenoma consistency: current progress and future perspectives.

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Abstract

Pituitary macroadenomas (PMAs) are among the most common intracranial tumors and pose significant surgical challenges, especially when the tumor consistency is increased due to high fibrous content. Accurate preoperative assessment of tumor consistency is crucial for optimizing surgical strategies and patient outcomes. Although conventional T2-weighted magnetic resonance imaging (MRI) is widely employed, its predictive value remains uncertain. Emerging techniques, such as diffusion-weighted imaging and magnetic resonance elastography, provide insights into tumor stiffness by assessing microstructural and mechanical properties, enhancing the prediction of PMA consistency. Furthermore, advancements in machine learning (ML) and deep learning, particularly convolutional neural networks (CNNs) and hybrid CNN-transformer models, have improved the extraction of complex imaging features, leading to greater predictive accuracy. This review summarizes recent developments in MRI and MR-driven imaging techniques for predicting PMA consistency, emphasizing their clinical application and the role of ML-based methods in refining predictive models. Despite numerous candidate imaging biomarkers, the field remains constrained by insufficient reproducibility, limited cross-study comparability, heterogeneity in MRI acquisition and post-processing protocols, and poor model generalizability. Continued integration of advanced MRI with ML-driven imaging analyses may further enhance preoperative prediction of PMA consistency and facilitate surgical planning.

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