AI and Radiomics for Predicting Cavernous Sinus Invasion in Pituitary Adenomas: Review of Current Evidence

ML and deep learning models consistently outperform conventional MRI for preoperative cavernous sinus invasion prediction in pituitary adenomas; CNN-based models on contrast-enhanced MRI often exceed AUC 0.85. External validation and multicenter data remain lacking.

• Clinically oriented review synthesizing MRI-based radiomics, ML classifiers, and CNN architectures benchmarked against the Knosp grading system for cavernous sinus invasion detection; exact study count and pooled n not reported in source.
• Radiomics pipelines integrating quantitative imaging features with clinical variables achieved high diagnostic accuracy; automated segmentation frameworks showed reliable tumor boundary delineation (Dice similarity coefficients reported across studies, specific values not provided in source).
• Key limitation: studies are predominantly small, single-center, and lack external validation — the primary barrier to clinical adoption highlighted by the authors.

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