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<p>Pituitary lesions are increasingly detected due to widespread use of magnetic resonance imaging (MRI), with incidental findings (“incidentalomas”) now identified in 10–20% of brain imaging studies. MRI remains the gold standard for evaluating sellar and parasellar pathology, offering superior soft-tissue contrast without ionizing radiation. This comprehensive narrative review synthesizes current evidence on the role of MRI in evaluating pituitary lesions, with a primary focus on intrasellar pathologies and post-operative imaging. We highlight technical advances from dynamic contrast-enhanced (DCE) imaging to emerging artificial intelligence (AI) applications. Dynamic contrast-enhanced MRI significantly improves microadenoma detection, with sensitivity exceeding 90% compared to approximately 50% with static imaging. High-field 3&#xa0;T imaging offers 10–15% improved detection compared to 1.5&#xa0;T systems. Post-operative baseline imaging optimally performed at 3–4&#xa0;months minimizes false-positive residual tumor interpretation. AI-based radiomics models demonstrate promising accuracy (AUC 0.85–0.95) for predicting tumor consistency, invasiveness, and recurrence, though prospective validation remains limited. A systematic approach to pituitary MRI interpretation, integrating optimized protocols with emerging AI tools, enhances diagnostic accuracy and guides clinical decision-making.</p>

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Decoding the sella: a review of MRI in pituitary lesions—from dynamic imaging to artificial intelligence

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月5日

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  • Rishabh Khimani,
  • Ajay Upadhyay,
  • Asutosh Dave

摘要

Pituitary lesions are increasingly detected due to widespread use of magnetic resonance imaging (MRI), with incidental findings (“incidentalomas”) now identified in 10–20% of brain imaging studies. MRI remains the gold standard for evaluating sellar and parasellar pathology, offering superior soft-tissue contrast without ionizing radiation. This comprehensive narrative review synthesizes current evidence on the role of MRI in evaluating pituitary lesions, with a primary focus on intrasellar pathologies and post-operative imaging. We highlight technical advances from dynamic contrast-enhanced (DCE) imaging to emerging artificial intelligence (AI) applications. Dynamic contrast-enhanced MRI significantly improves microadenoma detection, with sensitivity exceeding 90% compared to approximately 50% with static imaging. High-field 3 T imaging offers 10–15% improved detection compared to 1.5 T systems. Post-operative baseline imaging optimally performed at 3–4 months minimizes false-positive residual tumor interpretation. AI-based radiomics models demonstrate promising accuracy (AUC 0.85–0.95) for predicting tumor consistency, invasiveness, and recurrence, though prospective validation remains limited. A systematic approach to pituitary MRI interpretation, integrating optimized protocols with emerging AI tools, enhances diagnostic accuracy and guides clinical decision-making.