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Purpose <p>To evaluate the diagnostic contribution of MRI-based morphological and signal features—particularly T2-weighted signal intensity—for differentiating the etiology of pituitary stalk thickening (PST), and to develop imaging-based models for predicting inflammatory and neoplastic pathologies.</p> Methods <p>This retrospective study included 41 adult (51.2%) and pediatric (48.8%) patients with confirmed PST who underwent contrast-enhanced pituitary MRI between 2012 and 2021. Etiologies were classified as congenital/idiopathic, inflammatory/infectious, or neoplastic based on clinical, radiological, or histopathological criteria. Imaging findings including enhancement pattern and T2 signal intensity were assessed in consensus by two neuroradiologists blinded to clinical data. Statistical analyses included univariate and multivariate logistic regression and receiver operating characteristic (ROC) curve analysis.</p> Results <p>Neoplastic lesions were associated with significantly greater stalk thickness (median: 5.9&#xa0;mm) compared to non-neoplastic lesions (median: 3.83&#xa0;mm; <i>p</i> = 0.012). T2-weighted hyperintensity was present in 70% of neoplastic lesions, while hypointensity was more frequent in inflammatory/infectious lesions (<i>p</i> = 0.017). A multivariable model incorporating stalk thickness, non-T2 hypointensity and V-shaped enhancement patterns yielded excellent diagnostic performance for neoplastic pathologies (AUC: 0.848; 95% CI: 0.713–0.982). A second model using stalk thickness and T2 hypointensity predicted inflammatory lesions with an AUC of 0.836 (95% CI: 0.715–0.957).</p> Conclusion <p>To our knowledge, this is the first study to propose MRI-based models using stalk morphology and signal features to predict PST etiology. These non-invasive models, developed without clinical input, demonstrate promising diagnostic accuracy and may aid in differential diagnosis. Further validation in larger cohorts is needed.</p>

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Pituitary stalk thickening: can a multiparametric MRI approach improve etiologic prediction?

  • 期刊论文

  • 发表日期：

    2026年1月12日

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  • Erhan Bıyıklı,
  • Tahsin Aybal,
  • Bülent Aslan,
  • Gazanfer Ekinci

摘要

Purpose

To evaluate the diagnostic contribution of MRI-based morphological and signal features—particularly T2-weighted signal intensity—for differentiating the etiology of pituitary stalk thickening (PST), and to develop imaging-based models for predicting inflammatory and neoplastic pathologies.

Methods

This retrospective study included 41 adult (51.2%) and pediatric (48.8%) patients with confirmed PST who underwent contrast-enhanced pituitary MRI between 2012 and 2021. Etiologies were classified as congenital/idiopathic, inflammatory/infectious, or neoplastic based on clinical, radiological, or histopathological criteria. Imaging findings including enhancement pattern and T2 signal intensity were assessed in consensus by two neuroradiologists blinded to clinical data. Statistical analyses included univariate and multivariate logistic regression and receiver operating characteristic (ROC) curve analysis.

Results

Neoplastic lesions were associated with significantly greater stalk thickness (median: 5.9 mm) compared to non-neoplastic lesions (median: 3.83 mm; p = 0.012). T2-weighted hyperintensity was present in 70% of neoplastic lesions, while hypointensity was more frequent in inflammatory/infectious lesions (p = 0.017). A multivariable model incorporating stalk thickness, non-T2 hypointensity and V-shaped enhancement patterns yielded excellent diagnostic performance for neoplastic pathologies (AUC: 0.848; 95% CI: 0.713–0.982). A second model using stalk thickness and T2 hypointensity predicted inflammatory lesions with an AUC of 0.836 (95% CI: 0.715–0.957).

Conclusion

To our knowledge, this is the first study to propose MRI-based models using stalk morphology and signal features to predict PST etiology. These non-invasive models, developed without clinical input, demonstrate promising diagnostic accuracy and may aid in differential diagnosis. Further validation in larger cohorts is needed.