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Radiomics in Pituitary Adenomas: A Systematic Review of Clinical Applications and Predictive Models.

Edoardo Agosti1, Marcello Mangili1, Pier Paolo Panciani1

  • 1Neurosurgery Unit, Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, 25123 Brescia, Italy.

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|September 27, 2025
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Summary

Radiomics shows promise in predicting pituitary adenoma (PA) subtypes, invasiveness, and treatment response, with many studies reporting high diagnostic performance. However, clinical translation is limited by heterogeneity and lack of standardization.

Keywords:
machine learningmagnetic resonance imagingpituitary adenomaradiomicssystematic review

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Area of Science:

  • Radiology
  • Medical Imaging
  • Artificial Intelligence in Medicine

Background:

  • Radiomics provides quantitative imaging data for improved diagnosis and treatment of pituitary adenomas (PAs).
  • Pituitary adenomas are tumors of the pituitary gland requiring accurate diagnostic and prognostic modeling.
  • This systematic review synthesizes the clinical applications of radiomics in PAs.

Purpose of the Study:

  • To systematically review and synthesize the current clinical applications of radiomics in pituitary adenomas.
  • To focus on diagnostic, predictive, and prognostic modeling using radiomics in PAs.
  • To assess the performance and limitations of radiomics in PA management.

Main Methods:

  • Systematic literature search following PRISMA 2020 guidelines in PubMed, Scopus, and Web of Science.
  • Inclusion of studies evaluating radiomics-based MRI models for PA diagnosis, classification, consistency, invasiveness, treatment response, or recurrence.
  • Data extraction on study design, MRI sequences, feature extraction tools, machine learning algorithms, and performance metrics; quality assessment using Newcastle-Ottawa Scale.

Main Results:

  • 49 studies (9350+ patients) met inclusion criteria; most were retrospective.
  • Machine learning (88%) and deep learning (35%) were widely used; PyRadiomics was common.
  • High diagnostic performance (AUC ≥0.85 in 45% of studies) for PA subtypes, invasiveness, and treatment response was reported.
  • External validation was limited (12% of studies), and methodological heterogeneity was noted.

Conclusions:

  • Radiomics facilitates high-performance, noninvasive prediction of PA characteristics and outcomes.
  • Promising results in predicting PA subtypes, consistency, invasiveness, and treatment response.
  • Clinical translation is hindered by methodological heterogeneity, limited external validation, and lack of standardization.