ArticleJournal of computer assisted tomography
Predicting Subtype of Growth Hormone Pituitary Adenoma based on Magnetic Resonance Imaging Characteristics.
Article in Journal of computer assisted tomography. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Pituitary neuroendocrine tumors: imaging features, clinical relevance, and a proposal for standardized MRI reporting.Neuroradiology · 2026Review
- Histopathological and radiological predictors of surgical remission failure in GH-secreting pituitary adenomas.Pituitary · 2026Review
- Preoperative Metabolic Predictors of Granulation Subtypes in Somatotroph Tumors: A Multicenter Retrospective Cohort Study.CNS neuroscience & therapeutics · 2026Article
- Factors affecting disease control after pituitary tumor resection in acromegaly: What is the current evidence?World journal of radiology · 2025Article
- Predictors of biochemical and structural response to medical therapy in patients with active acromegaly following surgery: a real-world perspective.BMC endocrine disorders · 2025Article
- Distinguishing Pituitary Metastasis and Pituitary Neuroendocrine Tumors through Conventional MR Imaging and Clinical Features.AJNR. American journal of neuroradiology · 2024Article
- Imaging of pituitary tumors: an update with the 5th WHO Classifications-part 1. Pituitary neuroendocrine tumor (PitNET)/pituitary adenoma.Japanese journal of radiology · 2023Review
- Multidisciplinary management of difficult/aggressive growth-hormone pituitary neuro-endocrine tumors.Frontiers in endocrinology · 2023Review
- Prognostic Factors for Recurrence in Pituitary Adenomas: Recent Progress and Future Directions.Diagnostics (Basel, Switzerland) · 2022Review
- An interpretable machine learning model for identifying granulation patterns in somatotroph tumors: A multi-center radiomics study.Neuro-oncology advancesArticle
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10 authors.
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Abstract
purposeThis study aimed to investigate the value of magnetic resonance (MR) characteristics in differentiating the subtypes of growth hormone pituitary adenomas. MATERIALS AND
methodsThe clinical and MR imaging data of 70 patients with growth hormone pituitary adenoma confirmed by surgery and pathology were retrospectively analyzed. The tumors were divided into dense granular (DG; 36 cases) and sparse granular subtypes (SG; 34 cases). The tumors' MR features were analyzed, including the mean and maximum diameters, T2 signal intensity, T2 relative signal intensity (rSI), homogeneity, enhancement degree, and invasiveness (Knosp grade). Mann-Whitney U test and χ2 test were used to analyze MR characteristics between the 2 groups. The independent predictors and predictive probabilities of tumor subtypes were obtained via a logistic regression model, and the efficacy was compared by receiver operating characteristic curve.
resultsThe mean and maximum diameters of growth hormone adenoma in DG and SG were 1.77 versus 2.45 and 1.95 versus 3.00 cm (median, P < 0.05), respectively. There was a significant difference between the 2 groups in T2 signal intensity and rSI (P values were 0.02 and 0.001, respectively). Most DG adenomas (86.1%) appeared as hypointense on T2 images, and 38.2% of SG adenomas were hyperintense. There was no significant difference in tumor homogeneity (P = 0.622). A significant difference was found in the Knosp grade between the 2 subtypes (P = 0.004). In addition, the enhancement degree of SG adenomas was significantly higher than that of DG adenomas (P = 0.001). Logistic regression analysis showed that high T2 rSI value and marked contrast enhancement were independent predictors of the 2 subtypes, and the odds ratios were 4.811 and 4.649, respectively. The multivariate logistic model obtained relatively high predicting efficacy, and the area under the curve, sensitivity, and specificity were 0.765, 0.882, and 0.500, respectively.
conclusionsThere are significant differences in tumor size, T2 signal intensity, T2 rSI, enhancement degree, and invasiveness between DG and SG adenomas. The logistic model based on the marked contrast enhancement and high T2 rSI value has an important value in predicting the subtype of growth hormone adenoma.
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