ArticleNeurosurgical review2025
Development of a PAE-based nomogram to predict severe endocrine dysfunction after craniopharyngioma resection in children.
Article in Neurosurgical review, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
To develop and validate a Pediatric Anatomo-Endocrine (PAE)-based nomogram for predicting severe postoperative endocrine dysfunction in children undergoing craniopharyngioma (CP) resection, integrating anatomical classification, surgical factors, and baseline endocrine status. This retrospective study included 370 pediatric CP patients (≤ 18 years) who underwent primary resection at a single center between 2017 and 2024. Patients were randomly divided into a primary (n = 300) and a validation (n = 70) cohort. Endocrine dysfunction was assessed based on the number and severity of hypothalamic-pituitary axis abnormalities. A novel three-tier PAE classification was developed from preoperative MRI. Logistic regression was used to identify predictors of severe dysfunction. A nomogram was constructed and validated via ROC, calibration, and decision curve analyses. In the primary cohort, 83.4% of patients had worsened endocrine dysfunction postoperatively, with severe dysfunction increasing from 20.3% to 79.3%. Independent predictors included PAE classification, baseline endocrine status, and pituitary stalk sacrifice. The nomogram demonstrated strong performance (AUC 0.84 in primary; 0.82 in validation). Among PAE subtypes, the Global subtype posed the highest risk, with an odds ratio of 16.30 versus the Sella-Infundibulum subtype. The PAE classification stratifies hypothalamic-pituitary involvement and serves as an independent predictor of endocrine outcomes. Although validated internally in a large single-center cohort, its long-term benefit remains to be confirmed. The proposed nomogram may provide a practical, non-invasive tool for individualized risk prediction and surgical planning, but requires external multicenter validation to establish generalizability.
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