Related Experiment Video
Updated: Jan 9, 2026
Endoscopic Endonasal Trans-sphenoidal Approach: Minimally Invasive Surgery for Pituitary Adenomas
Published on: January 17, 2018
Development of a PAE-based nomogram to predict severe endocrine dysfunction after craniopharyngioma resection in
Jiahao Niu1, Ying Guo2, Wentao Zhou1,3
1Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, 119 South 4th Ring West Road, Fengtai District, Beijing, 100070, China.
Insights
A new nomogram using the Pediatric Anatomo-Endocrine (PAE) classification helps predict severe endocrine dysfunction after pediatric craniopharyngioma surgery, aiding surgical planning and risk assessment.
Area of Science:
- Pediatric Endocrinology
- Neurosurgery
- Oncology
Background:
- Craniopharyngioma (CP) resection in children frequently leads to severe postoperative endocrine dysfunction.
- Accurate prediction of this dysfunction is crucial for surgical planning and patient management.
Purpose of the Study:
- To develop and validate a Pediatric Anatomo-Endocrine (PAE)-based nomogram for predicting severe postoperative endocrine dysfunction in pediatric CP patients.
- To integrate anatomical classification, surgical factors, and baseline endocrine status into a predictive model.
Main Methods:
- Retrospective analysis of 370 pediatric CP patients (≤18 years) undergoing primary resection.
- Development of a three-tier PAE classification based on preoperative MRI.
- Logistic regression to identify predictors of severe dysfunction, followed by nomogram construction and validation (ROC, calibration, decision curve analyses).
Main Results:
- 83.4% of patients experienced worsened endocrine dysfunction postoperatively; severe dysfunction increased from 20.3% to 79.3%.
- Independent predictors included PAE classification, baseline endocrine status, and pituitary stalk sacrifice.
- The nomogram showed strong predictive performance (AUC 0.84 primary, 0.82 validation); the Global PAE subtype had the highest risk (OR 16.30).
Conclusions:
- The PAE classification effectively stratifies hypothalamic-pituitary involvement and predicts endocrine outcomes.
- The developed nomogram offers a practical tool for individualized risk prediction and surgical planning in pediatric CP.
- External multicenter validation is recommended to confirm generalizability and long-term benefits.
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.
