Prediction of CTNNB1 Mutation Status in Pediatric Cystic Adamantinomatous Craniopharyngioma by Using Preoperative Magnetic Resonance Imaging Manifestation

Objective: CTNNB1-targeted inhibitor is demonstrated to be an effective neoadjuvant therapy in adamantinomatous craniopharyngioma (ACP) patients and cystic degeneration is a canonical sign of pediatric ACP. This study aimed to investigate the relationship between the cystic performances and CTNNB1 mutation (CTNNB1 MUT) status so as to analyze the possible diagnostic criteria of CTNNB1 MUT in pediatric cystic ACP (PCACP). Methods: Patient's population, clinical characteristics, tissue samples and MRI data were collected and summarized in PCACP patients. The results were compared between CTNNB1 MUT and CTNNB1 wild-type (WT) groups according to the Sanger sequencing. MRI features of the cyst were also recorded. The receiving operating characteristic (ROC) curve analysis was applied to evaluate the differential diagnostic value. Results: 19 of the 61 patients manifested CTNNB1 MUT PCACP and 42 patients were CTNNB1 WT PCACP. Multiple cysts, irregular shape of cyst, hypo-intense interior signal of cyst on non-contrast T1W1, compression with optic chiasm and pituitary stalk and enhancement signal of cystic wall have been demonstrated in CTNNB1 MUT PCACP patients on MRI. Only the Area under the curve (AUC) values of quantity of cyst, shape of cyst and interior signal of cyst on non-contrast T1W1 were over 0.7. For criteria based on the combination of the 6 characteristic features, the AUC value was 0.928. Conclusion: Preoperative MRI may provide an effective value in predicting PCACP patients with CTNNB1 MUT and offer potential evidence for preoperative management with molecular targeted agents.

Automated summary

This study explored the relationship between cystic performances and CTNNB1 mutation in pediatric cystic ACP patients, finding that CTNNB1 MUT PCACP patients often exhibit multiple cysts, irregular shape, and specific MRI features. The combination of these features can enhance diagnostic accuracy, offering valuable insights for preoperative management.