ArticleChild's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery2026
Development and validation of a preoperative MRI-based predictive model for cerebellar mutism syndrome in pediatric posterior fossa tumors.
Article in Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeWhile systematic reviews have summarized qualitative risk factors for postoperative cerebellar mutism syndrome (pCMS), a critical gap remains in translating these findings into a practical, quantitative tool for individualized preoperative prediction. This study aims to bridge this gap by developing and validating the first preoperative predictive model based on novel quantitative imaging indices to accurately estimate the risk of pCMS.
methodsWe retrospectively analyzed data from 73 pediatric patients who underwent posterior fossa surgery. We defined and measured a set of innovative quantitative imaging indices (including coronal index CORd, sagittal index SAGa, axial indices OAXa, OAXd, and a composite index OAXd*CORd) from preoperative MRIs. Anatomical invasion sites were also evaluated. Multivariate logistic regression was used to integrate these features into a predictive model. The model's performance was compared with two previously published tools (Rotterdam scale and Zhang model) using discrimination metrics, calibration curves, and decision curve analysis.
resultsInvasion of the middle cerebellar peduncle (p = 0.001), superior cerebellar peduncle (p < 0.001), and brainstem (p = 0.003) were significant anatomical risk factors. The quantitative indices SAGa, CORd, OAXa, OAXd, and OAXd*CORd showed significant differences between pCMS and non-pCMS groups (p < 0.05). Our model had the highest AUC (0.850), sensitivity (92.31%) and negative predictive value (97.62%). Rotterdam scale had the highest overall accuracy (83.56%) and specificity (86.67%) of all three models. Our model yielded the highest net benefit at lower thresholds of risk (10-25%).
conclusionThis study moves beyond existing qualitative risk factor lists by establishing a highly accurate, quantitative predictive model for pCMS. Comparison with existing tools demonstrates that our model offers superior sensitivity for identifying high-risk patients. The novel imaging indices provide refined insights into the pathophysiological mechanisms, potentially involving cerebello-thalamic pathways. This model serves as a practical clinical tool for preoperative risk assessment, surgical planning, and patient counseling.
Indexed as
Identifiers
42020876What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.