Fractal-geometry analysis of pediatric posterior fossa tumors – a preoperative tool for prediction of histopathology

The most common types of pediatric posterior fossa tumors are pilocytic astrocytomas, embryonal tumors, and ependymomas. Preoperative knowledge of the pathological diagnosis may facilitate optimal surgical management. The complex geometry of tumors can be characterized by using different metrics, such as fractal geometry parameters that play an important role in the description of irregular, rough shapes. Our aim was to perform a fractal analysis of pediatric posterior fossa tumors and identify potential new radiological biomarkers. We conducted a retrospective clinical study using preoperative MRI images and clinical data from pediatric patients who had undergone surgery for posterior fossa tumors. T1, ceT1, T2 and FLAIR sequences were acquired for all patients. The tumors were segmented using ITK-SNAP software, then fractal analysis, t-tests, Fischer exact tests, logistic regression, and ROC analysis were performed. Forty-four patients met the selection criteria. T-tests evaluating tumor volume, fractal dimension (FD), FLAIR lacunarity index (LI), and the Fischer test assessing the cystic component revealed significant differences among the three main tumor types. The prediction formula was constructed by applying logistic regression to weight the selected factors, achieving an AUC of 0.793 (95% CI = 0.628–0.921). Our study suggests that fractal metrics are useful tools for the preoperative estimation of histological diagnosis.

Authors

Institutions

Publication Details

Journal
Neurosurgical Review
Published
2026-01-22
DOI
https://doi.org/10.1007/s10143-025-04078-9
Primary Topic
Glioma Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Fractal-geometry analysis of pediatric posterior fossa tumors – a preoperative tool for prediction of histopathology

János Báskay, M. Balazs, Péter Várallyay, Tamás Mezei et al.
Neurosurgical Review
Glioma Diagnosis and Treatment
article

Fractal-geometry analysis of pediatric posterior fossa tumors – a preoperative tool for prediction of histopathology

János Báskay, M. Balazs, Péter Várallyay, Tamás Mezei, Péter Banczerowski, Péter Pollner
article en

Abstract

The most common types of pediatric posterior fossa tumors are pilocytic astrocytomas, embryonal tumors, and ependymomas. Preoperative knowledge of the pathological diagnosis may facilitate optimal surgical management. The complex geometry of tumors can be characterized by using different metrics, such as fractal geometry parameters that play an important role in the description of irregular, rough shapes. Our aim was to perform a fractal analysis of pediatric posterior fossa tumors and identify potential new radiological biomarkers. We conducted a retrospective clinical study using preoperative MRI images and clinical data from pediatric patients who had undergone surgery for posterior fossa tumors. T1, ceT1, T2 and FLAIR sequences were acquired for all patients. The tumors were segmented using ITK-SNAP software, then fractal analysis, t-tests, Fischer exact tests, logistic regression, and ROC analysis were performed. Forty-four patients met the selection criteria. T-tests evaluating tumor volume, fractal dimension (FD), FLAIR lacunarity index (LI), and the Fischer test assessing the cystic component revealed significant differences among the three main tumor types. The prediction formula was constructed by applying logistic regression to weight the selected factors, achieving an AUC of 0.793 (95% CI = 0.628–0.921). Our study suggests that fractal metrics are useful tools for the preoperative estimation of histological diagnosis.

Neurosurgical ReviewVol. 49(1)
Semmelweis University (HU), Eötvös Loránd University (HU)
Semmelweis Egyetem
Openalex Percentile: Top 81%
Glioma Diagnosis and Treatment
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.