Assessing Tumor Morphological Complexity Using Fractal Analysis of Contrast-Enhanced CT for Risk Stratification in...

Area of Science:

• Radiology
• Oncology
• Medical Imaging Analysis

Background:

• Pediatric neuroblastoma risk stratification is crucial for treatment planning.
• Accurate assessment of tumor aggressiveness is essential for optimizing patient outcomes.
• Novel imaging biomarkers are needed to complement existing clinical and pathological data.

Purpose of the Study:

• To assess if tumor morphological complexity, measured by fractal analysis (FD) of contrast-enhanced CT images, can aid in risk stratification for pediatric neuroblastoma.
• To investigate the association between FD values and established clinical/pathological risk factors.
• To determine if FD can predict overall survival in neuroblastoma patients.

Main Methods:

• Retrospective analysis of contrast-enhanced CT scans from 222 pediatric neuroblastoma patients.
• Manual delineation of tumor regions of interest (ROIs) and calculation of 2D and 3D fractal dimension (FD) values using the box-counting method.
• Statistical assessment of correlations between FD metrics and MYCN amplification, Shimada histology, INRG stage, COG risk classification, and overall survival.

Main Results:

• FD values were significantly higher in tumors with MYCN amplification and unfavorable Shimada histology (P < 0.05).
• Morphological complexity (FD) increased with higher INRG stages (L2/M vs. L1) and across COG risk groups (low to high).
• Global FD was independently associated with overall survival (P = 0.021) in multivariate analysis.

Conclusions:

• Fractal dimension metrics derived from CT imaging are significantly associated with key risk factors in pediatric neuroblastoma.
• FD analysis offers a non-invasive method to quantify tumor complexity.
• FD shows potential as an imaging biomarker to support risk stratification and clinical decision-making in pediatric neuroblastoma.