Assessing Tumor Morphological Complexity Using Fractal Analysis of Contrast-Enhanced CT for Risk Stratification in Pediatric Neuroblastoma.
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PURPOSE: To evaluate whether tumor morphological complexity, quantified via fractal analysis of contrast-enhanced CT images, can support risk stratification in pediatric neuroblastoma. MATERIALS AND METHODS: This retrospective study included 222 pediatric patients with pathologically confirmed neuroblastoma. Tumor regions of interest (ROIs) were manually delineated slice-by-slice on contrast-enhanced CT images at initial diagnosis. Fractal dimension (FD) values were calculated using the box-counting method across multiple spatial scales (ε = 2, 4, 8, 16, and 32) in MATLAB. Extracted metrics included the two-dimensional maximum, mean, minimum, and median FD values, along with the three-dimensional global FD. Associations between FD and clinical/pathological variables, including MYCN amplification, Shimada histology, International Neuroblastoma Risk Group (INRG) stage, Children's Oncology Group (COG) risk classification, and overall survival, were statistically assessed. RESULTS: FD values were significantly higher in tumors with MYCN amplification and unfavorable Shimada histology (P < 0.05). Significant differences in FD were also observed among INRG stages, especially between L1 vs. L2 and L1 vs. M (P < 0.05), with L2 and M stage tumors exhibiting greater morphological complexity. Moreover, FD metrics increased progressively across low-, intermediate-, and high-risk COG groups (P < 0.05). Multivariate Cox proportional hazards regression analysis revealed that only global FD was independently associated with overall survival (P = 0.021). CONCLUSION: FD metrics derived from contrast-enhanced CT images are significantly associated with established clinical/pathological risk factors and overall survival in pediatric neuroblastoma. FD may serve as a non-invasive imaging biomarker to assist in risk stratification and clinical decision-making.