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Enhancing efficiency in pediatric brain tumor segmentation using a pathologically diverse single-center clinical

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Summary

Deep learning segmentation accurately delineates whole pediatric brain tumors (PBTs) and T2-hyperintensity, comparable to human experts. Further refinement is needed for enhancing tumor and cystic component segmentation in PBTs.

Keywords:
MRI segmentationdeep learningnnU-Netpediatric brain tumourstumor subregions

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Area of Science:

  • Medical imaging analysis
  • Artificial intelligence in oncology

Background:

  • Pediatric brain tumors (PBTs) are diverse and challenging to diagnose and treat.
  • Deep learning (DL) shows promise for PBT segmentation but requires validation across subtypes and MRI protocols.

Purpose of the Study:

  • To evaluate the performance of a 3D nnU-Net model for segmenting various pediatric brain tumor subregions.
  • To assess DL segmentation accuracy across different PBT subtypes, MRI sequences, and tumor locations.

Main Methods:

  • Retrospective analysis of 174 pediatric patients with diverse brain tumors.
  • Utilized MRI sequences (T1, T1-C, T2, FLAIR) with manual annotations for whole tumor (WT), T2-hyperintensity (T2H), enhancing tumor (ET), and cystic component (CC).
  • Trained and tested a 3D nnU-Net model, evaluating performance using Dice Similarity Coefficient (DSC) against human variability.

Main Results:

  • The DL model achieved robust performance for WT and T2H segmentation (mean DSC: 0.85), comparable to human annotators.
  • Moderate accuracy was observed for ET segmentation (mean DSC: 0.75), with poor performance for CC (mean DSC: 0.26).
  • Segmentation accuracy varied by tumor type, MRI sequence combination, and location; T1, T1-C, and T2 sequences yielded results similar to the full protocol.

Conclusions:

  • Deep learning-based segmentation is feasible and effective for WT and T2H in PBTs.
  • Challenges persist in segmenting ET and CC, necessitating further model development.
  • Findings suggest potential for protocol simplification and automated volumetric assessment in pediatric neuro-oncology.