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Related Experiment Videos

Dual-stage deep learning framework for neuroblastoma differentiation by integrating cell segmentation and multiscale

Jieni Xiong1, Zhu Zhu2,3, Weizhong Gu4

  • 1Surgical Oncology, Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Children and Adolescents' Health and Diseases, Hangzhou, Zhejiang, China.

World Journal of Pediatric Surgery
|July 9, 2026
PubMed
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A novel deep learning framework precisely diagnoses neuroblastoma (NB), a common childhood cancer. This AI tool enhances pathological diagnosis by overcoming limitations of manual interpretation, improving accuracy for critical clinical decisions.

Area of Science:

  • Computational pathology
  • Artificial intelligence in oncology
  • Pediatric oncology

Background:

  • Accurate pathological diagnosis of neuroblastoma (NB), the most common pediatric extracranial solid tumor, is crucial for treatment.
  • Traditional manual diagnosis faces challenges due to tumor heterogeneity and inter-observer variability.
  • Objective, quantitative intelligent diagnostic tools are needed to improve NB classification.

Purpose of the Study:

  • To develop and validate a two-stage deep learning framework for precise neuroblastoma classification.
  • To enhance diagnostic accuracy and consistency in neuroblastoma pathological analysis.
  • To provide an objective auxiliary tool for clinical decision-making in neuroblastoma cases.

Main Methods:

  • A two-stage deep learning framework utilizing hybrid supervision was developed.
Keywords:
Diagnostic ImagingMedical OncologyPathologyPediatrics

Related Experiment Videos

  • Stage 1: Enhanced Medical Segment Anything Model (MedSAM) with cross-attention for pixel-level tumor cell segmentation (Dice coefficient: 0.94).
  • Stage 2: Optimized Swin Transformer (ST) for multiscale classification with a confidence voting strategy for full slice analysis.

    • Main Results:

    • The model achieved an AUC of 0.864 on an independent dataset of 185 whole-slide images.
    • Performance significantly outperformed existing methods, showing an 8.7% improvement over the second-best model.
    • Highest recognition accuracy was observed for the poorly differentiated neuroblastoma subtype.

    Conclusions:

    • The deep learning framework effectively addresses tumor microenvironment interference and small-sample generalization.
    • A cascaded feature extraction and decision-making mechanism provides a robust intelligent auxiliary tool.
    • This AI-driven approach offers a significant advancement in neuroblastoma pathological diagnosis.