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Multicenter Histology Image Integration and Multiscale Deep Learning Support Machine Learning-Enabled Pediatric

Adam H Thiesen1,2, Sergii Domanskyi1, Ali Foroughi Pour1

  • 1The Jackson Laboratory for Genomic Medicine , Farmington, Connecticut.

Cancer Research
|January 2, 2026
PubMed
Summary
This summary is machine-generated.

Deep learning models accurately classify pediatric sarcoma subtypes from digital histology slides, improving diagnosis. This computational approach enhances accuracy and efficiency for rare cancer identification.

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Area of Science:

  • Computational pathology
  • Artificial intelligence in oncology
  • Digital histopathology

Background:

  • Pediatric sarcomas are rare and diagnostically challenging, often needing specialized expertise and expensive genetic tests.
  • Current diagnostic methods for pediatric sarcomas can be limited by rarity and subtype diversity.
  • Overcoming diagnostic barriers in pediatric sarcoma classification is crucial for timely and accurate treatment.

Purpose of the Study:

  • To develop and validate a deep learning pipeline for accurate pediatric sarcoma subtype classification using digitized histology slides.
  • To assess the generalizability of computational models across multiple institutions and datasets.
  • To improve the efficiency and accessibility of pediatric sarcoma diagnosis through advanced AI methods.

Main Methods:

  • A harmonized dataset of 867 whole slide images (WSIs) from three medical centers and the Children's Oncology Group (COG) was utilized.
  • Multiple deep learning architectures, including convolutional neural networks (CNNs) and vision transformers (ViTs), were evaluated.
  • Input parameters like tile size and resolution were optimized, and SAMPLER-based WSI representations were employed.

Main Results:

  • Advanced ViT foundation models (UNI, CONCH) significantly outperformed previous methods.
  • The pipeline achieved high performance in distinguishing rhabdomyosarcoma (RMS) from non-rhabdomyosarcoma (NRSTS) (AUC 0.969) and RMS subtypes (AUC 0.961).
  • A two-stage approach successfully identified Ewing sarcoma from other NRSTS (AUC 0.929) with significantly faster training times compared to conventional transformers.

Conclusions:

  • Digital histopathology combined with rigorous image harmonization offers a powerful solution for pediatric sarcoma classification.
  • Deep learning models, particularly advanced ViTs, demonstrate high accuracy and efficiency in classifying rare pediatric cancers.
  • This computational approach has the potential to reduce diagnostic challenges and improve patient outcomes for pediatric sarcoma.