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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
Summary
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.
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.
