A Multicenter Swedish Histopathology Image Dataset Of Pediatric Central Nervous System Tumors
NYMAN, P.; Tampu, I. E.; Shamikh, A.; Prochazka, G.; Blystad, i.; Basmaci, E.; Diaz de Stahl, T.; Augustsson, P.; Zielinska-Chomej, K.; Cao, D.; von Salome, J.; Ardalan, A.; Somarajan, P. R.; Ljungman, G.; Lundberg, P.; Sandgren, J.; Haj-Hosseini, N.
Show abstract
Refined detection methods, more detailed tumor characterization, and adequate distinction between different pediatric tumor subtypes are necessary to improve diagnosis and treatment, enable precision medicine, and advance patient prognosis. However, the application of computational approaches to pediatric brain tumors remains limited, largely due to the lack of accessible datasets. To address part of this gap, we provide whole slide images (WSIs) of hematoxylin and eosin (H&E)-stained tissue sections from all pediatric central nervous system (CNS) samples collected in Sweden between 2013 and 2023. These data represent a population-based national cohort encompassing all six pediatric oncology centers in Sweden and are available through the Swedish Childhood Tumor Biobank (BTB). The dataset includes 1,446 WSIs of sufficient image quality with confirmed CNS tumor diagnoses, derived from 537 unique subjects (562 cases). In addition, diagnosticrelevant clinical information is included. Corresponding whole-genome sequencing (WGS), wholetranscriptome sequencing (WTS), and methylation array data are available for most tumor samples through separate resources. This H&E dataset has been specifically curated to support artificial intelligence-based analyses, while also serving broader applications in medical research and education. When combined with matched molecular data, it provides a valuable resource for advancing multimodal and precision diagnostic approaches in the pediatric population. Refined detection methods, more detailed tumor mapping and adequate distinction between different subtypes of pediatric tumors are necessary to improve treatment, enable precision medicine and improve patient prognosis. Application of computational algorithms for pediatric brain tumors is very limited mainly due to the unavailability of pediatric histology brain tumor data sets. To enable the development of AI models comprehensive datasets covering a wide range of pediatric brain tumors are needed.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Automated Tumor Segmentation and Brain Tissue Extraction from Multiparametric MRI of Pediatric Brain Tumors: A Multi-Institutional Study 95%
- Early prognostication of overall survival for pediatric diffuse midline gliomas using MRI radiomics and machine learning 94%
- Evidence of supratentorial white matter injury prior to treatment in children with posterior fossa tumours using diffusion MRI 93%
Similar papers in this journal
- Image-localized Biopsy Mapping of Brain Tumor Heterogeneity: A Single-Center Study Protocol 94%
- Comprehensive cancer-oriented biobanking resource of human samples for studies of post-zygotic genetic variation involved in cancer predisposition 92%
- Pixelwise H-score: a novel digital image analysis based-metric to quantify membrane biomarker expression from immunohistochemistry images 92%
Similar papers in this journal
- Detection of local microvascular proliferation in IDH wild-type Glioblastoma using relative Cerebral Blood Volume 94%
- A Large Scale Multi-dataset Investigation of Brain Metastases Distribution Based on Primary Cancer Type 93%
- Radiomic-Based Approaches in the Multi-metastatic Setting: A Quantitative Review 92%
Similar papers in this journal
Similar papers in this journal
- Genomic Characterization of Lung Cancer in Never-Smokers Using Deep Learning 91%
- Attention-based whole-slide image compression achieves pathologist-level pre-screening of multi-organ routine histopathology biopsies 91%
- Artificial Intelligence for Advance Requesting of Immunohistochemistry in Diagnostically Uncertain Prostate Biopsies 90%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.