ArticleCancer medicine2025
Deep Learning and Multidisciplinary Imaging in Pediatric Surgical Oncology: A Scoping Review.
Article in Cancer medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Applications of artificial intelligence in early childhood health management: a systematic review from fetal to pediatric periods.Frontiers in pediatrics · 2025Pooled it
- Application of artificial intelligence in paediatric oncology imaging.Pediatric radiology · 2026Review
- Deep Learning-Based Analysis of Gene Expression Data and Gene-Related Information in Pediatric Surgical Oncology: A Scoping Review.Cancer medicine · 2026Article
- Article
- Evaluation of UNeXt for Automatic Bone Surface Segmentation on Ultrasound Imaging in Image-Guided Pediatric Surgery.Bioengineering (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMedical images play an important role in diagnosis and treatment of pediatric solid tumors. The field of radiology, pathology, and other image-based diagnostics are getting increasingly important and advanced. This indicates a need for advanced image processing technology such as Deep Learning (DL).
aimOur review focused on the use of DL in multidisciplinary imaging in pediatric surgical oncology.
methodsA search was conducted within three databases (Pubmed, Embase, and Scopus), and 2056 articles were identified. Three separate screenings were performed for each identified subfield.
resultsIn total, we identified 36 articles, divided between radiology (n = 22), pathology (n = 9), and other image-based diagnostics (n = 5). Four types of tasks were identified in our review: classification, prediction, segmentation, and synthesis. General statements about the studies'' performance could not be made due to the inhomogeneity of the included studies. To implement DL in pediatric clinical practice, both technical validation and clinical validation are of uttermost importance.
conclusionIn conclusion, our review provided an overview of all DL research in the field of pediatric surgical oncology. The more advanced status of DL in adults should be used as guide to move the field of DL in pediatric oncology further, to keep improving the outcomes of children with cancer.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.