Deep Learning and Multidisciplinary Imaging in Pediatric Surgical Oncology: A Scoping Review

Abstract

Background: Medical 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). Aim: Our review focused on the use of DL in multidisciplinary imaging in pediatric surgical oncology. Methods: A search was conducted within three databases (Pubmed, Embase, and Scopus), and 2056 articles were identified. Three separate screenings were performed for each identified subfield. Results: In 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. Conclusion: In 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.