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Deep Learning and Multidisciplinary Imaging in Pediatric Surgical Oncology: A Scoping Review
M A D Buser1, J K van der Rest1, M H W A Wijnen1
1Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands.
Cancer Medicine
|January 15, 2025
Summary
Deep Learning (DL) shows promise in pediatric surgical oncology imaging. Further research and validation are crucial for clinical application, guiding future advancements for improved child cancer outcomes.
Area of Science:
- Medical imaging analysis
- Computational pathology
- Radiology informatics
Background:
- Medical images are vital for diagnosing and treating pediatric solid tumors.
- Advancements in radiology and pathology necessitate sophisticated image processing, like Deep Learning (DL).
- DL applications are increasingly important in image-based diagnostics for pediatric oncology.
Purpose of the Study:
- To review the current applications of Deep Learning (DL) in multidisciplinary imaging within pediatric surgical oncology.
- To provide an overview of existing DL research in this specialized field.
Main Methods:
- A comprehensive literature search was performed across PubMed, Embase, and Scopus databases.
- A total of 2056 articles were initially identified and subsequently screened.
- Articles were categorized into radiology, pathology, and other image-based diagnostics.
Main Results:
- 36 articles were included, with 22 in radiology, 9 in pathology, and 5 in other diagnostics.
- Identified DL tasks included classification, prediction, segmentation, and synthesis.
- Inconsistent study methodologies prevented general performance statements; technical and clinical validation are essential for DL implementation.
Conclusions:
- This review summarizes DL research in pediatric surgical oncology imaging.
- Leveraging insights from adult DL applications can accelerate progress in pediatric oncology.
- Continued development and validation of DL are key to improving outcomes for children with cancer.
