The Role of Machine Learning Approaches in Pediatric Oncology: A Systematic Review.

IF 1 Q3 MEDICINE, GENERAL & INTERNAL Cureus Pub Date : 2025-01-16 eCollection Date: 2025-01-01 DOI:10.7759/cureus.77524
Nojoud Noureldayim Elsayid, Elwaleed Idrees Aydaross Adam, Samah Mohamed Yousif Mahmoud, Hoyam Saadeldeen, Muhammad Nauman, Tayseir Ahmed Ali Ahmed, Belgees Altigani Hamza Yousif, Allaa Ibrahim Awad Taha
{"title":"The Role of Machine Learning Approaches in Pediatric Oncology: A Systematic Review.","authors":"Nojoud Noureldayim Elsayid, Elwaleed Idrees Aydaross Adam, Samah Mohamed Yousif Mahmoud, Hoyam Saadeldeen, Muhammad Nauman, Tayseir Ahmed Ali Ahmed, Belgees Altigani Hamza Yousif, Allaa Ibrahim Awad Taha","doi":"10.7759/cureus.77524","DOIUrl":null,"url":null,"abstract":"<p><p>To enhance patient outcomes in pediatric cancer, a better understanding of the medical and biological risk variables is required. With the growing amount of data accessible to research in pediatric cancer, machine learning (ML) is a form of algorithmic inference from sophisticated statistical techniques. In addition to highlighting developments and prospects in the field, the objective of this systematic study was to methodically describe the state of ML in pediatric oncology. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to search for relevant studies on four distinct databases (Scopus, Web of Science, PubMed, and Cochrane Library). A total of 1536 relevant studies were retrieved to the EndNote library (Clarivate, Philadelphia, USA) where duplicates were removed and the rest of the studies were assessed for eligibility based on titles, abstracts, and the availability of full-text articles. After assessing the studies for eligibility, we found 42 studies eligible for inclusion in this systematic review. We found nine studies on liquid tumors, 13 on solid tumors, and 20 on central nervous system (CNS) tumors. ML goals included classification, treatment response prediction, and dose optimization. Neural networks, k-nearest neighbors, random forests, support vector machines, and naive Bayes were among the techniques employed. The identified studies' strengths included treatment response prediction and automated analysis that matched or outperformed physician comparators. Significant variation in clinical applicability, criteria for reporting, limited sample numbers, and the absence of external validation cohorts were among the common issues. We found places where ML can improve clinical care in manners that would not be possible otherwise. Even though ML has great promise for enhancing pediatric cancer diagnosis, decision-making, and monitoring, the discipline is still in its infancy, and standards and recommendations will support future research to guarantee robust methodologic design and maximize therapeutic applicability.</p>","PeriodicalId":93960,"journal":{"name":"Cureus","volume":"17 1","pages":"e77524"},"PeriodicalIF":1.0000,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11736508/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cureus","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.7759/cureus.77524","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q3","JCRName":"MEDICINE, GENERAL & INTERNAL","Score":null,"Total":0}
引用次数: 0

Abstract

To enhance patient outcomes in pediatric cancer, a better understanding of the medical and biological risk variables is required. With the growing amount of data accessible to research in pediatric cancer, machine learning (ML) is a form of algorithmic inference from sophisticated statistical techniques. In addition to highlighting developments and prospects in the field, the objective of this systematic study was to methodically describe the state of ML in pediatric oncology. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to search for relevant studies on four distinct databases (Scopus, Web of Science, PubMed, and Cochrane Library). A total of 1536 relevant studies were retrieved to the EndNote library (Clarivate, Philadelphia, USA) where duplicates were removed and the rest of the studies were assessed for eligibility based on titles, abstracts, and the availability of full-text articles. After assessing the studies for eligibility, we found 42 studies eligible for inclusion in this systematic review. We found nine studies on liquid tumors, 13 on solid tumors, and 20 on central nervous system (CNS) tumors. ML goals included classification, treatment response prediction, and dose optimization. Neural networks, k-nearest neighbors, random forests, support vector machines, and naive Bayes were among the techniques employed. The identified studies' strengths included treatment response prediction and automated analysis that matched or outperformed physician comparators. Significant variation in clinical applicability, criteria for reporting, limited sample numbers, and the absence of external validation cohorts were among the common issues. We found places where ML can improve clinical care in manners that would not be possible otherwise. Even though ML has great promise for enhancing pediatric cancer diagnosis, decision-making, and monitoring, the discipline is still in its infancy, and standards and recommendations will support future research to guarantee robust methodologic design and maximize therapeutic applicability.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
机器学习方法在儿科肿瘤学中的作用:系统综述。
为了提高儿童癌症患者的预后,需要更好地了解医学和生物学风险变量。随着儿科癌症研究可获得的数据越来越多,机器学习(ML)是一种从复杂的统计技术中进行算法推断的形式。除了强调该领域的发展和前景外,本系统研究的目的是系统地描述小儿肿瘤学中ML的状态。我们遵循系统评价和荟萃分析首选报告项目(PRISMA)指南,在四个不同的数据库(Scopus、Web of Science、PubMed和Cochrane Library)中搜索相关研究。共有1536项相关研究被检索到EndNote文库(Clarivate, Philadelphia, USA),其中删除了重复的研究,并根据标题、摘要和全文文章的可用性评估了其余研究的资格。在评估研究的合格性后,我们发现有42项研究符合纳入本系统评价的条件。我们发现9项关于液体肿瘤的研究,13项关于实体肿瘤的研究,20项关于中枢神经系统肿瘤的研究。ML目标包括分类、治疗反应预测和剂量优化。神经网络、k近邻、随机森林、支持向量机和朴素贝叶斯都是采用的技术。确定的研究的优势包括治疗反应预测和自动分析,与医生比较相匹配或优于医生比较。临床适用性的显著差异、报告标准、有限的样本数和缺乏外部验证队列是常见的问题。我们发现机器学习可以以其他方式改善临床护理的地方是不可能的。尽管ML在提高儿童癌症诊断、决策和监测方面有很大的前景,但该学科仍处于起步阶段,标准和建议将支持未来的研究,以保证稳健的方法设计和最大化的治疗适用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Evaluating the Performance of ChatGPT-4o Oncology Expert in Comparison to Standard Medical Oncology Knowledge: A Focus on Treatment-Related Clinical Questions. A Novel Approach to the Management of an Intra-abdominal Abscess: A Case Report and Literature Review. Comparing the Efficacy of a Unique Advanced Bioengineered Type-I Collagen-Based Skin Substitute Versus an Amnion Graft With Standard of Care in the Treatment of Non-healing Diabetic Foot Ulcers: A Randomized Clinical Trial. Exploring the Link Between Resilience and Disease Severity in Chronic Rhinosinusitis With Nasal Polyps. Physical Activity and Incident Pancreatic Cancer: Results From the UK Biobank Prospective Cohort.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1