⚡ Quick Summary
This systematic review explores the role of machine learning (ML) in pediatric oncology, analyzing 42 studies that highlight its potential in improving patient outcomes. The findings suggest that ML can enhance diagnosis, treatment response prediction, and clinical decision-making in pediatric cancer care.
🔍 Key Details
- 📊 Total studies analyzed: 42 studies
- 🧩 Types of tumors: 9 liquid tumors, 13 solid tumors, 20 CNS tumors
- ⚙️ ML techniques used: Neural networks, k-nearest neighbors, random forests, support vector machines, naive Bayes
- 🏆 Key goals: Classification, treatment response prediction, dose optimization
🔑 Key Takeaways
- 💡 Machine learning shows promise in enhancing pediatric cancer diagnosis and treatment.
- 📈 Significant strengths include treatment response prediction and automated analysis.
- ⚠️ Common issues identified: limited sample sizes and lack of external validation cohorts.
- 🌟 ML techniques often matched or outperformed traditional physician assessments.
- 🔍 Future research is needed to establish robust methodological standards.
- 🧠 The field is still in its infancy, indicating room for growth and development.
- 🌍 Potential applications could significantly improve clinical care for pediatric oncology patients.
📚 Background
Pediatric oncology presents unique challenges due to the complexity of cancer in children and the need for tailored treatment approaches. As the volume of data in this field grows, machine learning emerges as a powerful tool to analyze medical and biological risk factors, potentially leading to improved patient outcomes and more personalized care.
🗒️ Study
This systematic review adhered to the PRISMA guidelines and involved a comprehensive search across four databases: Scopus, Web of Science, PubMed, and Cochrane Library. A total of 1,536 studies were initially retrieved, with 42 studies ultimately meeting the eligibility criteria for inclusion in the review.
📈 Results
The review identified a diverse range of ML applications in pediatric oncology, with a focus on classification, treatment response prediction, and dose optimization. Notably, the strengths of the identified studies included their ability to provide automated analyses that often matched or exceeded the performance of human physicians. However, the review also highlighted significant variability in clinical applicability and reporting standards.
🌍 Impact and Implications
The findings of this review underscore the transformative potential of machine learning in pediatric oncology. By leveraging advanced algorithms, healthcare providers can enhance diagnostic accuracy, optimize treatment plans, and ultimately improve patient outcomes. As the field continues to evolve, establishing standardized methodologies will be crucial for maximizing the therapeutic applicability of ML in clinical settings.
🔮 Conclusion
This systematic review illustrates the great promise of machine learning in pediatric oncology, paving the way for improved diagnosis, treatment, and monitoring of pediatric cancer patients. As research progresses, it is essential to focus on developing robust methodologies and standards to ensure that the benefits of ML are fully realized in clinical practice.
💬 Your comments
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The Role of Machine Learning Approaches in Pediatric Oncology: A Systematic Review.
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.
Author: [‘Elsayid NN’, ‘Aydaross Adam EI’, ‘Yousif Mahmoud SM’, ‘Saadeldeen H’, ‘Nauman M’, ‘Ali Ahmed TA’, ‘Hamza Yousif BA’, ‘Awad Taha AI’]
Journal: Cureus
Citation: Elsayid NN, et al. The Role of Machine Learning Approaches in Pediatric Oncology: A Systematic Review. The Role of Machine Learning Approaches in Pediatric Oncology: A Systematic Review. 2025; 17:e77524. doi: 10.7759/cureus.77524