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Artificial Intelligence in Primary Malignant Bone Tumor Imaging: A Narrative Review.
Platon S Papageorgiou1, Rafail Christodoulou2, Panagiotis Korfiatis3
1First Department of Orthopaedics, University General Hospital Attikon, Medical School, National and Kapodistrian University of Athens, 12462 Athens, Greece.
Artificial Intelligence (AI) enhances orthopedic oncology by improving diagnosis and treatment prediction for primary malignant bone tumors (PBT). AI-driven radiomics and deep learning offer personalized strategies, but data limitations and standardization challenges require further research.
Area of Science:
- Oncology
- Medical Imaging
- Artificial Intelligence
Background:
- Artificial Intelligence (AI) is revolutionizing orthopedic oncology for primary malignant bone tumors (PBT).
- Machine learning and deep learning techniques enhance medical imaging interpretation and clinical decision-making.
- Radiomics integrated with AI allows for precise tumor characterization and personalized therapeutic strategies.
Purpose of the Study:
- To review the evolving applications of AI in PBT management.
- To focus on AI's role in early tumor detection, imaging analysis, therapy response prediction, and histological classification.
- To highlight AI's potential in advancing precision medicine for bone tumors.
Main Methods:
- This narrative review synthesizes current research on AI applications in PBT.
- Focus on machine learning, deep learning, and radiomics, particularly convolutional neural networks.
- Analysis of AI's impact on tumor detection, segmentation, differentiation, and treatment outcome prediction.
Main Results:
- AI demonstrates significant improvements in tumor detection, segmentation, and differentiation.
- AI-driven radiomics and predictive models show promise in assessing chemotherapy efficacy and predicting treatment outcomes.
- AI enhances healthcare efficiency by reducing physician workload and improving diagnostic accuracy.
Conclusions:
- AI offers transformative potential for PBT management, advancing personalized care.
- Challenges include data scarcity, lack of standardization, and ethical considerations.
- Future research should focus on multicenter collaborations, external validation, and explainable AI for clinical integration.
