Papageorgiou Platon S, Christodoulou Rafail, Korfiatis Panagiotis, Papagelopoulos Dimitra P, Papakonstantinou Olympia, Pham Nancy, Woodward Amanda, Papagelopoulos Panayiotis J
First Department of Orthopaedics, University General Hospital Attikon, Medical School, National and Kapodistrian University of Athens, 12462 Athens, Greece.
Department of Radiology, School of Medicine, Stanford University, Stanford, CA 94305, USA.
Diagnostics (Basel). 2025 Jul 4;15(13):1714. doi: 10.3390/diagnostics15131714.
Artificial Intelligence (AI) has emerged as a transformative force in orthopedic oncology, offering significant advances in the diagnosis, classification, and prediction of treatment response for primary malignant bone tumors (PBT). Through machine learning and deep learning techniques, AI leverages computational algorithms and large datasets to enhance medical imaging interpretation and support clinical decision-making. The integration of radiomics with AI enables the extraction of quantitative features from medical images, allowing for precise tumor characterization and the development of personalized therapeutic strategies. Notably, convolutional neural networks have demonstrated exceptional capabilities in pattern recognition, significantly improving tumor detection, segmentation, and differentiation. This narrative review synthesizes the evolving applications of AI in PBTs, focusing on early tumor detection, imaging analysis, therapy response prediction, and histological classification. AI-driven radiomics and predictive models have yielded promising results in assessing chemotherapy efficacy, optimizing preoperative imaging, and predicting treatment outcomes, thereby advancing the field of precision medicine. Innovative segmentation techniques and multimodal imaging models have further enhanced healthcare efficiency by reducing physician workload and improving diagnostic accuracy. Despite these advancements, challenges remain. The rarity of PBTs limits the availability of robust, high-quality datasets for model development and validation, while the lack of standardized imaging protocols complicates reproducibility. Ethical considerations, including data privacy and the interpretability of complex AI algorithms, also warrant careful attention. Future research should prioritize multicenter collaborations, external validation of AI models, and the integration of explainable AI systems into clinical practice. Addressing these challenges will unlock AI's full potential to revolutionize PBT management, ultimately improving patient outcomes and advancing personalized care.
人工智能（AI）已成为骨科肿瘤学中的一股变革力量，在原发性恶性骨肿瘤（PBT）的诊断、分类及治疗反应预测方面取得了重大进展。通过机器学习和深度学习技术，人工智能利用计算算法和大型数据集来加强医学影像解读并支持临床决策。放射组学与人工智能的整合能够从医学影像中提取定量特征，实现肿瘤的精准特征描述并制定个性化治疗策略。值得注意的是，卷积神经网络在模式识别方面展现出卓越能力，显著改善了肿瘤检测、分割及鉴别。本叙述性综述综合了人工智能在原发性恶性骨肿瘤中的不断演变的应用，重点关注早期肿瘤检测、影像分析、治疗反应预测及组织学分类。人工智能驱动的放射组学和预测模型在评估化疗疗效、优化术前影像及预测治疗结果方面取得了有前景的成果，从而推动了精准医学领域的发展。创新的分割技术和多模态影像模型通过减轻医生工作量和提高诊断准确性进一步提升了医疗效率。尽管取得了这些进展，但挑战依然存在。原发性恶性骨肿瘤的罕见性限制了用于模型开发和验证的强大、高质量数据集的可用性，而缺乏标准化的影像协议使可重复性变得复杂。伦理考量，包括数据隐私和复杂人工智能算法的可解释性，也值得密切关注。未来研究应优先开展多中心合作、对人工智能模型进行外部验证，并将可解释人工智能系统整合到临床实践中。应对这些挑战将释放人工智能在彻底改变原发性恶性骨肿瘤管理方面的全部潜力，最终改善患者预后并推进个性化医疗。
