Abstract：
Radiomics is the extraction of predefined mathematic features from medical images for predicting variables of clinical interest. Recent research has demonstrated that radiomics can be processed by artificial intelligence algorithms to reveal complex patterns and trends for diagnosis, and prediction of prognosis and response to treatment modalities in various types of cancer. Artificial intelligence tools can utilize radiological images to solve next-generation issues in clinical decision making. Bone tumors can be classified as primary and secondary (metastatic) tumors. Osteosarcoma, Ewing sarcoma, and chondrosarcoma are the dominating primary tumors of bone. The development of bone tumor model systems and relevant research, and the assessment of novel treatment methods are ongoing to improve clinical outcomes, notably for patients with metastases. Artificial intelligence and radiomics have been utilized in almost full spectrum of clinical care of bone tumors. Radiomics models have achieved excellent performance in the diagnosis and grading of bone tumors. Furthermore, the models enable to predict overall survival, metastases, and recurrence. Radiomics features have exhibited promise in assisting therapeutic planning and evaluation, especially neoadjuvant chemotherapy. This review provides an overview of the evolution and opportunities for artificial intelligence in imaging, with a focus on hand-crafted features and deep learning-based radiomics approaches. We summarize the current application of artificial intelligence-based radiomics both in primary and metastatic bone tumors, and discuss the limitations and future opportunities of artificial intelligence-based radiomics in this field. In the era of personalized medicine, our in-depth understanding of emerging artificial intelligence-based radiomics approaches will bring innovative solutions to bone tumors and achieve clinical application.
摘要：
影像组学是从医学图像中提取预定义的数学特征，以预测临床感兴趣的变量。最近的研究表明，影像组学可以通过人工智能算法进行处理，以揭示诊断的复杂模式和趋势，并预测各种类型癌症的预后和对治疗方式的反应。人工智能工具可以利用放射学图像来解决临床决策中的下一代问题。骨肿瘤可分为原发性和继发性(转移性)肿瘤。骨肉瘤,尤因肉瘤,软骨肉瘤是骨的主要原发性肿瘤。骨肿瘤模型系统的发展及相关研究,和评估新的治疗方法正在进行中，以改善临床结果，特别是对于有转移的患者。人工智能和影像组学已被用于骨肿瘤的几乎全部临床护理。影像组学模型在骨肿瘤的诊断和分级方面取得了出色的性能。此外，这些模型能够预测总体生存率，转移,和复发。影像组学功能在协助治疗计划和评估方面表现出了希望，尤其是新辅助化疗.这篇综述概述了人工智能在成像领域的发展和机遇，专注于手工制作的功能和基于深度学习的影像组学方法。我们总结了基于人工智能的影像组学在原发性和转移性骨肿瘤中的当前应用。并讨论了基于人工智能的影像组学在该领域的局限性和未来机遇。在个性化医疗时代，我们对新兴的基于人工智能的影像组学方法的深入了解将为骨肿瘤带来创新的解决方案并实现临床应用。
