人工智能在骨和软组织肿瘤影像诊断与治疗中的应用

Applications of artificial intelligence for imaging-driven diagnosis and treatment of bone and soft tissue tumors

骨和软组织肿瘤发生于肌肉骨骼系统,骨和软组织恶性肿瘤占所有人类恶性肿瘤的0.2%,如不及时诊断和治疗,患者可能面临预后较差的风险。图像判读在骨和软组织肿瘤诊断中起着越来越重要的作用,人工智能可用于整合临床肿瘤学中的大量多维数据,推导模型并预测结果,改善治疗决策。其中,深度学习是人工智能中常用的方法之一,其主要采用卷积神经网络,卷积神经网络通过对数据集进行反复训练和参数的迭代调整来得到最终模型。基于深度学习的人工智能模型已成功应用于骨和软组织肿瘤领域的多个方面,包括图像分割、肿瘤检测与分类、肿瘤分级与分期、化疗效果评估以及复发与预后预测等。文章介绍了人工智能在骨、软组织肿瘤医学图像诊断与治疗中的应用原理和现状,并广泛探讨了此领域目前面临的挑战和未来的前景。

Bone and soft tissue tumors occur in the musculoskeletal system,and malignant bone tumors of bone and soft tissue account for 0.2%of all human malignant tumors,and if not diagnosed and treated in a timely manner,patients may be at risk of a poor prognosis.Image interpretation plays an increasingly important role in the diagnosis of bone and soft tissue tumors.Artificial intelligence(AI)can be applied in clinical treatment to integrate large amounts of multidimensional data,derive models,predict outcomes,and improve treatment decisions.Among these methods,deep learning is a widely employed technique in AI that predominantly utilizes convolutional neural networks(CNN).The network is implemented through repeated training of datasets and iterative parameter adjustments.Deep learning-based AI models have successfully been applied to various aspects of bone and soft tissue tumors,encompassing but not limiting in image segmentation,tumor detection,classification,grading and staging,chemotherapy effect evaluation,recurrence and prognosis prediction.This paper provides a comprehensive review of the principles and current state of AI in the medical image diagnosis and treatment of bone and soft tissue tumors.Additionally,it explores the present challenges and future prospects in this field.