Jiao C B, Liu L, Liu W F
Department of Orthopaedic Oncology Surgery, Beijing Jishuitan Hospital, Fourth Medical College of Peking University, Beijing 100035, China.
Zhonghua Zhong Liu Za Zhi. 2024 Sep 23;46(9):855-861. doi: 10.3760/cma.j.cn112152-20231024-00215.
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.
骨与软组织肿瘤发生于肌肉骨骼系统，骨与软组织恶性肿瘤占人类所有恶性肿瘤的0.2%，若不及时诊断和治疗，患者可能预后不良。影像解读在骨与软组织肿瘤的诊断中发挥着越来越重要的作用。人工智能（AI）可应用于临床治疗，整合大量多维度数据、推导模型、预测结果并改善治疗决策。在这些方法中，深度学习是AI中广泛应用的技术，主要利用卷积神经网络（CNN）。该网络通过对数据集的反复训练和迭代参数调整来实现。基于深度学习的AI模型已成功应用于骨与软组织肿瘤的各个方面，包括但不限于图像分割、肿瘤检测、分类、分级和分期、化疗效果评估、复发和预后预测。本文全面综述了AI在骨与软组织肿瘤医学影像诊断与治疗中的原理和现状。此外，还探讨了该领域目前面临的挑战和未来前景。
