Related Research and Progress of Radiomics in Multiple Myeloma

多发性骨髓瘤的精准诊疗长期依赖侵入性骨髓活检，但其全身代表性和重复性不足，限制了预后评估的可靠性。放射组学通过从多模态影像(X线、CT、PET/CT、MRI)中提取高通量定量特征，结合手工特征与深度学习算法(如卷积神经网络)，为多发性骨髓瘤的非侵入性全景评估提供了新路径。研究表明，放射组学模型在鉴别MM与脊柱转移瘤、检测微小残留病及预测骨髓浆细胞浸润中显著优于传统影像学。然而，其临床应用受限于可解释性不足、数据标准化缺乏及多中心验证缺失。未来需通过跨学科整合、算法优化及大规模前瞻性研究，推动放射组学从科研向临床转化，最终实现MM个体化分层治疗。The precise diagnosis and treatment of multiple myeloma (MM) have long relied on invasive bone marrow biopsy. However, its insufficient systemic representativeness and repeatability have limited the reliability of prognostic evaluation. Radiomics, by extracting high-throughput quantitative features from multimodal imaging (X-ray, CT, PET/CT, MRI), and combining manual features with deep learning algorithms (such as convolutional neural networks), provides a new pathway for the non-invasive panoramic assessment of multiple myeloma. Studies have shown that radiomics models are significantly superior to traditional imaging techniques in differentiating MM from spinal metastases, detecting minimal residual disease, and predicting bone marrow plasma cell infiltration. Nevertheless, its clinical application is restricted by insufficient interpretability, lack of data standardization, and absence of multicenter validation. In the future, it is necessary to promote the transformation of radiomics from scientific research to clinical practice through interdisciplinary integration, algorithm optimization, and large-scale prospective studies, and ultimately achieve individualized stratified treatment for MM.