论文标题
CT数据的放射学和人工智能分析，用于鉴定多发性骨髓瘤的预后特征
Radiomics and artificial intelligence analysis of CT data for the identification of prognostic features in multiple myeloma
论文作者
论文摘要
多发性骨髓瘤（MM）是一种血液癌，暗示骨髓受累，肾脏损伤和骨化病变。 MM的骨骼参与是本文的核心，利用了放射线学和人工智能来识别基于图像的生物标志物的MM。初步结果表明，MM与整个身体的内部鉴定体积扩展有关，并且机器学习可以识别CT图像特征主要与疾病进化相关。这种计算方法允许依赖于这些生物标志物的MM患者自动分层，并制定了确定疾病随访的预后程序。
Multiple Myeloma (MM) is a blood cancer implying bone marrow involvement, renal damages and osteolytic lesions. The skeleton involvement of MM is at the core of the present paper, exploiting radiomics and artificial intelligence to identify image-based biomarkers for MM. Preliminary results show that MM is associated to an extension of the intrabone volume for the whole body and that machine learning can identify CT image features mostly correlating with the disease evolution. This computational approach allows an automatic stratification of MM patients relying of these biomarkers and the formulation of a prognostic procedure for determining the disease follow-up.
