我们探讨了使用总肿瘤表观扩散系数 (ttADC) 直方图参数来预测多发性骨髓瘤 (MM) 患者高危细胞遗传学异常 (HRCA) 的可行性，并将基于这些参数的图像预测模型的性能与基于这些参数的图像预测模型的性能进行了比较。基于这些参数和临床指标的组合预测模型。我们回顾性分析了92例MM患者基于全身弥散加权图像（WB-DWI）的ttADC直方图参数和临床指标。根据荧光原位杂交结果将患者分为HRCA组和非HRCA组。使用逻辑回归分析来构建图像预测和组合预测模型。受试者工作特征 (ROC) 曲线的曲线下面积 (AUC) 用于评估模型识别 HRCA 的性能。采用DeLong检验比较各预测模型的AUC差异。Logistic回归分析结果显示，ttADC直方图参数，ttADC熵<7.959（OR：39.167；95％置信区间[CI]：3.891-394.208；P<0.05 ），是 HRCA 的独立危险因素。图像预测模型由ttADC熵和ttADC SD组成。组合预测模型包括 ttADC 熵以及患者临床指标，例如生物性别和 M 蛋白百分比。图像预测和组合预测模型的 AUC 分别为 0.739 和 0.811 (P < .05)。图像预测模型的敏感性为 73.9%，特异性为 68.1%。组合预测模型显示出82.6%的敏感性和72.5%的特异性。使用基于WB-DWI图像的ttADC直方图参数来预测MM患者的HRCA是可行的，并且将ttADC参数与临床指标相结合可以取得更好的预测性能。Copyright © 2024大学放射科医生协会。由爱思唯尔公司出版。保留所有权利。

We explored the feasibility of using total tumor apparent diffusion coefficient (ttADC) histogram parameters to predict high-risk cytogenetic abnormalities (HRCA) in patients with multiple myeloma (MM) and compared the performance of an image prediction model based on these parameters with that of a combined prediction model based on these parameters and clinical indicators.We retrospectively analyzed the parameters of the ttADC histogram based on whole-body diffusion-weighted images(WB-DWI) and clinical indicators in 92 patients with MM. The patients were divided into HRCA and non-HRCA groups according to the results of the fluorescence in situ hybridization. Logistic regression analysis was used to construct the image prediction and combined prediction models. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used to evaluate the performance of the models to identify HRCA. The DeLong test was used to compare the AUC differences of each prediction model.Logistic regression analysis results revealed that the ttADC histogram parameter, ttADC entropy < 7.959 (OR: 39.167; 95% confidence interval [CI]: 3.891-394.208; P < 0.05), was an independent risk factor for HRCA. The image prediction model consisted of ttADC entropy and ttADC SD. The combined prediction model included ttADC entropy along with patient clinical indicators such as biological sex and M protein percentage. The AUCs of the image prediction and combined prediction models were 0.739 and 0.811, respectively (P < .05). The image prediction model showed a sensitivity of 73.9% and a specificity of 68.1%. The combined prediction model showed 82.6% sensitivity and 72.5% specificity.Using ttADC histogram parameters based on WB-DWI images to predict HRCA in patients with MM is feasible, and combining ttADC parameters with clinical indicators can achieve better predictive performance.Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.