Abstract：
OBJECTIVE: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
摘要：
目的：我们探讨了使用总肿瘤表观扩散系数（ttADC）直方图参数预测多发性骨髓瘤（MM）患者高危细胞遗传学异常（HRCA）的可行性，并比较了基于这些参数的图像预测模型与基于这些参数和临床指标的组合预测模型的性能。
方法：我们回顾性分析了92例MM患者基于全身扩散加权图像（WB-DWI）和临床指标的ttADC直方图的参数。根据荧光原位杂交结果将患者分为HRCA组和非HRCA组。采用Logistic回归分析构建图像预测和组合预测模型。使用受试者工作特征(ROC)曲线的曲线下面积(AUC)来评估模型的性能以识别HRCA。采用DeLong检验比较各预测模型的AUC差异。
结果：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ttADCSD组成。组合预测模型包括ttADC熵以及患者临床指标，如生物学性别和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<0.05）。图像预测模型显示灵敏度为73.9％，特异性为68.1％。组合预测模型的敏感性为82.6%,特异性为72.5%。
结论：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。
方法：我们回顾性分析了92例MM患者基于全身扩散加权图像（WB-DWI）和临床指标的ttADC直方图的参数。根据荧光原位杂交结果将患者分为HRCA组和非HRCA组。采用Logistic回归分析构建图像预测和组合预测模型。使用受试者工作特征(ROC)曲线的曲线下面积(AUC)来评估模型的性能以识别HRCA。采用DeLong检验比较各预测模型的AUC差异。
结果：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ttADCSD组成。组合预测模型包括ttADC熵以及患者临床指标，如生物学性别和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<0.05）。图像预测模型显示灵敏度为73.9％，特异性为68.1％。组合预测模型的敏感性为82.6%,特异性为72.5%。
结论：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。
