From the Department of Radiology.
Breast Disease Center, the Affiliated Hospital of Qingdao University, Qingdao, Shandong.
J Comput Assist Tomogr. 2022;46(3):447-454. doi: 10.1097/RCT.0000000000001298. Epub 2022 Apr 8.
The aim of this study was to explore the clinical utility of spinal magnetic resonance imaging-based radiomics to predict treatment response (TR) in patients with multiple myeloma (MM).
A total of 123 MM patients (85 in the training cohort and 38 in the test cohort) with complete response (CR) (n = 40) or non-CR (n = 83) were retrospectively enrolled in the study. Key feature selection and data dimension reduction were performed using the least absolute shrinkage and selection operator regression. A nomogram was built by combining radiomic signatures and independent clinical risk factors. The prediction performance of the nomogram was assessed using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis. Treatment response was assessed by determining the serum and urinary levels of M-proteins, serum-free light chain ratio, and the percentage of bone marrow plasma cells.
Thirteen features were selected to build a radiomic signature. The International Staging System (ISS) stage was selected as an independent clinical factor. The radiomic signature and nomogram showed better calibration and higher discriminatory capacity (AUC of 0.929 and 0.917 for the radiomics and nomogram in the training cohort, respectively, and 0.862 and 0.874 for the radiomics and nomogram in the test cohort, respectively) than the clinical model (AUC of 0.661 and 0.674 in the training and test cohort, respectively). Decision curve analysis confirmed the clinical utility of the radiomics model.
Nomograms incorporating a magnetic resonance imaging-based radiomic signature and ISS stage help predict the response to chemotherapy for MM and can be useful in clinical decision-making.
本研究旨在探讨基于脊柱磁共振成像的放射组学对多发性骨髓瘤（MM）患者治疗反应（TR）的临床预测价值。
回顾性纳入 123 例具有完全缓解（CR）（n=40）或非完全缓解（n=83）的 MM 患者（训练队列 85 例，测试队列 38 例）。采用最小绝对值收缩和选择算子回归进行关键特征选择和数据降维。通过结合放射组学特征和独立临床危险因素构建列线图。通过受试者工作特征曲线（ROC）下面积（AUC）、校准曲线和决策曲线分析评估列线图的预测性能。通过测定血清和尿液 M 蛋白、血清游离轻链比值和骨髓浆细胞百分比来评估治疗反应。
筛选出 13 个特征构建放射组学特征，国际分期系统（ISS）分期被选为独立的临床因素。放射组学特征和列线图均显示出更好的校准度和更高的判别能力（在训练队列中，放射组学和列线图的 AUC 分别为 0.929 和 0.917，在测试队列中，放射组学和列线图的 AUC 分别为 0.862 和 0.874），优于临床模型（在训练和测试队列中，AUC 分别为 0.661 和 0.674）。决策曲线分析证实了放射组学模型的临床实用性。
纳入基于磁共振成像的放射组学特征和 ISS 分期的列线图有助于预测 MM 患者对化疗的反应，可在临床决策中发挥作用。
