Meng Fei, Wu Qin, Zhang Wei, Hou Shirong
Department of Ultrasound, Xuan Cheng City Central Hospital, Xuancheng, Anhui, People's Republic of China.
Diabetes Metab Syndr Obes. 2023 Sep 7;16:2705-2716. doi: 10.2147/DMSO.S422364. eCollection 2023.
Diabetic kidney disease (DKD) patients have a high risk of suffering from cardiovascular disease (CVD), placing a heavy cost on the public health system. In this study, we intended to develop and validate a shear-wave elastography (SWE)-based radiomics nomogram for predicting the development of CVD in DKD patients. This approach allows extensive use of the valuable information contained in ultrasound images, thus helping clinicians to identify CVD in DKD patients.
Totally 337 and 145 patients constituted the training and validation cohorts, respectively. The radiomics features of the segmented kidney in ultrasound images were extracted and selected to generate the rad-score of each patient. These rad-score, as well as the predictors of risk of CVD occurrence from the clinical characteristics, were included in the multivariate analysis to develop a nomogram. It was further assessed in the training and validation cohorts.
Patients with CVD accounted for 30.9% (104/337) in the training cohort and 31.0% (45/145) in the validation cohort. The rad-score was calculated for each patient using 6 features extracted from the ultrasound images. The radiomics nomogram was built with the rad-score, age, systolic blood pressure (SBP), low-density lipoprotein cholesterol (LDL-C). It was superior to the clinical nomogram developed without the rad-score and demonstrated promising discrimination, calibration, and clinical utility in both training and validation cohorts.
We developed and validated an SWE-based radiomics nomogram to predict CVD risk in patients with DKD. The model was demonstrated to have a promising prediction performance, showing its potential to identify CVD in DKD patients and assist decision-making for appropriate early intervention.
糖尿病肾病（DKD）患者患心血管疾病（CVD）的风险很高，给公共卫生系统带来了沉重负担。在本研究中，我们旨在开发并验证一种基于剪切波弹性成像（SWE）的放射组学列线图，用于预测DKD患者CVD的发生。这种方法能够广泛利用超声图像中包含的有价值信息，从而帮助临床医生识别DKD患者中的CVD。
分别有337例和145例患者构成训练队列和验证队列。提取并选择超声图像中分割肾脏的放射组学特征，以生成每位患者的放射学评分（rad-score）。这些放射学评分以及临床特征中CVD发生风险的预测因素被纳入多变量分析以构建列线图。并在训练队列和验证队列中进一步评估。
训练队列中患有CVD的患者占30.9%（104/337），验证队列中占31.0%（45/145）。使用从超声图像中提取的6个特征为每位患者计算放射学评分。基于放射学评分、年龄、收缩压（SBP）、低密度脂蛋白胆固醇（LDL-C）构建了放射组学列线图。它优于未使用放射学评分开发的临床列线图，并且在训练队列和验证队列中均显示出良好的区分度、校准度和临床实用性。
我们开发并验证了一种基于SWE的放射组学列线图，用于预测DKD患者的CVD风险。该模型被证明具有良好的预测性能，显示出其在识别DKD患者中的CVD以及协助做出适当早期干预决策方面的潜力。
