目的

建立一个基于股直肌超声图像的影像组学模型，探索其对老年人肌少症的诊断价值。

方法

纳入2021 年7 月至2024 年4 月于北京协和医院老年医学科就诊的老年人。经过详细的分组流程后，共纳入100 名肌少症参与者与84 名正常参与者。临床评估方面，获得所有参与者的身高、体质量、体质量指数（BMI）等指标，并采用生物电阻抗分析测量所有参与者的躯干肌肉质量和肢体肌肉质量，据此计算四肢骨骼肌质量指数（ASMI）。超声测量方面，在B 模式下测量股直肌中点处的肌肉厚度（MT）、横截面积（CSA）以及回声强度（MEI）。采用Spearman 相关性检验分析超声测量指标与临床指标的相关性。影像组学方面，勾画超声图像中成像区域内的股直肌作为感兴趣区域（ROI），进行ROI 影像组学特征提取及分析，建立影像组学模型，模型训练和测试过程中原始数据按4 ∶1 比例随机分配至训练集和测试集。使用受试者操作特征（ROC）曲线下面积（AUC）值分别评估超声指标和影像组学模型诊断肌少症的准确性，通过多因素Logistic 回归分析建立联合诊断模型，并经ROC 曲线分析联合诊断模型的诊断效能。

结果

2 组的各项超声测量指标差异均存在统计学意义（P 均＜0.05）。MT 和CSA 与ASMI（r=0.587，P＜0.001；r=0.640，P＜0.001）等指标呈正相关，MEI 与ASMI（r=-0.358，P＜0.001）等指标呈负相关。CSA 对肌少症的诊断效能（总人群中AUC 值为0.795，男性中AUC 值为0.804，女性中AUC 值为0.800）优于MT 及MEI。本研究所建立的影像组学模型对肌少症诊断的AUC 值在训练集和测试集中分别为0.787 和0.781。通过多因素Logistic 回归分析建立联合诊断模型，纳入因素包括年龄、BMI 及影像组学模型评价结果，该联合模型对肌少症诊断具有高度准确性（总人群中AUC 值为0.903，男性中AUC 值为0.946，女性中AUC 值为0.909）。

结论

通过超声测量的股直肌MT 和CSA 以及超声影像组学模型对肌少症分别具有良好的独立诊断价值。联合诊断模型对肌少症诊断准确性高，可为肌少症诊断提供新的有效评估手段。

Objective

To establish a radiomics model based on ultrasound image analysis of the rectus femoris muscle, and to assess its diagnostic value for sarcopenia in elderly individuals.

Methods

Elderly participants were recruited from the Department of Gerontology, Peking Union Medical College Hospital from July 2021 to April 2024.After a detailed grouping process based on the diagnostic criteria of the Asian Working Group for Sarcopenia 2019 (AWGS 2019), a total of 100 participants with sarcopenia and 84 normal participants were included.In terms of clinical assessment, height, weight, and body mass index (BMI) were measured for all participants.Bioelectrical impedance analysis was also conducted to measure trunk muscle mass and limb muscle mass, based on which appendicular skeletal muscle mass index (ASMI) was calculated.For ultrasound measurements, muscle thickness (MT), cross-sectional area (CSA), and muscle echogenicity intensity (MEI) at the midpoint of the rectus femoris muscle were measured in B-mode.The Spearman correlation test was used to analyze the correlation between ultrasound measurement indicators and clinical indicators.In terms of radiomics, the region of interest (ROI) delineated included the rectus femoris muscle within the imaging area.Radiomic feature extraction and analysis were performed for the ROI to establish a radiomic model.During the model training and testing process, the original data were randomly allocated to a training set and a test set in a 4:1 ratio.The accuracy of ultrasound indicators and the radiomic model in predicting sarcopenia was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC) value, and a combined diagnostic model was established through multivariate logistic regression analysis, and the accuracy of the combined diagnostic model was evaluated by ROC curve.

Results

Statistically significant differences were observed in various ultrasoundmeasured indicators between the two groups (P＜0.05 for all).MT (r=0.587, P＜0.001) and CSA (r=0.640,P ＜0.001) showed a positive correlation with ASMI, while MEI demonstrated a negative correlation with ASMI (r=-0.358, P＜0.001).The diagnostic performance of CSA (AUC=0.795 for all participants,AUC=0.804 for males, and AUC=0.800 for females) was superior to that of MT and MEI.The AUC values of the radiomics model established in this study for the diagnosis of sarcopenia were 0.787 and 0.781 in the training set and test set, respectively.A combined diagnostic model was developed through multivariate logistic regression analysis, incorporating factors such as age, BMI, and evaluation results of the radiomics model.This combined model exhibited high accuracy in diagnosing sarcopenia, with AUC values of 0.903 for all participants, 0.946 for males, and 0.909 for females.

Conclusion

MT and CSA of the rectus femoris measured by ultrasound, as well as the ultrasound-based radiomics model, each possess good independent diagnostic value for sarcopenia.The combined diagnostic model exhibits high accuracy in diagnosing sarcopenia, offering a new and effective assessment method for sarcopenia diagnosis.