Lim Byunggul, Song Wook
Health and Exercise Science Laboratory, Department of Physical Education, Seoul National University, Seoul, Republic of Korea.
Institute on Aging, Seoul National University, Seoul, Republic of Korea.
Transl Cancer Res. 2025 Jul 30;14(7):4208-4218. doi: 10.21037/tcr-2024-2337. Epub 2025 Jul 22.
Sarcopenia in cancer survivors is often underdiagnosed due to limited access to imaging-based diagnostic tools such as computed tomography (CT) or dual-energy X-ray absorptiometry (DXA). Indirect classification using movement data may offer a practical, scalable alternative. This study aimed to develop and validate machine learning (ML)-based classification models for cancer-related sarcopenia using joint angle data obtained from Kinect-based mixed-reality (KMR) devices, aiming to improve classification accuracy and identify key movement-related predictors.
Overall, 77 breast cancer survivors (mean age, 48.9±5.4 years) were included based on stage I-III diagnosis, treatment completion ≥6 months prior, no metastasis, low physical activity, and no major comorbidities. Sarcopenia was diagnosed using skeletal muscle index (SMI) (<5.7 kg/m) and handgrip strength (HGS) (<18 kg). KMR device data were collected during 8 weeks of exercise. After preprocessing, the dataset was randomly split (8:2) for training and testing. Four ML models-support vector machine (SVM), K-nearest neighbor (KNN), random forest (RF), and XGBoost (XGB)-were trained. Five-fold cross-validation was used for tuning, and feature importance was analyzed.
Of the 38 participants in the exercise group included in the final analysis, 12 (31.5%) were initially diagnosed with sarcopenia. After the 8-week KMR device exercise intervention, 3 participants showed recovery from sarcopenia, resulting in 9 (23.6%) remaining classified with the condition. In the test set, the XGB model demonstrated the highest performance, achieving 94.7% accuracy, 91.2% recall, 95.8% precision, 93.4% F1 score, and 96.2% area under the curve (AUC). Feature importance analysis using RF and XGB consistently identified right "knee flexion (right)" as the most influential predictor.
Among ML classification models trained on KMR device joint data, XGB demonstrated the best performance. Right knee flexion emerged as the most influential feature in sarcopenia classification. These findings suggest that KMR device movement analysis may serve as a practical, non-invasive screening tool for sarcopenia, enabling early detection and personalized intervention strategies for breast cancer survivors in both clinical and remote settings.
由于难以使用计算机断层扫描（CT）或双能X线吸收法（DXA）等基于成像的诊断工具，癌症幸存者中的肌肉减少症常常未得到充分诊断。使用运动数据进行间接分类可能提供一种实用、可扩展的替代方法。本研究旨在开发并验证基于机器学习（ML）的癌症相关肌肉减少症分类模型，该模型使用从基于Kinect的混合现实（KMR）设备获得的关节角度数据，旨在提高分类准确性并识别与运动相关的关键预测因素。
总体而言，纳入了77名乳腺癌幸存者（平均年龄48.9±5.4岁），纳入标准为I-III期诊断、至少在6个月前完成治疗、无转移、身体活动水平低且无重大合并症。使用骨骼肌指数（SMI）（<5.7 kg/m）和握力（HGS）（<18 kg）诊断肌肉减少症。在为期8周的运动期间收集KMR设备数据。预处理后，将数据集随机分为训练集和测试集（8:2）。训练了四个ML模型——支持向量机（SVM）、K近邻（KNN）、随机森林（RF）和XGBoost（XGB）。使用五折交叉验证进行调优，并分析特征重要性。
在最终分析纳入的38名运动组参与者中，12名（31.5%）最初被诊断为肌肉减少症。经过为期8周的KMR设备运动干预后，3名参与者的肌肉减少症得到恢复，最终仍有9名（23.6%）被归类为患有该疾病。在测试集中，XGB模型表现最佳，准确率达到94.7%，召回率为91.2%，精确率为95.8%，F1分数为93.4%，曲线下面积（AUC）为96.2%。使用RF和XGB进行的特征重要性分析一致确定右“膝关节屈曲（右侧）”是最具影响力的预测因素。
在基于KMR设备关节数据训练的ML分类模型中，XGB表现最佳。右膝关节屈曲是肌肉减少症分类中最具影响力的特征。这些发现表明，KMR设备运动分析可作为一种实用的、非侵入性的肌肉减少症筛查工具，能够在临床和远程环境中对乳腺癌幸存者进行早期检测并制定个性化干预策略。
