在乳腺癌幸存者中使用基于kinect的混合现实练习来分类癌症相关肌肉减少症的机器学习模型的开发和验证。

IF 1.7 4区 医学 Q4 ONCOLOGY
Translational cancer research Pub Date : 2025-07-30 Epub Date: 2025-07-22 DOI:10.21037/tcr-2024-2337
Byunggul Lim, Wook Song
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引用次数: 0

摘要

背景:由于基于成像的诊断工具,如计算机断层扫描(CT)或双能x线吸收仪(DXA)的使用有限,癌症幸存者的肌肉减少症经常被误诊。使用运动数据的间接分类可能提供一种实用的、可扩展的替代方法。本研究旨在利用基于kinect的混合现实(KMR)设备获得的关节角度数据,开发和验证基于机器学习(ML)的癌症相关肌肉减少症分类模型,旨在提高分类精度并识别关键的运动相关预测因子。方法:根据I-III期诊断、治疗完成≥6个月、无转移、低体力活动、无主要合并症,共纳入77例乳腺癌幸存者(平均年龄48.9±5.4岁)。通过骨骼肌指数(SMI)(2)和握力(HGS)诊断肌肉减少症(结果:在最终分析的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8周的KMR器械运动干预后,3名参与者从肌肉减少症中恢复,9名(23.6%)仍被归类为该疾病。在测试集中,XGB模型表现出最高的性能,准确率为94.7%,召回率为91.2%,准确率为95.8%,F1得分为93.4%,曲线下面积(AUC)为96.2%。使用RF和XGB的特征重要性分析一致认为右“膝关节屈曲(右)”是最具影响力的预测因子。结论:在KMR设备联合数据训练的ML分类模型中,XGB表现出最好的性能。右膝屈曲是骨骼肌减少症分类中最重要的特征。这些发现表明,KMR装置运动分析可以作为一种实用的、非侵入性的肌肉减少症筛查工具,为临床和远程环境下的乳腺癌幸存者提供早期发现和个性化干预策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Development and validation of machine learning models for classifying cancer-related sarcopenia using Kinect-based mixed-reality exercises in breast cancer survivors.

Background: 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.

Methods: 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/m2) 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.

Results: 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.

Conclusions: 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.

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来源期刊
CiteScore
2.10
自引率
0.00%
发文量
252
期刊介绍: Translational Cancer Research (Transl Cancer Res TCR; Print ISSN: 2218-676X; Online ISSN 2219-6803; http://tcr.amegroups.com/) is an Open Access, peer-reviewed journal, indexed in Science Citation Index Expanded (SCIE). TCR publishes laboratory studies of novel therapeutic interventions as well as clinical trials which evaluate new treatment paradigms for cancer; results of novel research investigations which bridge the laboratory and clinical settings including risk assessment, cellular and molecular characterization, prevention, detection, diagnosis and treatment of human cancers with the overall goal of improving the clinical care of cancer patients. The focus of TCR is original, peer-reviewed, science-based research that successfully advances clinical medicine toward the goal of improving patients'' quality of life. The editors and an international advisory group of scientists and clinician-scientists as well as other experts will hold TCR articles to the high-quality standards. We accept Original Articles as well as Review Articles, Editorials and Brief Articles.
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