Anzai Tagayasu, Hirata Kenji, Kato Ken, Kudo Kohsuke
Cardiology Department, Tokyo Metropolitan Tama Medical Center, Fuchu, Japan.
Clinical AI Human Resources Development Program (CLAP), Faculty of Medicine, Hokkaido University, Sapporo, Japan.
Cardiooncology. 2025 May 22;11(1):49. doi: 10.1186/s40959-025-00348-z.
Global longitudinal strain (GLS) is an important prognostic indicator for predicting heart failure and cancer therapy-related cardiac dysfunction (CTRCD). Although access to GLS measurement has increased across institutions, its actual use in clinical practice remains limited due to practical barriers such as limited time and insufficient training. If reduced GLS could be predicted from conventional echocardiographic parameters, it could help identify patients who would most benefit from direct GLS assessment. Therefore, in this study, we tested the hypothesis that reduced GLS can be predicted from conventional echocardiography via a machine learning (ML) approach.
This single-center cross-sectional study included patients who visited the Tokyo Metropolitan Tama Medical Center Hospital and underwent echocardiography with GLS before or after anticancer chemotherapy. Low-GLS was defined as a GLS < 16; otherwise, it was defined as Normal-GLS. Patients with EF < 50% were excluded. We developed ML models that predict Low-GLS from conventional echocardiography measurements. Sixteen ML models were constructed including various boosting and tree-based methods. We assessed the models by the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, Positive predictive value (PPV), Negative predictive value (NPV), and F1 score. The Shapley Additive exPlanations (SHAP) method was employed to evaluate the essential predictors.
A total of 1,484 patients (64 ± 13 years old, 69% female) were enrolled for ML model development, including 406 patients with Low-GLS and 1,078 with Normal-GLS. The best model for the test dataset was the CatBoost classifier (AUC, 0.748; accuracy, 0.734). Diastolic dysfunction indices [such as septal/lateral mitral annular early diastolic velocity (e') and E-wave to atrial contraction filling velocity (E/A)] and peak velocity‑related parameters [aortic valve peak velocity (AV-Vmax) and left ventricular outflow tract velocity maximum (LVOT-Vmax)] played essential roles in the Low-GLS prediction model.
This study indicated the possibility that Low-GLS might be predicted by machine learning models from conventional echocardiography measurements in cancer patients.
整体纵向应变（GLS）是预测心力衰竭和癌症治疗相关心脏功能障碍（CTRCD）的重要预后指标。尽管各机构中能够进行GLS测量的机会有所增加，但由于时间有限和培训不足等实际障碍，其在临床实践中的实际应用仍然有限。如果能够从传统超声心动图参数预测GLS降低，这将有助于识别那些最能从直接GLS评估中获益的患者。因此，在本研究中，我们检验了通过机器学习（ML）方法可从传统超声心动图预测GLS降低的假设。
这项单中心横断面研究纳入了前往东京都立多摩医疗中心医院就诊且在抗癌化疗前后接受了GLS超声心动图检查的患者。低GLS定义为GLS < 16；否则定义为正常GLS。射血分数（EF）< 50%的患者被排除。我们开发了从传统超声心动图测量预测低GLS的ML模型。构建了16个ML模型，包括各种增强和基于树的方法。我们通过受试者操作特征曲线下面积（AUC）、准确性、敏感性、特异性、阳性预测值（PPV）、阴性预测值（NPV）和F1分数对模型进行评估。采用Shapley加性解释（SHAP）方法评估重要预测因素。
总共1484例患者（64 ± 13岁，69%为女性）被纳入ML模型开发，其中包括406例低GLS患者和1078例正常GLS患者。测试数据集的最佳模型是CatBoost分类器（AUC，0.748；准确性，0.734）。舒张功能障碍指标[如室间隔/侧壁二尖瓣环舒张早期速度（e'）和E波与心房收缩充盈速度（E/A）]以及峰值速度相关参数[主动脉瓣峰值速度（AV-Vmax）和左心室流出道最大速度（LVOT-Vmax）]在低GLS预测模型中起重要作用。
本研究表明，机器学习模型有可能从癌症患者的传统超声心动图测量中预测低GLS。
