用统一框架同时完成心功能评估、心肌异常分类和心脏毒性早期预测。
A Unified DINOv2-Based Framework for LVEF Estimation, GLS Dysfunction Classification, and Early Cardiotoxicity Prediction
- 基于DINOv2冻结主干，用低秩适配与时间聚合学任务特异性特征。
- 在1203例患者数据上，射血分数误差仅5.03%，毒性预测准确率超70%。
- 无需心脏周期分割或收缩末期/舒张末期标注，适合临床快速部署。
左室射血分数（LVEF）估计（任务1）、基于全局纵向应变（GLS）的心肌功能障碍分类（任务2）以及早期心脏毒性预测（任务3）为肿瘤心脏病学评估提供互补信息。LVEF反映心室容积的宏观变化，是临床标准；而GLS可捕捉细微心肌形变，提示在LVEF明显下降前的亚临床毒性。从治疗前基线超声预测心脏毒性，有助于早期干预。为此，我们提出基于DINOv2的统一框架，采用任务特化适配与预测头。基于冻结主干编码器，结合参数高效的低秩适配（LoRA）与时间聚合，学习任务专属表示，确保强泛化能力。推理时完全免于周期检测与相位标注，无需心脏周期分割或明确的收缩末期/舒张末期（ED/ES）标注。此外，针对任务1引入基于ED/ES引导的2D/3D混合多视角回归模型。在包含1,203例训练视频（237名患者）与300例验证视频（59名独立患者）的患者级划分数据集上，该框架在任务1上实现5.03%的平均绝对误差（MAE），任务2的AUC-ROC为76.48%，任务3为70.26%。任务1中，专用的ED/ES引导模型进一步提升性能，达4.64%的MAE。结果表明，基础模型表征在多样化心血管肿瘤学任务中有效，且生理引导建模显著提升LVEF估计精度。
原文摘要 · Abstract (English)
Left ventricular ejection fraction (LVEF) estimation (Task 1), global longitu-dinal strain (GLS)-based dysfunction classification (Task 2), and early cardi-otoxicity prediction (Task 3) provide complementary information for cardio-oncology assessment. LVEF reflects macroscopic ventricular volume chang-es as the clinical standard, whereas GLS captures subtle myocardial defor-mation, indicating subclinical cardiotoxicity before overt LVEF decline. Fur-thermore, predicting cardiotoxicity from baseline echocardiography prior to treatment enables preventive interventions at an early stage. To address these three tasks, we employ a DINOv2-based framework with task-specific adap-tation and prediction heads. Built upon a frozen foundation encoder, the framework incorporates parameter-efficient Low-Rank Adaptation (LoRA) and temporal aggregation to learn task-specialized representations, ensuring robust generalization. Crucially, during inference, it operates in a fully cycle-detection-free and phase-free manner, requiring neither cardiac cycle seg-mentation nor explicit End-Diastolic/End-Systolic (ED/ES) annotations. Ad-ditionally, we introduce an ED/ES-guided 2D/3D hybrid multi-view regres-sion model specifically to optimize Task 1. On a patient-level split containing 1,203 training videos from 237 patients and 300 validation videos from 59 independent patients, the DINOv2-based framework achieved a mean abso-lute error (MAE) of 5.03% for Task 1, an AUC-ROC of 76.48% for Task 2, and an AUC-ROC of 70.26% for Task 3. For Task 1, the specialized ED/ES-guided model further improves performance, achieving an MAE of 4.64%. This framework demonstrates the effectiveness of foundation model repre-sentations across diverse cardio-oncology tasks and the additional benefit of physiology-guided modeling for accurate LVEF estimation.
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