摘要：基于深度学习的全身 PET-CT 病灶分割可支持癌症分期、治疗规划和疗效评估，但其泛化能力受到标注稀缺和领域分布变化的限制。自监督学习（SSL）能够应对这些挑战，但在泛癌种、多示踪剂 PET-CT 场景中仍缺乏充分探索。在本研究中，我们提出 MUST-PET（MUltimodal Self-Supervised learning across Tracers，跨示踪剂多模态自监督学习），这是一个用于可泛化全身 PET-CT 病灶分割的多模态、多示踪剂 SSL 框架。MUST-PET 在由多个机构采集的、多样化泛癌种 PET-CT 扫描集合上进行训练和验证，所用扫描采用 FDG 及靶向前列腺特异性膜抗原（PSMA）的放射性示踪剂。MUST-PET 采用上下文感知的掩码重建方法：部分遮蔽一个模态，并利用 PET 和 CT 的互补信息对其进行重建。随后，使用带标签样本对预训练模型进行微调，并从重建质量、病灶分割、标签效率以及在独立留出数据集上的泛化能力等方面进行评估。MUST-PET 可降低重建误差，相比从头训练提升病灶分割表现，并能在标注数据有限及未见外部数据集的情况下取得良好效果，证明了多示踪剂 SSL 在实现高标签效率、可泛化全身 PET-CT 病灶分割方面的潜力。

Abstract:Deep learning-based whole-body PET-CT lesion segmentation can support cancer staging, treatment planning, and response assessment, but generalization is limited by scarce annotations and domain shifts. Self-supervised learning (SSL) can address these challenges but remains underexplored in pan-cancer, multi-tracer PET-CT. In this work, we propose MUST-PET (MUltimodal Self-Supervised learning across Tracers), a multimodal, multi-tracer SSL framework for generalizable whole-body PET-CT lesion segmentation. MUST-PET is trained and validated on a diverse, multi-institutional collection of pan-cancer PET-CT scans acquired with FDG and prostate-specific membrane antigen (PSMA)-targeted radiotracers. MUST-PET uses context-aware masked reconstruction, where one modality is partially masked and reconstructed using complementary information from both PET and CT. The pretrained model is subsequently fine-tuned with labeled samples and evaluated for reconstruction quality, lesion segmentation, label efficiency, and generalizability across independent held-out datasets. MUST-PET reduces reconstruction error, improves lesion segmentation over training from scratch, and performs well with limited labeled data and on unseen external datasets, demonstrating the potential of multi-tracer SSL for label-efficient, generalizable whole-body PET-CT. segmentation.