融合影像与生化指标，提升神经内分泌瘤放疗后生存预测精度
Multimodal Deep Learning for Prediction of Progression-Free Survival in Patients with Neuroendocrine Tumors Undergoing 177Lu-based Peptide Receptor Radionuclide Therapy
- 构建多模态深度学习模型，整合影像、血液指标与CT数据
- 多模态模型预测准确率（AUROC）达0.72，显著优于单一模态
- 适用于个性化治疗决策支持，尤其适合放疗后风险分层
肽受体放射性核素治疗（PRRT）是转移性神经内分泌肿瘤（NETs）的成熟疗法，但仅部分患者获得长期疾病控制。预测无进展生存期（PFS）有助于个体化治疗规划。本研究回顾性分析116例接受177Lu-DOTATOC治疗的转移性NET患者，收集临床特征、实验室指标及治疗前生长抑素受体正电子发射断层扫描/计算机断层扫描（SR-PET/CT）。训练七种模型以区分低-高PFS组，包括单模态（实验室、SR-PET、CT）和多模态融合方法。42名患者（36%）短期PFS（<1年），74名长期PFS（>1年）。短PFS组基线嗜铬粒蛋白A更高（p=0.003）、γ-GT升高（p=0.002）、PRRT疗程更少（p<0.001）。仅使用实验室指标的随机森林模型表现最差（AUROC 0.59±0.02）；仅用SR-PET或CT的三维卷积神经网络表现更差（AUROC 0.42±0.03 和 0.54±0.01）。融合实验室、SR-PET与CT的多模态模型（含预训练CT分支）表现最佳（AUROC 0.72±0.01，AUPRC 0.80±0.01）。多模态深度学习优于单模态方法，未来外验证后可用于风险适应性随访策略。
原文摘要 · Abstract (English)
Peptide receptor radionuclide therapy (PRRT) is an established treatment for metastatic neuroendocrine tumors (NETs), yet long-term disease control occurs only in a subset of patients. Predicting progression-free survival (PFS) could support individualized treatment planning. This study evaluates laboratory, imaging, and multimodal deep learning models for PFS prediction in PRRT-treated patients. In this retrospective, single-center study 116 patients with metastatic NETs undergoing 177Lu-DOTATOC were included. Clinical characteristics, laboratory values, and pretherapeutic somatostatin receptor positron emission tomography/computed tomographies (SR-PET/CT) were collected. Seven models were trained to classify low- vs. high-PFS groups, including unimodal (laboratory, SR-PET, or CT) and multimodal fusion approaches. Explainability was evaluated by feature importance analysis and gradient maps. Forty-two patients (36%) had short PFS (< 1 year), 74 patients long PFS (>1 year). Groups were similar in most characteristics, except for higher baseline chromogranin A (p = 0.003), elevated gamma-GT (p = 0.002), and fewer PRRT cycles (p < 0.001) in short-PFS patients. The Random Forest model trained only on laboratory biomarkers reached an AUROC of 0.59 +- 0.02. Unimodal three-dimensional convolutional neural networks using SR-PET or CT performed worse (AUROC 0.42 +- 0.03 and 0.54 +- 0.01, respectively). A multimodal fusion model laboratory values, SR-PET, and CT -augmented with a pretrained CT branch - achieved the best results (AUROC 0.72 +- 0.01, AUPRC 0.80 +- 0.01). Multimodal deep learning combining SR-PET, CT, and laboratory biomarkers outperformed unimodal approaches for PFS prediction after PRRT. Upon external validation, such models may support risk-adapted follow-up strategies.
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