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.

翻译：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，但仅部分患者能实现长期疾病控制。预测无进展生存期（PFS）有助于个体化治疗规划。本研究评估了实验室指标、影像学及多模态深度学习模型在PRRT治疗患者PFS预测中的应用。这项回顾性单中心研究纳入了116例接受¹⁷⁷Lu-DOTATOC治疗的转移性NETs患者，收集了临床特征、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）数据。训练了七种模型用于区分低PFS与高PFS组，包括单模态（实验室指标、SR-PET或CT）及多模态融合方法。通过特征重要性分析和梯度图评估模型可解释性。42例患者（36%）为短PFS组（<1年），74例为长PFS组（>1年）。两组在多数特征上相似，但短PFS组基线嗜铬粒蛋白A水平更高（p=0.003）、γ-谷氨酰转移酶升高（p=0.002）且PRRT治疗周期更少（p<0.001）。仅基于实验室生物标志物训练的随机森林模型AUROC为0.59±0.02。使用SR-PET或CT的单模态三维卷积神经网络表现较差（AUROC分别为0.42±0.03和0.54±0.01）。融合实验室指标、SR-PET与CT（通过预训练CT分支增强）的多模态融合模型取得最佳结果（AUROC 0.72±0.01，AUPRC 0.80±0.01）。结合SR-PET、CT和实验室生物标志物的多模态深度学习在PRRT后PFS预测中优于单模态方法。经外部验证后，此类模型有望支持风险适应性随访策略。