Multimodal Deep Learning for Prediction of Progression-Free Survival in Patients with Neuroendocrine Tumors Undergoing 177Lu-based Peptide Receptor Radionuclide Therapy

Simon Baur; Tristan Ruhwedel; Ekin Böke; Zuzanna Kobus; Gergana Lishkova; Christoph Wetz; Holger Amthauer; Christoph Roderburg; Frank Tacke; Julian M. Rogasch; Wojciech Samek; Henning Jann; Jackie Ma; Johannes Eschrich

arXiv · 2511.05169

Multimodal Deep Learning for Prediction of Progression-Free Survival in Patients with Neuroendocrine Tumors Undergoing 177Lu-based Peptide Receptor Radionuclide Therapy

Simon Baur·Tristan Ruhwedel·Ekin Böke·Zuzanna Kobus·Gergana Lishkova·Christoph Wetz·Holger Amthauer·Christoph Roderburg·Frank Tacke·Julian M. Rogasch·Wojciech Samek·Henning Jann·Jackie Ma·Johannes Eschrich

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Abstract

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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Simon Baur, Tristan Ruhwedel, Ekin Böke, Zuzanna Kobus, Gergana Lishkova, Christoph Wetz, Holger Amthauer, Christoph Roderburg, Frank Tacke, Julian M. Rogasch, Wojciech Samek, Henning Jann, Jackie Ma, Johannes Eschrich. 2025-11-07. Multimodal Deep Learning for Prediction of Progression-Free Survival in Patients with Neuroendocrine Tumors Undergoing 177Lu-based Peptide Receptor Radionuclide Therapy. https://arxiv.org/abs/2511.05169

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