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

Titel:	Multimodal Deep Learning for Prediction of Progression-Free Survival in Patients with Neuroendocrine Tumors Undergoing 177Lu-Based Peptide Receptor Radionuclide Therapy
URL für Lesezeichen:	https://docserv.uni-duesseldorf.de/servlets/DocumentServlet?id=73374
URN (NBN):	urn:nbn:de:hbz:061-20260526-120745-3
Kollektion:	Publikationen
Sprache:	Englisch
Dokumententyp:	Wissenschaftliche Texte » Artikel, Aufsatz
Medientyp:	Text
Autoren:	Roderburg, Christoph [Autor] Baur, Simon [Autor] Ruhwedel, Tristan [Autor] Böke, Ekin [Autor] Kobus, Zuzanna [Autor] Lishkova, Gergana [Autor] Wetz, Christoph [Autor] Amthauer, Holger [Autor] Tacke, Frank [Autor] Rogasch, Julian M. [Autor]
Dateien:	[Dateien anzeigen] Adobe PDF [Details] 1,97 MB in einer Datei [ZIP-Datei erzeugen] Dateien vom 26.05.2026 / geändert 26.05.2026
Stichwörter:	somatostatin receptor PET/CT , progression-free survival , [177Lu]Lu- DOTATOC , treatment outcome , neuroendocrine tumors , multimodal deep learning , peptide receptor radionuclide therapy , radiomics , predictive modeling
Beschreibung:	Background/Objectives: 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. Methods: In this retrospective, single-center study 116 patients with metastatic NETs undergoing [177Lu]Lu-DOTATOC were included. Clinical characteristics, laboratory values, and pretherapeutic somatostatin receptor positron emission tomography/computed tomographies (SR-PET/CTs) were collected. Seven models were trained to classify low- vs. high-PFS groups, including unimodal (laboratory, SR-PET, or CT) and multimodal fusion approaches. Performance was assessed via repeated 3-fold cross-validation with area under the receiver operating characteristic curve (AUROC) and area under the precision–recall curve (AUPRC). Explainability was evaluated by feature importance analysis and gradient based saliency maps. Results: Forty-two patients (36%) displayed short PFS (≤1 year) and 74 patients displayed long PFS (>1 year). Groups were similar in most characteristics, except for higher baseline chromogranin A (p = 0.003), elevated 𝛾-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 integrating 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). Explainability analyses provided insights into model predictions, with explainability patterns in the fusion model appearing physiologically plausible and predominantly tumor-focused. Conclusions: 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.
Rechtliche Vermerke:	Originalveröffentlichung: Baur, S., Ruhwedel, T., Böke, E., Kobus, Z., Lishkova, G., Wetz, C., Amthauer, H., Roderburg, C., Tacke, F., Rogasch, J. M., Samek, W., Jann, H., Ma, J., & Eschrich, J. (2026). Multimodal Deep Learning for Prediction of Progression-Free Survival in Patients with Neuroendocrine Tumors Undergoing 177Lu-Based Peptide Receptor Radionuclide Therapy. Cancers, 18(8), Article 1194. https://doi.org/10.3390/cancers18081194
Lizenz:	Dieses Werk ist lizenziert unter einer Creative Commons Namensnennung 4.0 International Lizenz
Fachbereich / Einrichtung:	Medizinische Fakultät
Dokument erstellt am:	26.05.2026
Dateien geändert am:	26.05.2026