Multimodal Deep Learning for Prediction of Progression-Free Survival in Patients with Neuroendocrine Tumors Undergoing <sup>177</sup>Lu-Based Peptide Receptor Radionuclide Therapy.
S. Baur, Tristan Ruhwedel, Ekin Böke, Zuzanna Kobus, Gergana Lishkova, Christoph Wetz, H Amthauer, Christoph Roderburg, Frank Tacke, Julian M Rogasch, Wojciech Samek, Henning Jann, Jackie Ma, Johannes Eschrich
原始摘要（英文原文）· Original abstract
: 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.
Multimodal Deep Learning for Prediction of Progression-Free Survival in Patients with Neuroendocrine Tumors Undergoing <sup>177</sup>Lu-Based Peptide Receptor Radionuclide Therapy. — 科研速览 Science Skim