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Advancing Therapeutic Strategies for Neuroendocrine Tumors: Towards Personalized and Multidisciplinary Care

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Therapy".

Deadline for manuscript submissions: 31 December 2026 | Viewed by 923

Special Issue Editor


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Guest Editor
Department of Medicine, Division of Hematology/Oncology, Vanderbilt University Medical Center, Nashville, TN 37232, USA
Interests: neuroendocrine tumors; PRRT; DIPNECH; pulmonary carcinoid tumors; multidisciplinary care

Special Issue Information

Dear Colleagues,

As the incidence of neuroendocrine neoplasms (NENs) continues to rise, so does the need for better treatment options and a multidisciplinary approach to patient care. NENs are very diverse in terms of aggressiveness, response to treatment, sites of origin, symptoms, and patient life expectancy. For instance, a patient with a low-grade NEN and a small volume of disease with minimal symptoms may be a candidate for curative intent treatment and have a normal life expectancy. On the other hand, a patient with a high-grade neuroendocrine carcinoma may be very symptomatic and have a limited chance of long-term survival.

A multidisciplinary team is essential to address the intricacies of pathologic variations that affect diagnosis and treatments, including surgery, liver-directed therapy, radionuclide therapy, targeted therapies, and chemotherapies. In addition, symptom management and quality of life are paramount.

This Special Issue will focus on the latest updates in the diagnosis, treatment, and supportive care of patients with NENs, with an emphasis on personalized and multidisciplinary care.

Dr. Robert A. Ramirez
Guest Editor

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Keywords

  • neuroendocrine tumor
  • neuroendocrine carcinoma
  • multidisciplinary care
  • PRRT
  • hepatic embolization
  • tumor debulking
  • targeted therapy
  • lung carcinoid

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Published Papers (1 paper)

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Research

19 pages, 1991 KB  
Article
Multimodal Deep Learning for Prediction of Progression-Free Survival in Patients with Neuroendocrine Tumors Undergoing 177Lu-Based Peptide Receptor Radionuclide Therapy
by 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 and Johannes Eschrich
Cancers 2026, 18(8), 1194; https://doi.org/10.3390/cancers18081194 - 8 Apr 2026
Viewed by 658
Abstract
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 [...] Read more.
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. Full article
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