The State of Peptide Receptor Radionuclide Therapy and Its Sequencing among Current Therapeutic Options for Gastroenteropancreatic Neuroendocrine Tumors

Gastroenteropancreatic neuroendocrine tumors (GEP-NETs) are the most common form of neuroendocrine neoplasia, but there is no current consensus for the sequencing of approved therapies, particularly with respect to peptide receptor radionuclide therapy (PRRT). This comprehensive review evaluates the...

Full description

Saved in:
Bibliographic Details
Published inNeuroendocrinology Vol. 111; no. 11; p. 1086
Main Authors Raymond, Lauren M, Korzun, Tetiana, Kardosh, Adel, Kolbeck, Kenneth J, Pommier, Rodney, Mittra, Erik S
Format Journal Article
LanguageEnglish
Published Switzerland 01.10.2021
Subjects
Online AccessGet more information

Cover

Loading…
More Information
Summary:Gastroenteropancreatic neuroendocrine tumors (GEP-NETs) are the most common form of neuroendocrine neoplasia, but there is no current consensus for the sequencing of approved therapies, particularly with respect to peptide receptor radionuclide therapy (PRRT). This comprehensive review evaluates the data supporting approved therapies for GEP-NETs and recommendations for therapeutic sequencing with a focus on how PRRT currently fits within sequencing algorithms. The current recommendations for PRRT sequencing restrict its use to metastatic, inoperable, progressive midgut NETs; however, this may change with emerging data to suggest that PRRT might be beneficial as neoadjuvant therapy for inoperable tumors, is more tolerable than other treatment modalities following first-line standard dose somatostatin analogs, and can be used as salvage therapy after disease relapse following prior successful cycles of PRRT. PRRT has also been shown to reduce tumor burden, improve quality of life, and prolong the time to disease progression in a broad spectrum of patients with GEP-NETs. As the various potential benefits of PRRT in GEP-NET therapy continues to expand, it is necessary to review and critically evaluate our treatment algorithms for GEP-NETs.
ISSN:1423-0194
DOI:10.1159/000516015