Affiliation:
1. AC Camargo Cancer Center, São Paulo, Brazil
2. Clinical Oncology Department, AC Camargo Cancer Center, R. Prof. Antonio Prudente 211, Liberdade, São Paulo 01509010, Brazil
Abstract
Background: Gastroenteropancreatic (GEP) neuroendocrine neoplasms with Ki-67 > 20% were subdivided in the most recent 2019 World Health Organization histopathological classification into grade 3 (G3) neuroendocrine tumors (NETs), described as well-differentiated tumors, and neuroendocrine carcinomas, which are described as poorly differentiated tumors. This classification met the demand noted for different prognoses between these subgroups, prompting the need for treatment recommendations for well-differentiated G3 tumors. Methods: We systematically searched medical literature databases and oncology conferences for studies on G3 GEP NET to describe epidemiology, diagnosis, molecular features, and treatments used. We excluded studies that did not discriminate G3 NET data. Data were tabulated and described, and a quality analysis of the reports was performed. Results: We found 23 published studies and six abstracts; 89.7% of studies were retrospective, six were composed exclusively of G3 NETs. Among 761 patients, the median number of patients per study was 15, most were male and older than 60 years, and functional imaging tests were positive in more than 80% of cases. Overall, the scientific evidence supporting the treatment of G3 GEP NETs is limited. For localized disease, resection remains the standard treatment but there is no evidence to support neoadjuvant or adjuvant therapy. For advanced disease, capecitabine and temozolomide seems to be the most effective option, with a response rate, median progression-free survival, and median overall survival up to 37.9%, 20.6 months, and 41.2 months, respectively. Conclusion: The latest available data on the epidemiology, diagnosis, molecular changes, and treatment of G3 GEP NET are described. Yet, the level of evidence for treatment recommendations is low, as most studies are retrospective. A treatment algorithm for G3 GEP NET is proposed.
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