The ENETS guidelines: the new TNM classification system

Area of Science:

• Endocrinology and Oncology
• Gastroenteropancreatic Neuroendocrine Tumors (GEP NETs)
• Lung Neuroendocrine Tumors (LNETs)

Background:

• Current classification systems for neuroendocrine tumors (NETs) include the World Health Organization (WHO) definitions and the European Neuroendocrine Tumor Society (ENETS) grading and TNM staging systems.
• These systems aid in the general classification and patient stratification of GEP NETs, aligning with European Union clinical practices.
• The WHO also utilizes a four-class system for lung endocrine tumors, enabling prognostically effective patient stratification.

Purpose of the Study:

• To evaluate the utility of the WHO classification for GEP NETs in current clinical practice.
• To assess the complementary roles of ENETS grading and TNM staging in improving patient stratification for GEP NETs.
• To investigate the prognostic value of these classification and staging systems across different gut sites and in prospective studies.

Main Methods:

• Analysis of the WHO's 3-class system for well-differentiated endocrine tumors, well-differentiated endocrine carcinoma, and poorly differentiated carcinoma.
• Review of the ENETS grading and TNM staging systems for GEP NETs.
• Examination of the WHO's four-class system for lung endocrine tumors.

Main Results:

• The WHO definition provides an efficient general classification for GEP NETs, suitable for EU clinical use.
• ENETS grading and TNM staging are valuable complementary tools for patient stratification, with strong recommendations for their adoption.
• Further investigation is required to fully establish the prognostic value of these systems across all gut sites and in prospective series.

Conclusions:

• The WHO classification system is effective for general classification of GEP NETs.
• ENETS grading and TNM staging systems are recommended for enhanced patient stratification.
• A unified grading system for both lung and GEP NETs is advocated to improve prognostic accuracy and patient management.