Back to Search Start Over

Prognostic Factors Across Poorly Differentiated Neuroendocrine Neoplasms: a Pooled Analysis

Authors :
Giovanni Centonze
Patrick Maisonneuve
Natalie Prinzi
Sara Pusceddu
Luca Albarello
Eleonora Pisa
Massimo Barberis
Alessandro Vanoli
Paola Spaggiari
Paola Bossi
Laura Cattaneo
Giovanna Sabella
Enrico Solcia
Stefano La Rosa
Federica Grillo
Giovanna Tagliabue
Aldo Scarpa
Mauro Papotti
Marco Volante
Alessandro Mangogna
Alessandro Del Gobbo
Stefano Ferrero
Luigi Rolli
Elisa Roca
Luisa Bercich
Mauro Benvenuti
Luca Messerini
Frediano Inzani
Giancarlo Pruneri
Adele Busico
Federica Perrone
Elena Tamborini
Alessio Pellegrinelli
Ketevani Kankava
Alfredo Berruti
Ugo Pastorino
Nicola Fazio
Fausto Sessa
Carlo Capella
Guido Rindi
Massimo Milione
Centonze, Giovanni
Maisonneuve, Patrick
Prinzi, Natalie
Pusceddu, Sara
Albarello, Luca
Pisa, Eleonora
Barberis, Massimo
Vanoli, Alessandro
Spaggiari, Paola
Bossi, Paola
Cattaneo, Laura
Sabella, Giovanna
Solcia, Enrico
La Rosa, Stefano
Grillo, Federica
Tagliabue, Giovanna
Scarpa, Aldo
Papotti, Mauro
Volante, Marco
Mangogna, Alessandro
Del Gobbo, Alessandro
Ferrero, Stefano
Rolli, Luigi
Roca, Elisa
Bercich, Luisa
Benvenuti, Mauro
Messerini, Luca
Inzani, Frediano
Pruneri, Giancarlo
Busico, Adele
Perrone, Federica
Tamborini, Elena
Pellegrinelli, Alessio
Kankava, Ketevani
Berruti, Alfredo
Pastorino, Ugo
Fazio, Nicola
Sessa, Fausto
Capella, Carlo
Rindi, Guido
Milione, Massimo
Publication Year :
2023

Abstract

Introduction: Poorly differentiated neuroendocrine carcinomas (NECs) are characterized by aggressive clinical course and poor prognosis. No reliable prognostic markers have been validated to date; thus, the definition of a specific NEC prognostic algorithm represents a clinical need. This study aimed to analyze a large NEC case series to validate the specific prognostic factors identified in previous studies on gastro-entero-pancreatic and lung NECs and to assess if further prognostic parameters can be isolated. Methods: A pooled analysis of four NEC retrospective studies was performed to evaluate the prognostic role of Ki-67 cut-off, the overall survival (OS) according to primary cancer site, and further prognostic parameters using multivariable Cox proportional hazards model and machine learning random survival forest (RSF). Results: 422 NECs were analyzed. The most represented tumor site was the colorectum (n = 156, 37%), followed by the lungs (n = 111, 26%), gastroesophageal site (n = 83, 20%; 66 gastric, 79%) and pancreas (n = 42, 10%). The Ki-67 index was the most relevant predictor, followed by morphology (pure or mixed/combined NECs), stage, and site. The predicted RSF response for survival at 1, 2, or 3 years showed decreasing survival with increasing Ki-67, pure NEC morphology, stage III–IV, and colorectal NEC disease. Patients with Ki-67 Conclusion: The most effective parameters to predict OS for NEC patients could be Ki-67, pure or mixed/combined morphology, stage, and site.

Details

Language :
English
Database :
OpenAIRE
Accession number :
edsair.doi.dedup.....2c96326f1d84f9924997bf8086dc7966