Shonan Sho1,2,3, Colin M Court4,5, Paul Winograd4,5, Paul A Toste4, Joseph R Pisegna6,7, Michael Lewis8, Timothy R Donahue4,9, Oscar J Hines4,9, Howard A Reber4,9, David W Dawson10,11, James S Tomlinson4,5,9. 1. Department of Surgery, University of California Los Angeles, Los Angeles, CA, 90095, USA. ssho@mednet.ucla.edu. 2. Department of Surgery, VA Greater Los Angeles Healthcare System, Los Angeles, CA, 90073, USA. ssho@mednet.ucla.edu. 3. , Los Angeles, USA. ssho@mednet.ucla.edu. 4. Department of Surgery, University of California Los Angeles, Los Angeles, CA, 90095, USA. 5. Department of Surgery, VA Greater Los Angeles Healthcare System, Los Angeles, CA, 90073, USA. 6. Division of Gastroenterology, Hepatology and Parenteral Nutrition, VA Greater Los Angeles Healthcare System, Los Angeles, CA, 90095, USA. 7. Department of Medicine and Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, 90095, USA. 8. Department of Pathology, VA Greater Los Angeles Healthcare System, Los Angeles, CA, 90073, USA. 9. UCLA Center for Pancreatic Diseases, Los Angeles, CA, 90095, USA. 10. Department of Pathology and Laboratory Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, 90095, USA. 11. Jonsson Comprehensive Cancer Center, David Geffen School of Medicine at UCLA, Los Angeles, CA, 90095, USA.
Abstract
BACKGROUND: Patients with early-stage pancreatic neuroendocrine tumors (PNETs) may develop metastatic recurrences despite undergoing potentially curative pancreas resections. We sought to identify factors predictive of metastatic recurrences and develop a prognostication strategy to predict recurrence-free survival (RFS) in resected PNETs. METHODS: Patients with localized PNETs undergoing surgical resection between 1989 and 2015 were identified. Univariate and multivariate analysis were used to identify potential predictors of post-resection metastasis. A score-based prognostication system was devised using the identified factors. The bootstrap model validation methodology was utilized to estimate the external validity of the proposed prognostication strategy. RESULTS: Of the 140 patients with completely resected early-stage PNETs, overall 5- and 10-year RFS were 84.6% and 67.1%, respectively. The median follow-up was 56 months. Multivariate analysis identified tumor size > 5 cm, Ki-67 index 8-20%, lymph node involvement, and high histologic grade (G3, or Ki-67 > 20%) as independent predictors of post-resection metastatic recurrence. A scoring system based on these factors stratified patients into three prognostic categories with distinct 5-year RFS: 96.9%, 54.8%, and 33.3% (P < 0.0001). The bootstrap model validation methodology projected our proposed prognostication strategy to retain a high predictive accuracy even when applied in an external dataset (validated c-index of 0.81). CONCLUSIONS: The combination of tumor size, LN status, grade, and Ki-67 was identified as the most highly predictive indicators of metastatic recurrences in resected PNETs. The proposed prognostication strategy may help stratify patients for adjuvant therapies, enhanced surveillance protocols and future clinical trials.
BACKGROUND: Patients with early-stage pancreatic neuroendocrine tumors (PNETs) may develop metastatic recurrences despite undergoing potentially curative pancreas resections. We sought to identify factors predictive of metastatic recurrences and develop a prognostication strategy to predict recurrence-free survival (RFS) in resected PNETs. METHODS: Patients with localized PNETs undergoing surgical resection between 1989 and 2015 were identified. Univariate and multivariate analysis were used to identify potential predictors of post-resection metastasis. A score-based prognostication system was devised using the identified factors. The bootstrap model validation methodology was utilized to estimate the external validity of the proposed prognostication strategy. RESULTS: Of the 140 patients with completely resected early-stage PNETs, overall 5- and 10-year RFS were 84.6% and 67.1%, respectively. The median follow-up was 56 months. Multivariate analysis identified tumor size > 5 cm, Ki-67 index 8-20%, lymph node involvement, and high histologic grade (G3, or Ki-67 > 20%) as independent predictors of post-resection metastatic recurrence. A scoring system based on these factors stratified patients into three prognostic categories with distinct 5-year RFS: 96.9%, 54.8%, and 33.3% (P < 0.0001). The bootstrap model validation methodology projected our proposed prognostication strategy to retain a high predictive accuracy even when applied in an external dataset (validated c-index of 0.81). CONCLUSIONS: The combination of tumor size, LN status, grade, and Ki-67 was identified as the most highly predictive indicators of metastatic recurrences in resected PNETs. The proposed prognostication strategy may help stratify patients for adjuvant therapies, enhanced surveillance protocols and future clinical trials.
Authors: Nikiforos Ballian; Agnes G Loeffler; Victoria Rajamanickam; Peter A Norstedt; Sharon M Weber; Clifford S Cho Journal: HPB (Oxford) Date: 2009-08 Impact factor: 3.647
Authors: Cristina R Ferrone; Laura H Tang; James Tomlinson; Mithat Gonen; Steven N Hochwald; Murray F Brennan; David S Klimstra; Peter J Allen Journal: J Clin Oncol Date: 2007-12-10 Impact factor: 44.544
Authors: Joshua S Hill; James T McPhee; Theodore P McDade; Zheng Zhou; Mary E Sullivan; Giles F Whalen; Jennifer F Tseng Journal: Cancer Date: 2009-02-15 Impact factor: 6.860
Authors: Thorvardur R Halfdanarson; Joseph Rubin; Michael B Farnell; Clive S Grant; Gloria M Petersen Journal: Endocr Relat Cancer Date: 2008-06 Impact factor: 5.678
Authors: Karl Y Bilimoria; Mark S Talamonti; James S Tomlinson; Andrew K Stewart; David P Winchester; Clifford Y Ko; David J Bentrem Journal: Ann Surg Date: 2008-03 Impact factor: 12.969
Authors: Karl Y Bilimoria; James S Tomlinson; Ryan P Merkow; Andrew K Stewart; Clifford Y Ko; Mark S Talamonti; David J Bentrem Journal: J Gastrointest Surg Date: 2007-09-11 Impact factor: 3.452
Authors: Masayuki Tanaka; Max Heckler; André L Mihaljevic; Pascal Probst; Ulla Klaiber; Ulrike Heger; Simon Schimmack; Markus W Büchler; Thilo Hackert Journal: Ann Surg Oncol Date: 2020-07-27 Impact factor: 5.344
Authors: Angela Lamarca; Hamish Clouston; Jorge Barriuso; Mairéad G McNamara; Melissa Frizziero; Was Mansoor; Richard A Hubner; Prakash Manoharan; Sarah O'Dwyer; Juan W Valle Journal: J Clin Med Date: 2019-10-05 Impact factor: 4.241