Abstract Library
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#2977 Predicting Survival in Patients with a Neuroendocrine Tumor of the Small Intestine (SI-NET)
Introduction: SI-NET comprise the largest group of neuroendocrine tumors. Because of their heterogeneity, predicting survival has proven challenging. A nomogram (NG) to assess SI-NET disease specific survival (DSS) has been developed in 2010 by Modlin, et al. based on analysis of retrospective data from the Surveillance, Epidemiology and End Results (SEER) database.
Conference: 17th Annual ENETSConcerence (2020)
Presenting Author:
Authors: Levy S, van Veenendaal L, Korse C, Verbeek W, Vriens M,
Introduction: Neuroendocrine differentiation and an aggressive phenotype is an important feature of castration-resistant prostate cancer (CRPC). Current biomarkers are unable to detect these advanced, treatment refractory variants.
Conference: 17th Annual ENETSConcerence (2020)
Presenting Author: Modlin I
Authors: Modlin I, Kidd M, Rahbar K, Malczewska A, Rajwa P,
Keywords: Biomarker, NETest, PSA, prostate cancer, neuroendocrine phenotype, castration-resistance, neuroendocrine,
Introduction: There is currently no effective blood biomarker for lung neuroendocrine neoplasia diagnosis and management.
Conference: 15th Annual ENETSConcerence (2018)
Presenting Author: Modlin I
Authors: Modlin I, Kidd M, Lewczuk-Myślińska A, Chung K, Ćwikła J,
Keywords: bronchopulmonary, lung, biomarker, PCR, diagnostics, progression,
#1878 Measurement in Blood of a Circulating NET mRNA Gene to Predict Surgical Efficacy
Introduction: Surgery is the only curative treatment for GEP-NETs, but identifying post-surgery residual disease is difficult and often delayed.
Conference: 14th Annual ENETSConcerence (2017)
Presenting Author: Kidd M
Authors: Modlin I, Frilling A, Salem R, Cichocki A, Ćwikła J,
Introduction: Most small bowel neuroendocrine tumours (SBNET) are Grade 1 (G1), and exhibit at least nodal metastasis at diagnosis. The clinical behaviours of G1 SBNET remain poorly predictable. The ‘NET nomogram’ developed by Modlin et al. designates SBNET as low/medium/high risk by mathematical analysis of 15 clinicopathological parameters.
Conference: 14th Annual ENETSConcerence (2017)
Presenting Author: Clift A