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Poster display session

5209 - Plasma Protein Fingerprinting and Machine Learning for the Diagnosis of Small Intestinal Neuroendocrine Tumors: The Nordic NET Biomarker Group EXPLAIN Study


10 Sep 2017


Poster display session


Neuroendocrine Tumours


Magnus Kjellman


Annals of Oncology (2017) 28 (suppl_5): v142-v157. 10.1093/annonc/mdx368


M.M. Kjellman1, S. Welin2, U. Knigge3, H. Gronbaek4, H. Sorbye3, E. Thiis-Evensen5, M. Thyregod Jørgensen6, V. Johanson7, C. Schalin-Jäntti8, P. Myrenfors9, T. Ström9, K. Becker9, R. Belusa9

Author affiliations

  • 1 Dept Of Endocrine Surgery, Karolinska Institutet, 17176 - Stockholm/SE
  • 2 Dept. Of Endocrine Oncology, Uppsala akademiska, 75185 - Uppsala/SE
  • 3 Dept Of Surgery, Rigshospitalet, Copenhagen University Hospital, 2100 - Copenhagen/DK
  • 4 Dept. Of Clinical Medicine, Aarhus university hospital, 8200 - aarhus/SE
  • 5 Dept. Of Gastroenterology, Oslo university hospital, Oslo/SE
  • 6 Medicinsk Gastroenterologi, Odense University Hospital, 5230 - Odense/DK
  • 7 Surgery, Sahlgrenska Hospital, 405 30 - Gothenburg/SE
  • 8 Depar. Endocrinology, Helsinki University Hospita, Helsinki/FI
  • 9 Medical Depart., IPSEN, Stockholm/SE


Abstract 5209


Small intestinal neuroendocrine tumors (siNETs) are notoriously difficult to diagnose, especially in an early stage. The EXPLAIN study aimed to investigate 92 plasma proteins (PP), previously shown to be cancer related, in an attempt to improve the accuracy in diagnosis of siNETs.


This non-interventional exploratory study in the nordic countries analysed 136 patients with siNET from 17 hospitals and 144 age and sex matched controls (all with written consent). Exclusion criteria: NET not confirmed, previously treated for NET, other malignant diseases, chronic inflammatory disease, kidney or liver failure. Blood samples (4 ml) were obtained at first visit. Samples analysis used the Proseek Multiplex Oncology II assay (OLink) to measure relative levels of the 92-cancer related PP. In addition, chromogranin A (CgA) was analyzed centrally (Akademiska Lab. Uppsala). Data was subjected to statistical supervised learning techniques (SSLT): random forest and support vector machine.


This is the first interim analysis. Patient characteristics: age 65±10 (mean±SD), 58% male, 48% G1 and 52% G2, 88% N1 and 65% M1, 23% >3 bowel mov/d and 11.5% >3 flushes/d. CgA (mean (SD), nmol/L) in 115 patients free from proton pump inhibitor treatment (PPI): 42.37 (86.62), in 21 NET patients with PPI: 68.41 (74.21), in 132 controls free from PPI: 3.67 (3.57) and in 12 controls treated with PPI: 11.83 (8.97). Several PP (>20) showed significant p


Both a high level of sensitivity and specificity (0.9) were obtained using our multi plasma protein strategy combined with SSLT for the diagnosis of siNET. Further development of the machine learning model is ongoing.

Clinical trial identification

Legal entity responsible for the study

Peter Myrenfors Ipsen




All authors have declared no conflicts of interest.

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