Plasma protein biomarkers for the detection of pancreatic neuroendocrine tumors and differentiation from small intestinal neuroendocrine tumors

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Nordic NET Biomarker Grp , Thiis-Evensen , E , Kjellman , M , Knigge , U , Gronbaek , H , Schalin-Jäntti , C , Welin , S , Sorbye , H , Schneider , M D P & Belusa , R 2022 , ' Plasma protein biomarkers for the detection of pancreatic neuroendocrine tumors and differentiation from small intestinal neuroendocrine tumors ' , Journal of Neuroendocrinology , vol. 34 , no. 7 , 13176 . https://doi.org/10.1111/jne.13176

Title: Plasma protein biomarkers for the detection of pancreatic neuroendocrine tumors and differentiation from small intestinal neuroendocrine tumors
Author: Nordic NET Biomarker Grp; Thiis-Evensen, Espen; Kjellman, Magnus; Knigge, Ulrich; Gronbaek, Henning; Schalin-Jäntti, Camilla; Welin, Staffan; Sorbye, Halfdan; Schneider, Maria del Pilar; Belusa, Roger
Contributor organization: HUS Abdominal Center
Clinicum
University of Helsinki
Endokrinologian yksikkö
Date: 2022-07
Language: eng
Number of pages: 9
Belongs to series: Journal of Neuroendocrinology
ISSN: 0953-8194
DOI: https://doi.org/10.1111/jne.13176
URI: http://hdl.handle.net/10138/346661
Abstract: There is an unmet need for novel biomarkers to diagnose and monitor patients with neuroendocrine neoplasms. The EXPLAIN study explores a multi-plasma protein and supervised machine learning strategy to improve the diagnosis of pancreatic neuroendocrine tumors (PanNET) and differentiate them from small intestinal neuroendocrine tumors (SI-NET). At time of diagnosis, blood samples were collected and analyzed from 39 patients with PanNET, 135 with SI-NET (World Health Organization Grade 1-2) and 144 controls. Exclusion criteria were other malignant diseases, chronic inflammatory diseases, reduced kidney or liver function. Prosed Oncology-II (i.e., OLink) was used to measure 92 cancer related plasma proteins. Chromogranin A was analyzed separately. Median age in all groups was 65-67 years and with a similar sex distribution (females: PanNET, 51%; SI-NET, 42%; controls, 42%). Tumor grade (G1/G2): PanNET, 39/61%; SI-NET, 46/54%. Patients with liver metastases: PanNET, 78%; SI-NET, 63%. The classification model of PanNET versus controls provided a sensitivity (SEN) of 0.84, specificity (SPE) 0.98, positive predictive value (PPV) of 0.92 and negative predictive value (NPV) of 0.95, and area under the receiver operating characteristic curve (AUROC) of 0.99; the model for the discrimination of PanNET versus SI-NET providing a SEN 0.61, SPE 0.96, PPV 0.83, NPV 0.90 and AUROC 0.98. These results suggest that a multi-plasma protein strategy can significantly improve diagnostic accuracy of PanNET and SI-NET.
Subject: biomarker
diagnosis
machine learning
NET
Plasma proteins
CHROMOGRANIN-A
CLINICAL UTILITY
DIAGNOSIS
POLYPEPTIDE
CANCER
MANAGEMENT
NEOPLASMS
3121 General medicine, internal medicine and other clinical medicine
3112 Neurosciences
3124 Neurology and psychiatry
Peer reviewed: Yes
Rights: cc_by_nc_nd
Usage restriction: openAccess
Self-archived version: publishedVersion


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