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.