Magnus Kjellman1, Ulrich Knigge2, Staffan Welin3, Espen Thiis-Evensen4, Henning Gronbaek5, Camilla Schalin-Jäntti6, Halfdan Sorbye7, Maiken Thyregod Joergensen8, Viktor Johanson9, Saara Metso10, Helge Waldum11, Jon Arne Søreide12, Tapani Ebeling13, Fredrik Lindberg14, Kalle Landerholm15, Goran Wallin16, Farhad Salem17, Maria Del Pilar Schneider18, Roger Belusa19. 1. Endocrine Surgery Unit, Karolinska Hospital, Stockholm, Sweden, magnus.kjellman@ki.se. 2. Department of Endocrinology and Gastrointestinal Surgery, ENETS Neuroendocrine Tumor Centre of Excellence, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark. 3. Department of Endocrine Oncology, ENETS Neuroendocrine Tumor Centre of Excellence, Uppsala University Hospital, Uppsala, Sweden. 4. Department of Gastroenterology, ENETS Neuroendocrine Tumor Centre of Excellence, Oslo University Hospital, Rikshospitalet, Oslo, Norway. 5. Department of Hepatology and Gastroenterology, ENETS Neuroendocrine Tumor Centre of Excellence, Aarhus University Hospital, Aarhus, Denmark. 6. Department of Endocrinology, Abdominal Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland. 7. Department of Oncology and Department of Clinical Science, Haukeland University Hospital, Bergen, Norway. 8. Department of Medical Gastroenterology and Hepatology, Odense University Hospital, Odense, Denmark. 9. Department of Surgery, Institute of Clinical Sciences at the Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden. 10. Unit of Endocrinology, Department of Internal Medicine, Tampere University Hospital, Teiskontie Tampere, Tampere, Finland. 11. St. Olavs Hospital, Trondheim, Norway. 12. Department of Gastrointestinal Surgery, Stavanger University Hospital, Stavanger, Norway. 13. Faculty of Medicine, University of Oulu, Finland and Division of Endocrinology, Oulu University Hospital, Oulu, Finland. 14. Department of Surgery, Norrland University Hospital, Umeå, Sweden. 15. Department of Clinical and Experimental Medicine, Linköping University and Department of Surgery, Ryhov County Hospital, Jönköping, Sweden. 16. Faculty of Medicine and Health, Örebro University Hospital, Örebro, Sweden. 17. Skånes University Hospital, Unit for Endocrine and Sarcoma Surgery, Lund, Sweden. 18. IPSEN Innovation SAS, Les Ulis, France. 19. IPSEN, Kista Science Tower, Kista, Sweden.
Abstract
BACKGROUND: Small intestinal neuroendocrine tumors (SI-NETs) are difficult to diagnose in the early stage of disease. Current blood biomarkers such as chromogranin A (CgA) and 5-hydroxyindolacetic acid have low sensitivity (SEN) and specificity (SPE). This is a first preplanned interim analysis (Nordic non-interventional, prospective, exploratory, EXPLAIN study [NCT02630654]). Its objective is to investigate if a plasma protein multi-biomarker strategy can improve diagnostic accuracy (ACC) in SI-NETs. METHODS: At the time of diagnosis, before any disease-specific treatment was initiated, blood was collected from patients with advanced SI-NETs and 92 putative cancer-related plasma proteins from 135 patients were analyzed and compared with the results of age- and sex-matched controls (n = 143), using multiplex proximity extension assay and machine learning techniques. RESULTS: Using a random forest model including 12 top ranked plasma proteins in patients with SI-NETs, the multi-biomarker strategy showed SEN and SPE of 89 and 91%, respectively, with negative predictive value (NPV) and positive predictive value (PPV) of 90 and 91%, respectively, to identify patients with regional or metastatic disease with an area under the receiver operator characteristic curve (AUROC) of 99%. In 30 patients with normal CgA concentrations, the model provided a diagnostic SPE of 98%, SEN of 56%, and NPV 90%, PPV of 90%, and AUROC 97%, regardless of proton pump inhibitor intake. CONCLUSION: This interim analysis demonstrates that a multi-biomarker/machine learning strategy improves diagnostic ACC of patients with SI-NET at the time of diagnosis, especially in patients with normal CgA levels. The results indicate that this multi-biomarker strategy can be useful for early detection of SI-NETs at presentation and conceivably detect recurrence after radical primary resection.
BACKGROUND: Small intestinal neuroendocrine tumors (SI-NETs) are difficult to diagnose in the early stage of disease. Current blood biomarkers such as chromogranin A (CgA) and 5-hydroxyindolacetic acid have low sensitivity (SEN) and specificity (SPE). This is a first preplanned interim analysis (Nordic non-interventional, prospective, exploratory, EXPLAIN study [NCT02630654]). Its objective is to investigate if a plasma protein multi-biomarker strategy can improve diagnostic accuracy (ACC) in SI-NETs. METHODS: At the time of diagnosis, before any disease-specific treatment was initiated, blood was collected from patients with advanced SI-NETs and 92 putative cancer-related plasma proteins from 135 patients were analyzed and compared with the results of age- and sex-matched controls (n = 143), using multiplex proximity extension assay and machine learning techniques. RESULTS: Using a random forest model including 12 top ranked plasma proteins in patients with SI-NETs, the multi-biomarker strategy showed SEN and SPE of 89 and 91%, respectively, with negative predictive value (NPV) and positive predictive value (PPV) of 90 and 91%, respectively, to identify patients with regional or metastatic disease with an area under the receiver operator characteristic curve (AUROC) of 99%. In 30 patients with normal CgA concentrations, the model provided a diagnostic SPE of 98%, SEN of 56%, and NPV 90%, PPV of 90%, and AUROC 97%, regardless of proton pump inhibitor intake. CONCLUSION: This interim analysis demonstrates that a multi-biomarker/machine learning strategy improves diagnostic ACC of patients with SI-NET at the time of diagnosis, especially in patients with normal CgA levels. The results indicate that this multi-biomarker strategy can be useful for early detection of SI-NETs at presentation and conceivably detect recurrence after radical primary resection.
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