A Plasma Protein Biomarker Strategy for Detection of Small Intestinal Neuroendocrine Tumors [Elektronisk resurs]
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Kjellman, Magnus (författare)
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Knigge, Ulrich (författare)
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Welin, Staffan (författare)
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Thiis-Evensen, Espen (författare)
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Gronbæk, Henning (författare)
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Schalin-Jäntti, Camilla (författare)
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Sorbye, Halfdan (författare)
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Joergensen, Maiken Thyregod (författare)
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Johanson, Viktor (författare)
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Metso, Saara (författare)
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Waldum, Helge (författare)
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Søreide, Jon Arne (författare)
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Ebeling, Tapani (författare)
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Lindberg, Fredrik (författare)
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Landerholm, Kalle (författare)
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Wallin, Goran (författare)
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Salem, Farhad (författare)
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Schneider, Maria del Pilar (författare)
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Belusa, Roger (författare)
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Umeå universitet Medicinska fakulteten (utgivare)
- Publicerad: S. Karger, 2021
- Engelska.
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Ingår i: Neuroendocrinology. - 0028-3835. ; 111, 840-849
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- Relaterad länk:
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http://www.umu.se/ (Värdpublikation)
Sammanfattning
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- 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.
Ämnesord
- Medical and Health Sciences (hsv)
- Clinical Medicine (hsv)
- Surgery (hsv)
- Medicin och hälsovetenskap (hsv)
- Klinisk medicin (hsv)
- Kirurgi (hsv)
Genre
- government publication (marcgt)
Indexterm och SAB-rubrik
- Neuroendocrine tumor
- Biomarker
- Diagnosis
- Machine learning
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Neuroendocrinology