4.6 Article

Diagnostic imaging of gastrointestinal neuroendocrine tumours (GI-NETs): relationship between MDCT features and 2010 WHO classification

期刊

RADIOLOGIA MEDICA
卷 124, 期 2, 页码 94-102

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SPRINGER-VERLAG ITALIA SRL
DOI: 10.1007/s11547-018-0946-8

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Gastrointestinal neuroendocrine tumours; Multi-detector computed tomography; Intestinal signs; Extra-intestinal signs; Pathological classification

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AimsWe aimed to present our series of gastrointestinal neuroendocrine tumours (GI-NETs) in order to illustrate and highlight the associated contrast-enhanced multi-detector computed tomography (MDCT) features. We also attempted to identify a relationship between MDCT imaging and the 2010 World Health Organization (WHO) classification system.Materials and methodsWe selected all patients with pathologically proven GI-NETs diagnosed between January 2010 and August 2017. Only patients undergone contrast-enhanced MDCT imaging in the immediate preoperative period were included in our study. Later, two expert radiologists retrospectively assessed MDCT intestinal and extra-intestinal signs. We also analysed the relationship between MDCT imaging and the 2010 WHO classification.ResultsA total of 20 patients (13 males, 7 females, age range 37-89years, mean age 69.9years) were included in our study. The majority of GI-NETs (85%) occurred in the small bowel and mainly in the terminal ileum. Forty-five percentage of our GI-NETs were diagnosed after an access to emergency medical service for obstruction symptoms or gastrointestinal bleeding. Regarding intestinal signs, 15/20 patients showed an intraluminal nodular mass and 5/20 a wall thickening. Extra-intestinal signs were present in 75% of cases. Desmoplastic reaction and lymph nodes metastases were significantly correlated with higher grade of GI-NETs.ConclusionsThe majority of GI-NETs appears as intraluminal mass often associated with extra-intestinal signs. We found a significantly correlation between higher grade of GI-NETs and extra-intestinal signs. MDCT imaging may be useful in predicting the pathological classification of GI-NETs.

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