AIM To identify the multidetector computed tomography (MDCT) features of pancreatic neuroendocrine tumours (pNETs), which correlate with tumour histology and enable preoperative grading. MATERIALS AND METHODS Thirty-nine patients with histologically confirmed pNET who underwent preoperative contrast-enhanced MDCT were included in this study. Nineteen tumours were classified as Grade 1 (G1) and 20 as Grade 2 (G2). Histopathology slides were reviewed to assess the intratumoural microvascular density (MVD) and the amount of tumour stroma. Computed tomography (CT) image analysis included tumour size, margin delineation, calcifications, homogeneity, contrast enhancement (CE) pattern, tumour absolute and relative enhancement, presence of cystic changes, pancreatic duct dilatation, regional and distant metastases. The diagnostic ability to predict tumour grade was measured for each MDCT finding and their combinations. RESULTS The mean arterial enhancement ratio had a mean±standard deviation of 1.53±0.45 in G1 and 1.01±0.33 in G2 pNETs (p=0.0003) and correlated with intratumoural microvascular density (MVD; r=0.55, p=0.0002). Tissue stroma percentage did not correlate with imaging findings. Late CE of the tumour (the peak attenuation observed in the venous phase) was significantly associated with G2. Tumour size ≥20 mm, arterial enhancement ratio <1.1, and late CE showed 74.4%, 79.5%, and 74.4% accuracy, respectively, in diagnosing G2 tumours, while the accuracy of at least two of these criteria used in combination was 82%. Based on these results, a diagnostic algorithm was proposed, which showed high interobserver agreement (k=0.82) in the prediction of tumour grade. CONCLUSION Contrast-enhanced MDCT features correlate with histological findings and enable the differentiation between G1 and G2 pNETs during preoperative examination.

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Abstract. The study proposes the way of data organization to store the information obtained as the result of measurements of morphological structures of prostate gland preparations in the computer microscopy system. The proposed data structure provides fast access to object features in interactive analysis of preparation images during oncologic diagnostics of prostate gland diseases.