Abstract
To evaluate pancreatic neuroendocrine neoplasms (panNENs) grade prediction by means of qualitative and quantitative CT evaluation, and 3D CT-texture analysis. Patients with histopathologically-proven panNEN, availability of Ki67% values and pre-treatment CT were included. CT images were retrospectively reviewed, and qualitative and quantitative images analysis were done; for quantitative analysis four enhancement-ratios and three permeability-ratios were created. 3D CT-texture imaging analysis was done (Mean Value; Variance; Skewness; Kurtosis; Entropy). Subsequently, these features were compared among the three grading (G) groups. 304 patients affected by panNENs were considered, and 100 patients were included. At qualitative evaluation, frequency of irregular margins was significantly different between tumor G groups. At quantitative evaluation, for all ratios, comparisons resulted statistical significant different between G1 and G3 groups and between G2 and G3 groups. At 3D CT-texture analysis, Kurtosis resulted statistical significant different among three G groups and Entropy resulted statistical significant different between G1 and G3 and between G2 and G3 groups. Quantitative CT evaluation of panNENs can predict tumor grade, discerning G1 from G3 and G2 from G3 tumors. CT-texture analysis can predict panNENs tumor grade, distinguishing G1 from G3 and G2 from G3, and G1 from G2 tumors.
Highlights
The assessment of tumor grade can be achieved in an invasive way with biopsy or after surgery with surgical specimen histopathological analysis
From our Institute archives, 304 patients affected by PanNEN were considered
204 patients were excluded due to un-availability of computed tomography (CT) examinations, caused by damaged DICOM files or by old DICOM files not stored in the PACS
Summary
The assessment of tumor grade can be achieved in an invasive way with biopsy or after surgery with surgical specimen histopathological analysis. Several studies attempted to identify radiological predictors of malignancy for PanNENs5–11, including tumor conspicuity in MDCT images, CT perfusion parameters, and values on MRI, including values in ADC and DWI images. Despite very few literature data, the computed texture analysis of computed tomography (CT) data seems to be able to provide predictive metrics for several pathological features. Lubner MG et al.[13] reported an association between CT texture features with pathological features and clinical outcomes in patients with metastatic colorectal cancer. The aim of this study was to evaluate PanNENs grade prediction possibility by means of CT qualitative and quantitative analysis as well as of CT 3D texture analysis
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