Table 1.

Published papers on the value of CT texture analysis in predicting the grade in pNEN.

	No of Patients	Images Phase/ROI/Sofware	Type RF	RF Correlated with G
D’Onofiro et al., 2019 [84], Sci Rep	100	Pancreatic 2D ROI 1 slice MaZda 4.6	1st order	Kurtosis and entropy
Guo et al., 2018 [79], Abdo Imaging	37	Arterial 2D Five slices Matlab 2014a	1st order	Mean grey level intensity
Canellas et al., 2018 [81], AJR	101	Portal TextRad 2D ROI	1st order	Entropy
Choi et al., 2018 [82], Acta Radiol	66	Arterial + portal 2D ROI	1st order	Sphericity, skewness, kurtosis
Gu et al., 2018 [80], Eur Radiol	104 training 34 validation	Arterial and portal 3D ROI Pyradiomics 1.3.0	1st, 2nd, and 3rd order radiomics signature: 15 arterial RF + 10 portal RF from 853 RF	Radiomics signature on arterial + portal images Nomagram: Radiomics + tumor margin
Liang et al., 2019 [83], Clin Cancer Res	86 training 51 validation	Arterial 3D ROI In-house software	1st, 2nd, and 3rd order radiomics signature 8RF from 467 RF	Monogram: Radiomics signature on arterial + clinical stage

MRI-based radiomics in prostate cancer und neuroendocrine tumors.