CT-derived radiomic features to discriminate histologic characteristics of pancreatic neuroendocrine tumors



To assess the ability of radiomic features (RF) extracted from contrast-enhanced CT images (ceCT) and non-contrast-enhanced (non-ceCT) in discriminating histopathologic characteristics of pancreatic neuroendocrine tumors (panNET).


panNET contours were delineated on pre-surgical ceCT and non-ceCT. First- second- and higher-order RF (adjusted to eliminate redundancy) were extracted and correlated with histological panNET grade (G1 vs G2/G3), metastasis, lymph node invasion, microscopic vascular infiltration. Mann–Whitney with Bonferroni corrected p values assessed differences. Discriminative power of significant RF was calculated for each of the end-points. The performance of conventional-imaged-based-parameters was also compared to RF.


Thirty-nine patients were included (mean age 55-years-old; 24 male). Mean diameters of the lesions were 24 × 27 mm. Sixty-nine RF were considered. Sphericity could discriminate high grade tumors (AUC = 0.79, p = 0.002). Tumor volume (AUC = 0.79, p = 0.003) and several non-ceCT and ceCT RF were able to identify microscopic vascular infiltration: voxel-alignment, neighborhood intensity-difference and intensity-size-zone families (AUC ≥ 0.75, p < 0.001); voxel-alignment, intensity-size-zone and co-occurrence families (AUC ≥ 0.78, p ≤ 0.002), respectively). Non-ceCT neighborhood-intensity-difference (AUC = 0.75, p = 0.009) and ceCT intensity-size-zone (AUC = 0.73, p = 0.014) identified lymph nodal invasion; several non-ceCT and ceCT voxel-alignment family features were discriminative for metastasis (p < 0.01, AUC = 0.80–0.85). Conventional CT ‘necrosis’ could discriminate for microscopic vascular invasion (AUC = 0.76, p = 0.004) and ‘arterial vascular invasion’ for microscopic metastasis (AUC = 0.86, p = 0.001). No conventional-imaged-based-parameter was significantly associated with grade and lymph node invasion.


Radiomic features can discriminate histopathology of panNET, suggesting a role of radiomics as a non-invasive tool for tumor characterization.

Trial registration number: NCT03967951, 30/05/2019

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The study was supported by an AIRC grant (IG18965); the PI is professor Massimo Falconi. PhD Scholarship of Dr Valentina Andreasi and Research Fellowship of Dr. Francesca Muffatti were supported by Gioja Bianca Costanza Fund.

Author information




All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Giulia Benedetti, Martina Mori, Marta Maria Panzeri, Maurizio Barbera and Francesca Muffatti. The first draft of the manuscript was written by Giulia Benedetti and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Francesco De Cobelli.

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Ethics approval research involves human participants

This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of IRCCS Ospedale San Raffaele (19/INT/ 2019). The study is registered at ClinicalTrial.gov (registration number NCT03967951, 31/05/2019).

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Benedetti, G., Mori, M., Panzeri, M.M. et al. CT-derived radiomic features to discriminate histologic characteristics of pancreatic neuroendocrine tumors. Radiol med (2021). https://doi.org/10.1007/s11547-021-01333-z

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  • Pancreatic neoplasms
  • Neuroendocrine tumors
  • Radiomic features
  • Area under the curve (AUC)
  • Computed tomography