Predicting grade 1 (G1) and 2 (G2) primary pancreatic neuroendocrine tumour (panNET) is crucial to foresee panNET clinical behaviour. 51 patients with G1-G2 primary panNET demonstrated by pre-surgical [68Ga]Ga-DOTANOC PET/CT and diagnostic conventional imaging were grouped according to the tumour grade assessment method: histology on the whole excised primary lesion (HS) or biopsy (BS). First-order and second-order radiomic features (RFs) were computed from SUV maps for the whole tumour volume on HS. The RFs showing the lowest p-values and the highest Area Under the Curve (AUC) were selected. Three radiomic models were assessed: A (trained on HS, validated on BS), B (trained on BS, validated on HS), C (using the cross-validation on the whole dataset). The second-order Normalized homogeneity and Entropy was the most effective RFs couple predicting G2 and G1. The best performance was achieved by model A (test AUC=0.90, sensitivity=0.88, specificity=0.89), followed by model C (median test AUC=0.87, sensitivity=0.83, specificity=0.82). Model B performed worse. Using HS to train a radiomic model leads to the best prediction, although a “hybrid” (HS+BS) population performs better than biopsy-only. The non-invasive prediction of panNET grading may be especially useful in lesions not amenable to biopsy while [68Ga]Ga-DOTANOC heterogeneity might recommend FDG PET/CT.

A [68Ga]Ga-DOTANOC PET/CT radiomic model for non-invasive prediction of tumour grade in pancreatic neuroendocrine tumours

Alessandro Bevilacqua;Diletta Calabrò;Silvia Malavasi;Claudio Ricci;Riccardo Casadei;Davide Campana;Stefano Fanti;Valentina Ambrosini
2021

Abstract

Predicting grade 1 (G1) and 2 (G2) primary pancreatic neuroendocrine tumour (panNET) is crucial to foresee panNET clinical behaviour. 51 patients with G1-G2 primary panNET demonstrated by pre-surgical [68Ga]Ga-DOTANOC PET/CT and diagnostic conventional imaging were grouped according to the tumour grade assessment method: histology on the whole excised primary lesion (HS) or biopsy (BS). First-order and second-order radiomic features (RFs) were computed from SUV maps for the whole tumour volume on HS. The RFs showing the lowest p-values and the highest Area Under the Curve (AUC) were selected. Three radiomic models were assessed: A (trained on HS, validated on BS), B (trained on BS, validated on HS), C (using the cross-validation on the whole dataset). The second-order Normalized homogeneity and Entropy was the most effective RFs couple predicting G2 and G1. The best performance was achieved by model A (test AUC=0.90, sensitivity=0.88, specificity=0.89), followed by model C (median test AUC=0.87, sensitivity=0.83, specificity=0.82). Model B performed worse. Using HS to train a radiomic model leads to the best prediction, although a “hybrid” (HS+BS) population performs better than biopsy-only. The non-invasive prediction of panNET grading may be especially useful in lesions not amenable to biopsy while [68Ga]Ga-DOTANOC heterogeneity might recommend FDG PET/CT.
Alessandro Bevilacqua, Diletta Calabrò, Silvia Malavasi, Claudio Ricci, Riccardo Casadei, Davide Campana, Serena Baiocco, Stefano Fanti, Valentina Ambrosini
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/820151
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