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. 2022 Sep;127(9):928-938.
doi: 10.1007/s11547-022-01529-x. Epub 2022 Aug 2.

Gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs): a radiomic model to predict tumor grade

Giuditta Chiti  1 , Giulia Grazzini  2   3 , Federica Flammia  1 , Benedetta Matteuzzi  1 , Paolo Tortoli  4 , Silvia Bettarini  4 , Elisa Pasqualini  5 , Vincenza Granata  6 , Simone Busoni  4 , Luca Messserini  7 , Silvia Pradella  1   8 , Daniela Massi  5 , Vittorio Miele  1
Affiliations

Gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs): a radiomic model to predict tumor grade

Giuditta Chiti et al. Radiol Med. 2022 Sep.

Abstract

Purpose: The aim of this single-center retrospective study is to assess whether contrast-enhanced computed tomography (CECT) radiomics analysis is predictive of gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs) grade based on the 2019 World Health Organization (WHO) classification and to establish a tumor grade (G) prediction model.

Material and methods: Preoperative CECT images of 78 patients with GEP-NENs were retrospectively reviewed and divided in two groups (G1-G2 in class 0, G3-NEC in class 1). A total of 107 radiomics features were extracted from each neoplasm ROI in CT arterial and venous phases acquisitions with 3DSlicer. Mann-Whitney test and LASSO regression method were performed in R for feature selection and feature reduction, in order to build the radiomic-based predictive model. The model was developed for a training cohort (75% of the total) and validated on the independent validation cohort (25%). ROC curves and AUC values were generated on training and validation cohorts.

Results: 40 and 24 features, for arterial phase and venous phase, respectively, were found to be significant in class distinction. From the LASSO regression 3 and 2 features, for arterial phase and venous phase, respectively, were identified as suitable for groups classification and used to build the tumor grade radiomic-based prediction model. The prediction of the arterial model resulted in AUC values of 0.84 (95% CI 0.72-0.97) and 0.82 (95% CI 0.62-1) for the training cohort and validation cohort, respectively, while the prediction of the venous model yielded AUC values of 0.7877 (95% CI 0.6416-0.9338) and 0.6813 (95% CI 0.3933-0.9693) for the training cohort and validation cohort, respectively.

Conclusions: CT-radiomics analysis may aid in differentiating the histological grade for GEP-NENs.

Keywords: Contrast-enhanced computed tomography (CECT); Gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs); Radiomic; Tumor grade.

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