Gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs): a radiomic model to predict tumor grade.
Contrast-enhanced computed tomography (CECT)
Gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs)
Radiomic
Tumor grade
Journal
La Radiologia medica
ISSN: 1826-6983
Titre abrégé: Radiol Med
Pays: Italy
ID NLM: 0177625
Informations de publication
Date de publication:
Sep 2022
Sep 2022
Historique:
received:
08
04
2022
accepted:
12
07
2022
pubmed:
3
8
2022
medline:
28
9
2022
entrez:
2
8
2022
Statut:
ppublish
Résumé
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. 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. 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. CT-radiomics analysis may aid in differentiating the histological grade for GEP-NENs.
Identifiants
pubmed: 35917099
doi: 10.1007/s11547-022-01529-x
pii: 10.1007/s11547-022-01529-x
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
928-938Informations de copyright
© 2022. Italian Society of Medical Radiology.
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