Literature DB >> 32808056

Magnetic resonance imaging radiomic analysis can preoperatively predict G1 and G2/3 grades in patients with NF-pNETs.

Yun Bian1, Jing Li1, Kai Cao1, Xu Fang1, Hui Jiang2, Chao Ma1, Gang Jin3, Jianping Lu1, Li Wang4.   

Abstract

PURPOSE: We aimed to explore the relationship between the magnetic resonance imaging (MRI) radiomic score (rad-score) and the grades of non-functioning pancreatic neuroendocrine tumors (NF-pNETs) and evaluate the potential of the calculated MRI rad-score to differentiate grade 1 from grade 2/3 NF-pNETs.
METHODS: This retrospective study assessed 157 patients with surgically resected, pathologically confirmed NF-pNETs who underwent magnetic resonance scans from November 2012 to December 2019. Radiomic features were extracted from arterial and portal venous MRI. The least absolute shrinkage and selection operator method were used to select the features. Multivariate logistic regression models were used to analyze the association between the MRI rad-score and NF-pNET grades. The MRI rad-score performance was assessed based on its discriminative ability and clinical usefulness.
RESULTS: The MRI rad-score, which consisted of seven selected features, was significantly associated with the NF-pNET grades. Every 1-point increase in the rad-score was associated with a 35% increased risk of grade 2/3 disease. The score also showed high accuracy (area under the curve = 0.775). The best cut-off point for maximal sensitivity and specificity was at 0.41. In the decision curves, when the threshold probability was higher than 0.3, the rad-score used in this study to distinguish grades 1 and 2/3 NF-pNETs offered more benefits than the use of a treat-all-patients or a treat-none scheme.
CONCLUSIONS: The MRI rad-score showed a significant association with the grades of NF-pNETs. Thus, it may be used as a valuable non-invasive tool for differential NF-pNET grading.

Entities:  

Keywords:  Magnetic resonance imaging; Neoplasm grading; Non-functioning pancreatic endocrine tumor; Pancreatic neoplasms; Radiomics

Mesh:

Year:  2020        PMID: 32808056     DOI: 10.1007/s00261-020-02706-0

Source DB:  PubMed          Journal:  Abdom Radiol (NY)


  32 in total

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