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

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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.

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Acknowledgements

Huiying Medical Technology (Beijing) Co., Ltd, Beijing, China

Funding

This work was supported in part by National Science Foundation for Scientists of China (81871352), National Science Foundation for Young Scientists of China (81701689, 81601468), 63-class General Financial Grant from the China Postdoctoral Science Foundation (2018M633714), Key Junior College of National Clinical of China, Shanghai Technology Innovation Project 2017 on Clinical Medicine (17411952200), and Project of Precision Medical Transformation Application of NMMU (2017JZ42).

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Authors and Affiliations

  1. Department of Radiology, Changhai Hospital, The Navy Military Medical University, 168 Changhai Road, Shanghai, 200433, China

    Yun Bian, Jing Li, Kai Cao, Xu Fang, Chao Ma & Jianping Lu

  2. Department of Pathology, Changhai Hospital, The Navy Military Medical University, Shanghai, China

    Hui Jiang & Li Wang

  3. Department of Pancreatic Surgery, Changhai Hospital, The Navy Military Medical University, Shanghai, China

    Gang Jin

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  1. Yun Bian
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Bian, Y., Li, J., Cao, K. et al. Magnetic resonance imaging radiomic analysis can preoperatively predict G1 and G2/3 grades in patients with NF-pNETs. Abdom Radiol 46, 667–680 (2021). https://doi.org/10.1007/s00261-020-02706-0

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