Noncontrast Radiomics Approach for Predicting Grades of Nonfunctional Pancreatic Neuroendocrine Tumors

Abstract

Background Endoscopic ultrasound‐guided fine‐needle aspiration is associated with the accurate determination of tumor grade. However, because it is an invasive procedure there is a need to explore alternative noninvasive procedures. Purpose To develop and validate a noncontrast radiomics model for the preoperative prediction of nonfunctional pancreatic neuroendocrine tumor (NF‐pNET) grade (G). Study Type Retrospective, single‐center study. Subjects Patients with pathologically confirmed PNETs (139) were included. Field Strength/Sequence 3T/breath‐hold single‐shot fast‐spin echo T2‐weighted sequence and unenhanced and dynamic contrast‐enhanced T1‐weighted fat‐suppressed sequences. Assessment Tumor features on contrast MR images were evaluated by three board‐certified abdominal radiologists. Statistical Tests Multivariable logistic regression analysis was used to develop the clinical model. The least absolute shrinkage and selection operator method and linear discriminative analysis (LDA) were used to select the features and to construct a radiomics model. The performance of the models was assessed using the training cohort (97 patients) and the validation cohort (42 patients), and decision curve analysis (DCA) was applied for clinical use. Results The clinical model included 14 imaging features, and the corresponding area under the curve (AUC) was 0.769 (95% confidence interval [CI], 0.675–0.863) in the training cohort and 0.729 (95% CI, 0.568–0.890) in the validation cohort. The LDA included 14 selected radiomics features that showed good discrimination—in the training cohort (AUC, 0.851; 95% CI, 0.758–0.916) and the validation cohort (AUC, 0.736; 95% CI, 0.518–0.874). In the decision curves, if the threshold probability was 0.17–0.84, using the radiomics score to distinguish NF‐pNET G1 and G2/3, offered more benefit than did the use of a treat‐all‐patients or treat‐none scheme. Data Conclusion The developed radiomics model using noncontrast MRI could help differentiate G1 and G2/3 tumors, to make the clinical decision, and screen pNETs grade. Level of Evidence 4 Technical Efficacy Stage 2 J. Magn. Reson. Imaging 2020;52:1124–1136.

Radiomics in Oncology, Part 1: Technical Principles and Gastrointestinal Application in CT and MRI

“…They demonstrated that the combined model was extremely promising to differentiate PNET G1 from G2/G3 with AUC 0.97 in training and 0.90 in validation cohort. In addition, Bian and colleagues [ 73 ] performed a radiomic model to evaluate preoperative tumor grading in patients affected by non-functional PNETs on 3T MRI and confirmed promising radiomic results. They visually evaluated non-functional PNETs for radiological findings used for the clinical score and then selected a volume of interest (VOI) of the lesion to extract radiomic features.…”

Radiomic nomogram based on MRI to predict grade of branching type intraductal papillary mucinous neoplasms of the pancreas: a multicenter study

“…Therefore, we hypothesized that the tumor biology and heterogeneity were better represented by high-order features. Previous studies recognized the value of assessing pathological features among radiomic features, such as in nonfunctional pancreatic neuroendocrine tumors, soft-tissue masses, and rectal cancer [36][37][38]. However, it remains challenging to associate a single radiomic feature with the complex biological processes of tumors.…”

Applications of artificial intelligence in pancreatic and biliary diseases

“…Bian et al . assessed the radiomic features of non‐contrast MRI images from 139 PNET patients for differentiation between G1 and G2/3 tumors, 34 and a model that combined the radiomic signature and 14 imaging features achieved an AUC of 0.729 in the validation set.…”