Noncontrast Radiomics Approach for Predicting Grades of Nonfunctional Pancreatic Neuroendocrine Tumors

• Published in: Journal of magnetic resonance imaging Vol. 52; no. 4; pp. 1124 - 1136
• Main Authors: Bian, Yun, Zhao, Zengrui, Jiang, Hui, Fang, Xu, Li, Jing, Cao, Kai, Ma, Chao, Guo, Shiwei, Wang, Li, Jin, Gang, Lu, Jianping, Xu, Jun
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.10.2020; Wiley Subscription Services, Inc
• Subjects: Area Under Curve; Auditory discrimination; Cohort Studies; Confidence intervals; Decision analysis; Decision making; Field strength; Humans; Image contrast; Magnetic Resonance Imaging; Medical imaging; Neoplasm Grading; Neuroendocrine tumors; Pancreas; Pancreatic cancer; Pancreatic Neoplasms - diagnostic imaging; Radiomics; Regression analysis; Retrospective Studies; Statistical analysis; Statistical tests; Training; Tumors; Ultrasound; radiomics; magnetic resonance imaging; Neoplasm Grading; pancreas; neuroendocrine tumors
• ISSN: 1053-1807; 1522-2586; 1522-2586
• DOI: 10.1002/jmri.27176

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.