Yun Bian1, Zengrui Zhao2, Hui Jiang3, Xu Fang1, Jing Li1, Kai Cao1, Chao Ma1, Shiwei Guo4, Li Wang1, Gang Jin4, Jianping Lu1, Jun Xu2. 1. Department of Radiology, Changhai Hospital, Shanghai, China. 2. Jiangsu Key Laboratory of Big Data Analysis Technique, Nanjing University of Information Science and Technology, Nanjing, China. 3. Department of Pathology, Changhai Hospital, Shanghai, China. 4. Department of Pancreatic Surgery, Changhai Hospital, Shanghai, China.
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.
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.
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