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: CD8 + T cell in pancreatic ductal adenocarcinoma (PDAC) is closely related to the prognosis and treatment response of patients. Accurate preoperative CD8 + T-cell expression can better identify the population benefitting from immunotherapy. Purpose: To develop and validate a machine learning classifier based on noncontrast magnetic resonance imaging (MRI) for the preoperative prediction of CD8 + T-cell expression in patients with PDAC. Study Type: Retrospective cohort study. Population: Overall, 114 patients with PDAC undergoing MR scan and surgical resection; 97 and 47 patients in the training and validation cohorts. Field Strength/Sequence/3 T: Breath-hold single-shot fast-spin echo T2-weighted sequence and noncontrast T1-weighted fat-suppressed sequences. Assessment: CD8 + T-cell expression was quantified using immunohistochemistry. For each patient, 2232 radiomics features were extracted from noncontrast T1-and T2-weighted images and reduced using the Wilcoxon rank-sum test and least absolute shrinkage and selection operator method. Linear discriminative analysis was used to construct radiomics and mixed models. Model performance was determined by its discriminative ability, calibration, and clinical utility. Statistical Tests: Kaplan-Meier estimates, Student's t-test, the Kruskal-Wallis H test, and the chi-square test, receiver operating characteristic curve, and decision curve analysis. Results: A log-rank test showed that the survival duration in the CD8-high group (25.51 months) was significantly longer than that in the CD8-low group (22.92 months). The mixed model included all MRI characteristics and 13 selected radiomics features, and the area under the curve (AUC) was 0.89 (95% confidence interval [CI], 0.77-0.92) and 0.69 (95% CI, 0.53-0.82) in the training and validation cohorts. The radiomics model included 13 radiomics features, which showed good discrimination in the training cohort (AUC, 0.85; 95% CI, 0.77-0.92) and the validation cohort (AUC, 0.76; 95% CI, 0.61-0.87). Data Conclusions: This study developed a noncontrast MRI-based radiomics model that can preoperatively determine CD8 + T-cell expression in patients with PDAC and potentially immunotherapy planning.
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