Differentiation of atypical non-functional pancreatic neuroendocrine tumor and pancreatic ductal adenocarcinoma using CT based radiomics. Issue 117 (August 2019)

• Record Type: Journal Article
• Authors: He, Ming; Liu, Zhenyu; Lin, Yusong; Wan, Jianzhong; Li, Juan; Xu, Kai; Wang, Yun; Jin, Zhengyu; Tian, Jie; Xue, Huadan
• Is Part Of: European journal of radiology. Issue 117(2019)
• Page Start: 102
• Page End: 111
• Publication Date: 2019-08
• Subjects: NF-pNET non-functional pancreatic neuroendocrine tumor -- PDAC pancreatic ductal adenocarcinoma -- LASSO least absolute shrinkage and selection operator -- SVM support vector machine -- RF random forest -- AUC area under the receiver operating characteristic curve -- CI confidential interval; Pancreatic ductal carcinoma -- Neuroendocrine tumor -- Multidetector computed tomography -- Radiomics -- Differential diagnosis; Medical radiology -- Periodicals; Radiology -- Periodicals; Radiologie médicale -- Périodiques; Medical radiology; Periodicals; 616.075705
• DOI: 10.1016/j.ejrad.2019.05.024
• Languages: English
• ISSNs: 0720-048X

Abstract

Abstract: Purpose: To develop and validate an effective model to differentiate NF-pNET from PDAC. Materials and methods: Between July 2014 and December 2017, 147 patients (80 patients with PDAC and 67 patients with atypical NF-pNET) with pathology results and enhanced CT were consecutively enrolled and chronologically divided into primary and validation cohorts. Three models were built to differentiate atypical NF-pNET from PDAC, including a model based on radiomic signature alone, one based on clinicoradiological features alone and one that integrated the two. The diagnostic performance of the three models was estimated and compared with the area under the receiver operating characteristic curve (AUC) in the validation cohort. A nomogram was used to represent the model with the best performance, and the associated calibration was also assessed. Results: In the validation cohort, the AUC for differential diagnosis was 0.884 with the integrated model, which was significantly improved over that of the model based on clinicoradiological features (AUC = 0.775, p value = 0.004) and was comparable to that of the model based on the radiomic signature (AUC = 0.873, p value = 0.512). The nomogram representing the integrated model achieved good discrimination performances in both the primary and validation cohorts, with C-indices of 0.960 and 0.884, respectively. Conclusion: The integrated model outperformed the model based on clinicoradiological features alone and was comparable to the model based on the radiomic signature alone with respect to the differential diagnosis of atypical NF-pNET and PDAC. The nomogram achieved an optimal preoperative, noninvasive differential diagnosis between atypical pNET and PDAC, which can better inform therapeutic choice in clinical practice.