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Background: To establish and validate a prediction model for pancreatic neuroendocrine neoplasms (pNENs) recurrence after radical surgery with preoperative computed tomography (CT) images. Methods:We retrospectively collected data from 74 patients with pathologically confirmed pNENs (internal group: 56 patients, Hospital I; external validation group: 18 patients, Hospital II). Using the internal group, models were trained with CT findings evaluated by radiologists, radiomics, and deep learning radiomics (DLR) to predict 5-year pNEN recurrence. Radiomics and DLR models were established for arterial (A), venous (V), and arterial and venous (A&V) contrast phases. The model with the optimal performance was further combined with clinical information, and all patients were divided into high-and low-risk groups to analyze survival with the Kaplan-Meier method.Results: In the internal group, the areas under the curves (AUCs) of DLR-A, DLR-V, and DLR-A&V models were 0.80, 0.58, and 0.72, respectively. The corresponding radiomics AUCs were 0.74, 0.68, and 0.70.The AUC of the CT findings model was 0.53. The DLR-A model represented the optimum; added clinical information improved the AUC from 0.80 to 0.83. In the validation group, the AUCs of DLR-A, DLR-V, and DLR-A&V models were 0.77, 0.48, and 0.64, respectively, and those of radiomics-A, radiomics-V, and radiomics-A&V models were 0.56, 0.52, and 0.56, respectively. The AUC of the CT findings model was 0.52. In the validation group, the comparison between the DLR-A and the random models showed a trend of significant difference (P=0.058). Recurrence-free survival differed significantly between high-and low-risk groups (P=0.003).Conclusions: Using DLR, we successfully established a preoperative recurrence prediction model for pNEN patients after radical surgery. This allows a risk evaluation of pNEN recurrence, optimizing clinical decision-making.

We aimed to provide ideas for clinicians, especially radiologists, for the diagnosis of multiple endocrine neoplasia (MEN) syndromes.Background: MEN syndromes include MEN1, MEN2, and MEN4 and usually involve 2 or more endocrine tumors. The MEN syndromes are a group of euchromatic dominant genetic diseases, and the main genes involved include MEN1 (MEN1), RET (MEN2), and CDKN1B (MEN4).Methods: In this article, involving 8 cases (4 cases of MEN1, 2 cases of MEN2A, 1 case of MEN2B, 1 case of MEN4) from our center, we introduced the disease spectrum, clinical manifestations (especially imaging findings), and related genes involved in each type of MEN syndromes. We also discussed the differential diagnosis between MEN and sporadic tumors and emphasized that MEN should be screened and the relevant required examinations. Conclusions:Considering that MEN syndromes involve multiple endocrine gland tumors and nonendocrine organ diseases, it is very important to identify potential patients early and perform multiple examinations on them, including biochemical and multitype, and multisite imaging examinations according to the disease spectrum of each type. Considering that this is a group of genetic diseases, both interviewing patients about their family history and genetic testing are also very important. Only in this way can a comprehensive and accurate diagnosis be made, enabling patients to receive appropriate treatment and improve their prognosis.

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Background Patients with chronic pancreatitis often have irreversible pancreatic insufficiency before a clinical diagnosis. Pancreatic cancer is a fatal malignant tumor in the advanced stages. Patients having high risk of pancreatic diseases must be screened early to obtain better outcomes using new imaging modalities. Therefore, this study aimed to investigate the reproducibility of tomoelastography measurements for assessing pancreatic stiffness and fluidity and the variance among healthy volunteers. Methods Forty-seven healthy volunteers were prospectively enrolled and underwent two tomoelastography examinations at a mean interval of 7 days. Two radiologists blindly and independently measured the pancreatic stiffness and fluidity at the first examination to determine the reproducibility between readers. One radiologist measured the adjacent pancreatic slice at the first examination to determine the reproducibility among slices and measured the pancreas at the second examination to determine short-term repeatability. The stiffness and fluidity of the pancreatic head, body, and tail were compared to determine anatomical differences. The pancreatic stiffness and fluidity were compared based on sex, age, and body mass index (BMI). Results Bland–Altman analyses (all P > 0.05) and intraclass correlation coefficients (all >0.9) indicated near perfect reproducibility among readers, slices, and examinations at short intervals. Neither stiffness (P = 0.477) nor fluidity (P = 0.368) differed among the pancreatic anatomical regions. The mean pancreatic stiffness was 1.45 ± 0.09 m/s; the mean pancreatic fluidity was 0.83 ± 0.06 rad. Stiffness and fluidity did not differ by sex, age, or BMI. Conclusion Tomoelastography is a promising and reproducible tool for assessing pancreatic stiffness and fluidity in healthy volunteers.