Background and Purpose:Accurate differentiation of pancreatic ductal adenocarcinoma(PDAC)from mass-forming chronic pancreatitis(MFCP)is clinically significant.The application of dual-layer spectral detector CT(DLCT)in pancreas has been explored.This study aimed to investigate the value of DLCT in distinguishing resectable PDAC from MFCP.Methods:We retrospectively collected data of 33 patients with resectable PDAC and 19 patients with MFCP admitted to Fudan University Shanghai Cancer Center from September 1,2021 to May 31,2023.Prior to surgery,patients underwent enhanced DLCT scans,including arterial phase(AP),parenchymal phase(PP)and venous phase(VP).DLCT quantitative parameters,including attenuation enhancement fraction(AEF),lesion-to-parenchyma ratio(LPR)and iodine enhancement fraction(IEF)were calculated.Difference analysis was conducted using independent sample t-test or chi-square test.Univariate and multivariate analyses were performed using binary logistic regression.Receiver operating characteristic(ROC)curves were used for performance evaluation.P＜0.05 was considered statistically significant.Results:Statistically significant differences were observed between PDAC and MFCP in AEF_AP/PP,LPR40_VP,IEF_PP/VP,carbohydrate antigen 19-9(CA19-9)and double-duct sign(all P＜0.05).The spectral combined model composed of LPR40_VP and IEF_PP/VP exhibited the best discriminatory efficacy,surpassing CA19-9,double-duct sign and AEF_AP/PP(all P＜0.05).The combined model demonstrated an area under curve(AUC)of 0.841,sensitivity of 90%,specificity of 73%,and accuracy of 79%.Conclusion:DLCT has certain potential in differentiating resectable PDAC from MFCP.Spectral quantitative parameters can complement CA19-9 and outcome shortcomings of conventional CT in distinguishing resectable PDAC from MFCP.

Objective:To investigate the value of CT-radiomics based machine learning model in predicting the abundance of tumor infiltrating CD8 +T cells and the prognosis of pancreatic cancer patients. Methods:A total of 150 pancreatic cancer patients who underwent surgical excision and confirmed by pathology from Fudan University Shanghai Cancer Center between December 2011 and January 2017 were retrospectively enrolled. The patients were randomly divided into the training set ( n=105) and the validation set ( n=45) in a 7∶3 ratio with simple random sampling. The immunohistochemical method was used to assess the abundance of tumor infiltrating CD8 +T cells, and the patients were then divided into high infiltrating group ( n=75) and low infiltrating group ( n=75) according to the median. The prognosis between the 2 groups was evaluated using Kaplan-Meier method and log-rank test. Radiomic features were extracted from preoperative venous-phase enhanced CT images in the training set. The Wilcoxon test, the max-relevance and min-redundancy algorithm were used to select the optimal feature set. Three supervised machine learning models (decision tree, random forest and extra tree) were established based on the optimal feature set to predict the abundance of tumor infiltrating CD8 +T cells. Performance of above-mentioned models to predict the abundance of tumor infiltrating CD8 +T cells in pancreatic cancer was tested in the validation set. The evaluation parameters included area under the receiver operating characteristic curve (AUC), F1-score, accuracy, precision and recall. Results:The median overall survival time of patients in high infiltrating group and low infiltrating group were 875 days and 529 days, respectively (χ2=11.53, P<0.001). The optimal feature set consisted of 10 radiomic features in training set. In the validation set, the decision tree, random forest and extra tree model showed the AUC of 0.620, 0.704 and 0.745, respectively; corresponding to a F1-score of 0.457, 0.667 and 0.744, the accuracy of 57.8%, 68.9% and 75.6%, the precision of 66.7%, 73.7% and 80.0%, the recall of 34.8%, 60.9% and 69.6%. Conclusions:Pancreatic cancer patients with high tumor infiltrating CD8 +T cells have better prognosis than those with low tumor infiltrating CD8 +T cells. The radiomics-based extra tree model is valuable in predicting the CD8 +T cells infiltrating level in pancreatic cancer.

Objective To summarize and evaluate the characteristics of current recurrent spontaneous abortion (RSA) animal models at home and abroad, and to provide reference and guidance for the standardized preparation of RSA models. Methods"Recurrent spontaneous abortion" and "animal model" were used as co-keywords in CNKI, Wanfang, VIP, PubMed and Web of Science databases to search the RSA animal experimental literature, covering the period up to January 20, 2024, and a total of 1 411 articles were collected. The analysis focused on construction methods and essential elements of RSA animal models, the modeling process and result evaluation, as well as the application of these models in pharmacological and pharmacodynamic research. An Excel table was established for systematic analysis and discussion. Results A total of 138 experimental studies were obtained after screening. In constructing RSA animal models, immunological models were the most widely used in Western medicine (96.92%), with the Clark model being the main one (92.31%). In traditional Chinese medicine (TCM) models, 70.00% were kidney deficiency-luteal inhibition-syndrome combination models, 20.00% were kidney deficiency and blood stasis models, and 10.00% were deficiency-heat syndrome models. Most animals were selected at 6-8 weeks (33.86%) and 8 weeks (32.28%) of age. The majority of animals were paired for mating at 18:00 on the day of cage pairing. In 81.03% of literatures, vaginal plugs were checked once the following morning, with 8:00 being the most common time (17.02%). The most commonly used drug administration cycle was 14 days of continuous gavage after pregnancy. Among the tested drugs, Western drugs were mainly protein-based (29.17%), while TCM drugs were mainly TCM decoction (81.11%). The most frequently used methods for detecting indicators included visual observation of embryos (22.54%), western blot (15.96%), PCR (13.58%), ELISA (12.91%), HE staining (10.80%) and immunohistochemistry (9.39%). Conclusion The etiology of RSA is complex, and corresponding animal models should be established based on different etiologies. Clark model is commonly used in the construction of Western medicine model, while the kidney deficiency-luteal inhibition-syndrome combination model is predominant in TCM. RSA animal model is widely used in related research, but systematic evaluation needs to be strengthened.