Objective:To construct a machine-learning model for predicting the progression of pancreatic cystic lesions (PCLs) based on clinical and CT features, and to evaluate its predictive performance in internal/external testing cohorts.Methods:Baseline clinical and radiological data of 200 PCLs in 177 patients undergoing abdominal thin slice enhanced CT examination at Peking Union Medical College Hospital from July 2014 to December 2022 were retrospectively collected. PCLs were divided into progressive and non-progressive groups according to whether the signs indicated for surgery by the guidelines of the European study group on PCLs were present during three-year follow-up. 200 PCLs were randomly divided into training (150 PCLs) and internal testing cohorts (50 PCLs) at the ratio of 1∶3. 15 PCLs in 14 patients at Jinling Affiliated Hospital of Medical School of Nanjing University from October 2011 to May 2020 were enrolled as external testing cohort. The clinical and CT radiological features were recorded. Multiple feature selection methods and machine-learning models were implemented and combined to identify the optimal machine-learning model based on the 10-fold cross-validation method. Receiver operating characteristics (ROC) curve was drawn and area under curve (AUC) was calculated. The model with the highest AUC was determined as the optimal model. The optimal model's predictive performance was evaluated on testing cohort by calculating AUC, sensitivity, specificity and accuracy. Permutation importance was used to assess the importance of optimal model features. Calibration curves of the optimal model were established to evaluate the model's clinical applicability by Hosmer-Lemeshow test.Results:In training and internal testing cohorts, the progressive and non-progressive groups were significantly different on history of pancreatitis, lesions size, main pancreatic duct diameter and dilation, thick cyst wall, presence of septation and thick septation (all P value <0.05) In internal testing cohort, the two groups were significantly different on gender, lesion calcification and pancreatic atrophy (all P value <0.05). In external testing cohort, the two groups were significantly different on lesions size and pancreatic duct dilation (both P<0.05). The support vector machine (SVM) model based on five features selected by F test (lesion size, thick cyst wall, history of pancreatitis, main pancreatic duct diameter and dilation) achieved the highest AUC of 0.899 during cross-validation. SVM model for predicting the progression of PCLs demonstrated an AUC of 0.909, sensitivity of 82.4%, specificity of 72.7%, and accuracy of 76.0% in the internal testing cohort, and 0.944, 100%, 77.8%, and 86.7% in the external testing cohort. Calibration curved showed that the predicted probability by the model was comparable to the real progression of PCLs. Hosmer-Lemeshow goodness-of-fit test affirmed the model's consistency with actual PCLs progression in testing cohorts. Conclusions:The SVM model based on clinical and CT features can help doctors predict the PCLs progression within three-year follow-up, thus achieving efficient patient management and rational allocation of medical resource.

To establish a medical imaging teacher competency model and evaluate its application value in group teaching for undergraduates.

Pancreatic cancer, one of the most lethal cancers in the world, has been increasing in incidence and mortality year by year, and the overall prognosis of patients is poor. Early detection and effective treatment are crucial for improving the prognosis and survival rates of pancreatic cancer patients. Unlike traditional imaging, emerging molecular imaging can visualize the abnormalities at the molecular or cellular level in the process of tumor development. At present, multimodality molecular imaging that integrates multiple imaging methods to achieve complementary advantages and multifunctional nanoplatforms with integrated diagnosis and treatment functions have become research hotspots in the field of molecular imaging. Remarkable progress has been made in preclinical research concerning magnetic-optical multimodality molecular imaging probes and their derived multifunctional nanoplatforms, which provides new ideas for early detection, accurate treatment and efficacy evaluation of pancreatic cancer.

Objective To compare the acute effect of high-intensity interval training(HIIT)and moder-ate-intensity continuous training(MICT)on ectopic lipid levels in overweight and obese youth.Methods Twenty obese or overweight subjects,aged 24.15±1.98 years,participated in two crossover random-ized own-control trials of HIIT and MICT.In HIIT,participants performed high-intensity cycling at 85%VO2max for 9 sets of 2 minutes,interspersed with low-intensity cycling at 25%VO2max for 10 sets of 2 minutes each and the session started and ended with low-intensity cycling at 25%VO2max.Howev-er,in MICT,all participants cycled continuously at 50%VO2max for 60 minutes,maintaining a pedal-ing speed of 50-55 rpm,with a 7-day interval between the two interventions.Before,as well as im-mediately,60 and 120 minutes after exercise intervention,all subjects underwent magnetic resonance imaging(MRI)scans to observe the fat fraction(FF)and spin relaxation rate R2* of the rectus femo-ris,biceps femoris and liver.Results Rectus femoris FF decreased significantly immediately after HIIT and MICT(P<0.05),without significant differences.Moreover,sixty minutes after MICT,rectus femoris FF returned to pre-exercise levels,while 120 minutes after HIIT,the values restored to the pre-exer-cise levels.However,no significant changes were found in the biceps femoris and liver FF before and after the two exercise interventions(P>0.05).Meanwhile,the rectus femoris R2* was significantly lower in both the HIIT and MICT groups immediately after exercise(P<0.05)and remained significantly low-er in both groups 120 minutes after the exercise(P<0.05),with no significant differences between the two groups.Biceps femoris and liver R2* were significantly higher in both groups immediately after the exercise intervention(P<0.05).Liver R2* returned to pre-exercise levels 120 minutes after HIIT group,but remained significantly lower than pre-intervention levels after MICT(P<0.05).Conclusion Both acute HIIT and MICT are effective in reducing intramuscular fat in the working muscles of over-weight and obese adults but have no significant effect on liver fat.Acute HIIT and MICT show similar fat-burning effects,but a single bout of exercise proves more effective for fat loss in the active mus-cles compared to the antagonist muscles.

As the detection rate of pancreatic cystic lesions(PCL)increases,artificial intelligence(AI)has made breakthroughs in the imaging workflow of PCL,including image post-processing,lesion detection,segmentation,diagnosis and differential diagnosis.AI-based image post-processing can optimize the quality of medical images and AI-assisted models for lesion detection,segmentation,diagnosis and differential diagnosis significantly enhance the work efficiency of radiologists.This article reviews the application progress of AI in PCL imaging and provides prospects for future research directions.

Immune checkpoint inhibitors have shown remarkable benefits in the treatment of solid tumors,while the occurrence of atypical response patterns and immune-related adverse events during treatment challenges the accuracy of therapeutic evaluation.Medical imaging is crucial for the evaluation of immunotherapy.It enables the assessment of treatment efficacy via both morphological and functional ways and offers unique a predictive val-ue when being combined with artificial intelligence.Here we review the recent research progress in imaging-based evaluation of solid tumors treated with immune checkpoint inhibitors.

Objective:To explore application value of improving quality of the low dose pancreatic CT images by using deep learning reconstruction (DLR).Methods:From August to December 2020, 68 patients who underwent contrast-enhanced pancreatic CT were prospectively collected in Peking Union Medical College Hospital. All patients were randomly divided into routine dose group (34 patients, with tube voltage of 120 kV) and low dose group (34 patients, with tube voltage of 100 kV). All patients underwent non-contrast, arterial phase, parenchymal phase and delay phase scans. The four-phase images of low dose group were reconstructed by using filtered back projection (FBP), hybrid iterative reconstruction (AIDR) and DLR which were marked with LD-FBP, LD-AIDR and LD-DLR, respectively. The four-phase images of routine dose group were reconstructed by using AIDR algorithm which were marked with RD-AIDR. The CT value, image noise (SD), signal to noise ratio (SNR) and contrast to noise ratio (CNR) of pancreas were measured. The ANOVA test was performed in comparison with objective parameters of different reconstruction algorithms, and LSD test was performed in pairwise comparison. The subjective image scores were obtained and were compared using Kruskal-Wallis test.Results:CT value, SD, SNR and CNR of non-contrast, arterial phase, parenchymal phase and delay phase had significant difference among different reconstruction images of routine dose group and low dose group (all P<0.05). The CT value of LD-FBP, LD-AIDR, and LD-DLR images were significantly higher than those of RD-AIDR images in parenchymal phase and delay phase (all P<0.05). There were statistically significant differences in each pairwise comparison of SD and SNR of four phase images (all P<0.05). There were statistically significant differences of CNR among LD-FBP, LD-DLR and RD-AIDR in four phase images (all P<0.05). The CNR of RD-AIDR was better than that of LD-FBP, and CNR of LD-DLR was better than that of RD-AIDR. DLR algorithm improved the SD, SNR and CNR of four phases of pancreatic images. The improvement of SNR was more significant after contrast enhancement, and the improvement of CNR was more significant in the non-contrast and delay phases. Subjective image scores of different reconstruction images were statistically different in four phase images (all P<0.001). Overall image scores of LD-DLR and RD-AIDR had no significant differences in four phase ( Z value of four phases were 1.00, 2.24, 0.45 and 1.34, respectively; P value of four phases were 0.317, 0.025, 0.655 and 0.180, respectively). Conclusion:The DLR technology can decrease radiation dose of pancreatic CT, improve image quality and satisfy diagnostic requirement. The DLR technology can also reduce image noise, improve the SNR and CNR in low dose contrast-enhanced pancreatic CT.

The competency-based medical education has formed a global trend, and puts forward a greater challenge for educational design of resident training. The traditional curriculum cannot meet the goal of competency-based education as the curriculum design is lack of theoretical support. Curriculum design is the core of training content, and serves as a significant contributing factor of training outcome. Based on the six-step approach curriculum design, the theory and practice are integrated to form a curriculum design based on theoretical guidance. Through feedback evaluation, the current curriculum design is continuously improved in order to achieve a higher competency-based training quality. With the 5-year experiences and practice, preliminary reform demonstrates effectiveness. The current study hopes to share the teaching reform experiences of residency training base and provide references for colleagues of medical education.

Objective To evaluate changes in morphology of the cesarean scar and uterus between one and two years after cesarean section using high-resolution, three dimensional T2-weighted sampling perfection with application optimized contrast using different flip angle evolutions Magnetic Resonance Imaging (3D T2w SPACE MRI). Methods This prospective study was performed to investigate morphological changes in the cesarean scars and uterus from one to two years after cesarean section using high-resolution, 3D T2w SPACE MRI. The healthy volunteers having no childbearing history were recruited as the controls. All data were measured by two experienced radiologists. All data with normal distribution between the one-year and two-year groups were compared using a paired-sample t test or independent t test. Results Finally, 46 women took a pelvic MR examination one year after cesarean section, and a subset of 15 completed the same examination again after two years of cesarean section. Both the uterine length and the anterior wall thickness after two years of cesarean section (5.75 ± 0.46 and 1.45 ± 0.35 cm) were significantly greater than those measured at one year (5.33 ± 0.59 and 1.25 ± 0.27 cm) (t = -2.363 and -2.175, P= 0.033 and 0.048). No significant difference was shown in myometrial thickness two years after cesarean section (1.45 ± 0.35 cm) with respect to the control group (1.58 ± 0.21 cm, P = 0.170). Nine women who underwent MRI twice were considered to have scar diverticula one year after cesarean section, and still had diverticula two years after cesarean section. The thickness, height, and width of the uterine scar showed no significant change from one to two years (all P > 0.05). Conclusions 3D T2w SPACE MRI provides overall morphologic details and shows dynamic changes in the scar and the uterus between one and two years after cesarean section. Scar morphology after cesarean section reached relatively stable one year after cesarean section, and uterine morphology was closer to normal two years after cesarean section.

Pancreatic neuroendocrine neoplasms (pNENs) are highly heterogeneous, and the management of pNENs patients can be intractable. To address this challenge, an expert committee was established on behalf of the Chinese Pancreatic Surgery Association, Chinese Society of Surgery, Chinese Medical Association, which consisted of surgical oncologists, gastroenterologists, medical oncologists, endocrinologists, radiologists, pathologists, and nuclear medicine specialists. By reviewing the important issues regarding the diagnosis and treatment of pNENs, the committee concluded evidence-based statements and recommendations in this article, in order to further improve the management of pNENs patients in China.