Preoperative prediction of pancreatic neuroendocrine tumor grade based on 68Ga-DOTATATE PET/CT.

OBJECTIVE: To establish a prediction model for preoperatively predicting grade 1 and grade 2/3 tumors in patients with pancreatic neuroendocrine tumors (PNETs) based on 68Ga-DOTATATE PET/CT. METHODS: Clinical data of 41 patients with PNETs were included in this study. According to the pathological results, they were divided into grade 1 and grade 2/3. 68Ga-DOTATATE PET/CT images were collected within one month before surgery. The clinical risk factors and significant radiological features were filtered, and a clinical predictive model based on these clinical and radiological features was established. 3D slicer was used to extracted 107 radiomic features from the region of interest (ROI) of 68Ga-dotata PET/CT images. The Pearson correlation coefficient (PCC), recursive feature elimination (REF) based five-fold cross validation were adopted for the radiomic feature selection, and a radiomic score was computed subsequently. The comprehensive model combining the clinical risk factors and the rad-score was established as well as the nomogram. The performance of above clinical model and comprehensive model were evaluated and compared. RESULTS: Adjacent organ invasion, N staging, and M staging were the risk factors for PNET grading (p < 0.05). 12 optimal radiomic features (3 PET radiomic features, 9 CT radiomic features) were screen out. The clinical predictive model achieved an area under the curve (AUC) of 0.785. The comprehensive model has better predictive performance (AUC = 0.953). CONCLUSION: We proposed a comprehensive nomogram model based on 68Ga-DOTATATE PET/CT to predict grade 1 and grade 2/3 of PNETs and assist personalized clinical diagnosis and treatment plans for patients with PNETs.

Assessment of Pulmonary Arteries Hemodynamics and Its Relationship With Cardiac Remodeling and Myocardial Fibrosis in Athletes With Four-Dimensional Flow MRI.

BACKGROUND: Exercise-induced cardiac remodeling (CR) and myocardial fibrosis (MF) can increase cardiovascular risk in athletes. Early detection of pulmonary arterial hemodynamics parameters among athletes may be beneficial in optimizing the frequency of clinical follow-ups. PURPOSE: To analyze the hemodynamics of pulmonary arteries and its relationship with CR and MF in athletes using four-dimensional (4D) flow MRI. STUDY TYPE: Prospective. POPULATION: One hundred twenty-one athletes (median age, 24 years; mean exercise per week 10 hours, for mean of 5 years) and twenty-one sedentary healthy controls (median age, 25 years; exercise per week <3 hours, irregular pattern). FIELD STRENGTH/SEQUENCE: True fast imaging with steady state free precession, time-resolved 3D Cartesian phase-contrast, and phase sensitive inversion recovery late gadolinium enhancement sequences at 3.0 T. ASSESSMENT: CR was defined as any cardiac parameters exceeding the 99th percentile upper reference limits, encompassing ventricular function, bi-atrium and bi-ventricle diameters, and ventricular wall thickness. MF was visually evaluated by three independent radiologists. 4D flow parameters were assessed in the main, right, and left pulmonary arteries (MPA, RPA, and LPA, respectively) and compared between different groups. Four machine learning (ML) models were developed to differentiate between athletes with and without CR and/or MF. STATISTICAL TESTS: Univariate analysis was used to compare groups. Area under the receiver operating characteristic curve (AUC) was used to assess the performance of the ML models. RESULTS: Athletes had significantly higher WSSmax in the MPA, RPA, and LPA than controls. Athletes with CR and/or MF (N = 30) had significantly lower RPmax from MPA to RPA than those without (N = 91). Among the ML models, the gradient boosting machine model had the highest performance, with an AUC of 0.90. CONCLUSION: The pulmonary arterial hemodynamics parameters could differentiate CR and/or MF in athletes, which may be potential to assist in optimizing frequency of follow-up. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 2.

Effects of Urban Vibrancy on an Urban Eco-Environment: Case Study on Wuhan City.

In the context of rapid urbanisation and an emerging need for a healthy urban environment, revitalising urban spaces and its effects on the urban eco-environment in Chinese cities have attracted widespread attention. This study assessed urban vibrancy from the dimensions of density, accessibility, liveability, diversity, and human activity, with various indicators using an adjusted spatial TOPSIS (technique for order preference by similarity to an ideal solution) method. The study also explored the effects of urban vibrancy on the urban eco-environment by interpreting PM 2.5 and land surface temperature using "big" and "dynamic" data, such as those from mobile and social network data. Thereafter, spatial modelling was performed to investigate the influence of urban vibrancy on air pollution and temperature with inverted and extracted remote sensing data. This process identified spatial heterogeneity and spatial autocorrelation. The majority of the dimensions, such as density, accessibility, liveability, and diversity, are negatively correlated with PM 2.5, thereby indicating that the advancement of urban vibrancy in these dimensions potentially improves air quality. Conversely, improved accessibility increases the surface temperature in most of the districts, and large-scale infrastructure construction generally contributes to the increase. Diversity and human activity appear to have a cooling effect. In the future, applying spatial heterogeneity is advised to assess urban vibrancy and its effect on the urban eco-environment, to provide valuable references for spatial urban planning, improve public health and human wellbeing, and ensure sustainable urban development.