No abstract

Occupational exposure to ionizing radiation from medical practices in China has been collected for a 7 y period between 2010 and 2016 from roughly 220 individual monitoring service providers through the Chinese Registry of Radiation Workers. Statistical dose distributions and characteristic tendencies are presented based on the evaluation in terms of six occupational categories. A reduction can be seen in average annual effective dose for interventional radiology, nuclear medicine, diagnostic radiology, radiotherapy, dental radiology, and others by 52%, 47%, 46%, 34%, 69%, and 31%, respectively, for the 7 y period. More than 94.5% of radiation workers received annual doses less than the public dose limit (1 mSv) in 2016. Workers engaged in nuclear medicine and interventional radiology activities were found to receive relatively more dose than the other fields of practice. Diagnostic radiology makes the dominant contribution of 68% to the collective effective dose of 73,641.3 person mSv received by 211,613 radiation workers in medical practices in 2016. The observation of workers in medical practices receiving well below the recommended occupational dose limit (20 mSv) could be a result of an improvement in radiation protection practices in the medical field in China. However, it is still necessary to control and manage the workplace and radiation workers to avoid unnecessary exposures, in particular for the workers engaged in nuclear medicine and interventional radiology activities.

Given the high prevalence and relapse rates of hepatocellular carcinoma (HCC), an increased capacity for early identification of patients most at risk for post-resection recurrence would help improve patient outcomes and prioritize health care resources. Here, we combined spatial multi-transcriptomics and proteomics approaches to characterize the tumor and immunological landscape of 61 samples. We observed a spatial and HCC-recurrence-associated distribution of natural killer (NK) cells in the invasive front and tumor center. Using artificial-intelligence alongside an extreme gradient-boosting algorithm, we developed the Tumor Immune MicroEnvironment Spatial (“TIMES”) score based on the expression of five NK-associated markers (SPON2, ZFP36L2, ZFP36, VIM, and HLA-DRB1) to predict HCC recurrence. We also demonstrated that TIMES score (HR = 29.6, P < 0.001) outperforms the current standard tools for patient risk stratification including the TNM (HR = 1.93, P = 0.113) and BCLC (HR = 1.55, P = 0.253) systems. In the clinic, we validated the model in 103 patients from three multi-centered cohorts achieve a real-world sensitivity of 90.00% and specificity of 90.24%. In the lab, following up on the individual marker with the highest prediction accuracy, in vivo models revealed that SPON2 increases IFN-γ secretion and enhances infiltration potential of NK cells at the invasive front. Additionally, we established the TIMES score on a publicly accessible website that can be easily achieved by different levels of pathology labs to facilitate global prediction of HCC recurrence risk and stratification of high-risk patients. With its ability to efficiently stratify high-risk patients, it exemplifying the utility of artificial intelligence to improve our understanding on TIME features that underlie tumor progression.