Objective To evaluate the quality of the registration information for trials sponsored by China registered in the WHO International Clinical Trial Registration Platform (ICTRP) primary registries or other registries that meet the requirements of the International Committee Medical Journal Editor (ICMJE). Methods We assessed the registration information for trials registered in the 9 WHO primary registries and one other registry that met the requirements of ICJME as of 15 October 2008. We analyzed the trial registration data set in each registry and assessed the registration quality against the WHO Trial Registration Data Set (TRDS). We also evaluated the quality of the information in the Source(s) of Monetary or Material Support section, using a specially prepared scale. Results The entries in four registries met the 20 items of the WHO TRDS. These were the Chinese Clinical Trial Registration Center (ChiCR), Australian New Zealand Clinical Trials Registry (NZCTR), Clinical Trials Registry – India (CTRI), and Sri Lanka Clinical Trials Registry (SLCTR). Registration quality varied among the different registries. For example, using the Scale of TRDS, the NZCTR scoreda median of 19 points, ChiCTR (median = 18 points), ISRCTN.org (median = 17 points), and Clinical trials.org (median = 12 points). The data on monetary or material support for ChiCTR and ISRCTN.org were relatively complete and the score on our Scale for the Completeness of Funding Registration Quality ranged from ChiCTR (median = 7 points), ISRCTN.org (median = 6 points), NZCTR (median = 3 points) to clinicaltrials.gov (median = 2 points). Conclusion Further improvements are needed in both the quantity and quality of trial registration. This could be achieved by full completion of the 20 items of the WHO TRDS. Future research should assess ways to ensure the quality and scope of research registration and the role of mandatory registration of funded research.
ObjectiveTo develop an immune-related genes (IRGs) based prognostic signature and evaluate the value in predicting prognosis in patients with colon cancer.MethodsGene chip data sets of 452 colon cancer patients were collected from the TCGA database, and 2 498 IRGs data sets were obtained from the ImmPort database. After taking the intersection, univariate and multivariate Cox proportional hazards regression analysis were used to screen and construct the IRGs gene model. To evaluate the prognostic value of genetic models, Cox proportional hazards regression was used to analyze the correlation between IRGs model/clinicopathological features with prognosis of colon cancer. The relationship between risk score and immune cell infiltration was analyzed too.ResultsA total of 206 differentially expressed IRGs were identified in colon cancer tissues, and 11 kinds of IRGs were identified by univariate and multivariate Cox proportional hazards regression analysis: solute carrier family 10 member 2 (SLC10A2), C-X-C motif chemokine ligand 5 (CXCL5), C-C motif chemokine ligand 28 (CCL28), immunoglobulin kappa variable 1D-42 (IGKV1D-42), chromogranin A (CHGA), endothelial cell specific molecule 1 (ESM1), gastrin releasing peptide (GRP), stanniocalcin 2 (STC2), urocortin (UCN), oxytocin receptor (OXTR) and immunoglobulin heavy constant gamma 1 (IGHG1). Colon cancer patients were divided into high risk group and low risk group according to the median value of risk value of IRGs risk markers. Patients in the high risk group had shorter overall survival (OS) than that in the low risk group (P<0.001). The area of the time-dependent ROC curve (AUC) was 0.754, suggesting that IRGs model had a good ability to predict the prognosis of colon cancer patients. The higher the risk value of IRGs, the later T stage of colon cancer (T3–T4), the more lymph node metastasis (N1–N2) and the later clinical stage of colon cancer (Ⅲ–Ⅳ), P<0.05. Except for neutrophils, the infiltration density of B cells, CD4+ T cells, CD8+ T cells, dendritic cells and macrophages were significantly increased with the increased of the risk value (P<0.05).ConclusionThe risk values of the 11 kinds of IRGs gene models screened in this study can be used to predict the prognosis of colon cancer patients, and can be used as biomarkers to evaluate the prognosis of colon cancer patients.
Automatic detection of pulmonary nodule based on computer tomography (CT) images can significantly improve the diagnosis and treatment of lung cancer. However, there is a lack of effective interactive tools to record the marked results of radiologists in real time and feed them back to the algorithm model for iterative optimization. This paper designed and developed an online interactive review system supporting the assisted diagnosis of lung nodules in CT images. Lung nodules were detected by the preset model and presented to doctors, who marked or corrected the lung nodules detected by the system with their professional knowledge, and then iteratively optimized the AI model with active learning strategy according to the marked results of radiologists to continuously improve the accuracy of the model. The subset 5−9 dataset of the lung nodule analysis 2016(LUNA16) was used for iteration experiments. The precision, F1-score and MioU indexes were steadily improved with the increase of the number of iterations, and the precision increased from 0.213 9 to 0.565 6. The results in this paper show that the system not only uses deep segmentation model to assist radiologists, but also optimizes the model by using radiologists' feedback information to the maximum extent, iteratively improving the accuracy of the model and better assisting radiologists.