Multidisciplinary perspectives on newly revised 2018 FIGO staging of cancer of the cervix uteri / 부인종양

No abstract available.

Multiple risk factors prediction models for high risk population of colorectal cancer / 浙江大学学报·医学版

Colorectal cancer is caused by the interaction of genetic and environment factors. Domestic and foreign scholars have attempted to develop several colorectal cancer risk prediction models, in order to identity risk factors, to screen for high risk population and evaluate the risk of developing colorectal cancer, so as to provide personalized screening protocols for individuals with different risk, and eventually reduce the incidence and mortality rate of colorectal cancer. Currently, the common colorectal cancer risk prediction models were mainly developed based on case-control study and cohort study. Models developed in European and American regions and Asia (excluding China) only include common risk factors, while Chinese models also include hereditary factors on the bases of common risk factors. However, the development and verification of each model are mainly based on local population, whether it can be applied for other population need to be determined. This article reviews the development, validation and evaluation of the risk prediction models, in order to provide a basis for developing more precise risk prediction models for colorectal cancer.

Methodology for survival assessment of cancer patients using population-based cancer registration data / 浙江大学学报·医学版

Evaluating and monitoring long-term survival of cancer patients and reporting the survival rate are routinely employed by cancer registries. Long-term survival rate is a necessary indicator in evaluating the effect of cancer therapy and cancer burden. Cohort method is a traditional approach for survival analysis, but it essentially reflects the survival expectations of patients diagnosed many years ago, therefore survival status of cancer patients was often disclosed with delay. Given the limitation of cohort method, period analysis and model-based period analysis are subsequently proposed and gradually applied in assessment of survival rates in recent years. Period analysis includes the patients of interest period, which reflects more up-to-date estimates of long-term survival of cancer patients. While model-based period analysis can use the existing data to calculate survival rates and to assess the trend, and predict survival rates in the future. Compared with cohort approach, period analysis and model-based period analysis are better in timeliness and precision in survival analysis. This article reviews the definition and theory, calculation and application of cohort method, period analysis and model-based period analysis, in order to provide a basis on up-to-date and precise assessment of survival rates of cancer patients.