Non-small cell lung cancer is one of the cancers with the highest incidence and mortality rate in the world, and precise prognostic models can guide clinical treatment plans. With the continuous upgrading of computer technology, deep learning as a breakthrough technology of artificial intelligence has shown good performance and great potential in the application of non-small cell lung cancer prognosis model. The research on the application of deep learning in survival and recurrence prediction, efficacy prediction, distant metastasis prediction, and complication prediction of non-small cell lung cancer has made some progress, and it shows a trend of multi-omics and multi-modal joint, but there are still shortcomings, which should be further explored in the future to strengthen model verification and solve practical problems in clinical practice.
With the advancement of thyroid tumor treatment concepts and the progress of standardized treatment processes nationwide, the 5-year survival rate of thyroid tumors in China has risen from 67.5% in 2003 to 84.3% in 2015. As China has been continuously enriching its treatment options for advanced thyroid cancer in recent years, gradually improving the standardized treatment system for early and intermediate thyroid cancer, enhancing multidisciplinary collaboration methods and concepts, and regularizing scientific statistics, the survival rate of thyroid tumors continues to improve. We still need to consider the future development direction and core driving force of China’s thyroid discipline, correctly view the “prosperous” stage of domestic thyroid discipline development, and actively review the future development direction of China’s thyroid discipline.
ObjectiveTo categorize and describe stroke-patients based on factors related to patient reported outcomes. MethodsA questionnaire survey was conducted among stroke-patients in nine hospitals and communities in Shanxi Province. The general information questionnaire and stroke-patient reported outcome manual (Stroke-PROM) were completed. Latent profile analysis was used to analyze the scores of Stroke-PROM, and the explicit variables of the model were the final scores of each dimension. ANOVA and correlation analysis were used to measure the correlation between the factors and subtypes. ResultsFour unique stroke-patient profiles emerged, including a low physiological and low social group (9%), a high physiological and middle social group (40%), a middle physiological and middle social group (26%), and a middle physiological and high social group (25%). There were significant differences in scores of four areas among patients with different subtypes (P<0.05). Moreover, there was a correlation between age, payment, exercise and subtypes (P<0.05). ConclusionThere are obvious grouping characteristics for stroke patients. It is necessary to focus on stroke patients who are advanced in age, have a self-funded status and lack exercise, and provide targeted nursing measures to improve their quality of life.
Tuberculosis (TB) is one of the major public health concerns worldwide. Since the development of precision medicine, the filed regarding TB control and prevention has been brought into the era of precision medicine. Although great progress has been achieved in the accurate diagnosis, treatment and management of TB patients, we have to face several challenges. We should seize the opportunity, and develop and improve novel measures in TB prevention on the basis of precision medicine. The accurate diagnosis criteria, treatment regimen and management of TB patients should be carried out according to the standard of precision medicine. We aim to improve the treatment of TB patients and prevent the transmission of TB in the community, thereby contributing to the achievement of the End TB Strategy by 2035.
Retinitis pigmentosa (RP) is an inherited retinal disease characterized by degeneration of retinal pigment epithelial cells. Precision medicine is a new medical model that applies modern genetic technology, combining living environment, clinical data of patients, molecular imaging technology and bio-information technology to achieve accurate diagnosis and treatment, and establish personalized disease prevention and treatment model. At present, precise diagnosis of RP is mainly based on next-generation sequencing technology and preimplantation genetic diagnosis, while precise therapy is mainly reflected in gene therapy, stem cell transplantation and gene-stem cell therapy. Although the current research on precision medicine for RP has achieved remarkable results, there are still many problems in the application process that is needed close attention. For instance, the current gene therapy cannot completely treat dominant or advanced genetic diseases, the safety of gene editing technology has not been solved, the cells after stem cell transplantation cannot be effectively integrated with the host, gene sequencing has not been fully popularized, and the big data information platform is imperfect. It is believed that with the in-depth research of gene sequencing technology, regenerative medicine and the successful development of clinical trials, the precision medicine for RP will be gradually improved and is expected to be applied to improve the vision of patients with RP in the future.
In recent years, deep learning has provided a new method for cancer prognosis analysis. The literatures related to the application of deep learning in the prognosis of cancer are summarized and their advantages and disadvantages are analyzed, which can be provided for in-depth research. Based on this, this paper systematically reviewed the latest research progress of deep learning in the construction of cancer prognosis model, and made an analysis on the strengths and weaknesses of relevant methods. Firstly, the construction idea and performance evaluation index of deep learning cancer prognosis model were clarified. Secondly, the basic network structure was introduced, and the data type, data amount, and specific network structures and their merits and demerits were discussed. Then, the mainstream method of establishing deep learning cancer prognosis model was verified and the experimental results were analyzed. Finally, the challenges and future research directions in this field were summarized and expected. Compared with the previous models, the deep learning cancer prognosis model can better improve the prognosis prediction ability of cancer patients. In the future, we should continue to explore the research of deep learning in cancer recurrence rate, cancer treatment program and drug efficacy evaluation, and fully explore the application value and potential of deep learning in cancer prognosis model, so as to establish an efficient and accurate cancer prognosis model and realize the goal of precision medicine.
The "All of Us" research program is a research project supported by the National Institutes of Health. By recruiting over 1 million volunteers residing in the United States, the project builds a strong research resource to promote the exploration of biological, clinical, social, and environmental determinants of health and disease. This paper introduced the design plan of the "All of Us" research program systematically and provided information that can be used for the construction of a million natural population cohort of precision medicine in China.
Lung cancer is a leading cause of cancer-related deaths worldwide, with its high mortality rate primarily attributed to delayed diagnosis. Radiomics, by extracting abundant quantitative features from medical images, offers novel possibilities for early diagnosis and precise treatment of lung cancer. This article reviewed the latest advancements in radiomics for lung cancer management, particularly its integration with artificial intelligence (AI) to optimize diagnostic processes and personalize treatment strategies. Despite existing challenges, such as non-standardized image acquisition parameters and limitations in model reproducibility, the incorporation of AI significantly enhanced the precision and efficiency of image analysis, thereby improving the prediction of disease progression and the formulation of treatment plans. We emphasized the critical importance of standardizing image acquisition parameters and discussed the role of artificial intelligence in advancing the clinical application of radiomics, alongside future research directions.
ObjectiveTo summarize current patient-derived organoids as preclinical cancer models, and its potential clinical application prospects. MethodsCurrent patient-derived organoids as preclinical cancer models were reviewed according to the results searched from PubMed database. In addition, how cancer-derived human tumor organoids of pancreatic cancer could facilitate the precision cancer medicine were discussed. ResultsThe cancer-derived human tumor organoids show great promise as a tool for precision medicine of pancreatic cancer, with potential applications for oncogene modeling, gene discovery and chemosensitivity studies. ConclusionThe cancer-derived human tumor organoids can be used as a tool for precision medicine of pancreatic cancer.