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.
This comprehensive review systematically explores the multifaceted applications, inherent challenges, and promising future directions of artificial intelligence (AI) within the medical domain. It meticulously examines AI's specific contributions to basic medical research, disease prevention, intelligent diagnosis, treatment, rehabilitation, nursing, and health management. Furthermore, the review delves into AI's innovative practices and pivotal roles in clinical trials, hospital administration, medical education, as well as the realms of medical ethics and policy formulation. Notably, the review identifies several key challenges confronting AI in healthcare, encompassing issues such as inadequate algorithm transparency, data privacy concerns, absent regulatory standards, and incomplete risk assessment frameworks. Looking ahead, the future trajectory of AI in healthcare encompasses enhancing algorithm interpretability, propelling generative AI applications, establishing robust data-sharing mechanisms, refining regulatory policies and standards, nurturing interdisciplinary talent, fostering collaboration among industry, academia, and medical institutions, and advancing inclusive, personalized precision medicine. Emphasizing the synergy between AI and emerging technologies like 5G, big data, and cloud computing, this review anticipates a new era of intelligent collaboration and inclusive sharing in healthcare. Through a multidimensional analysis, it presents a holistic overview of AI's medical applications and development prospects, catering to researchers, practitioners, and policymakers in the healthcare sector. Ultimately, this review aims to catalyze the deep integration and innovative deployment of AI technology in healthcare, thereby driving the sustainable advancement of smart healthcare.
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.
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.
This article introduces the exploration and establishment of “grass-roots Party building + targeted poverty alleviation” model by the Party Branch of Emergency Department of West China Hospital of Sichuan University, and discusses how to establish the “trinity mode” of management support, personnel training, and on-site guidance under the leading of grass-roots Party building through a series of the branches combined activities, according to the core idea of “strengthening the Party construction, bringing people closer together, and promoting development”. The aim is to form a long-term mechanism of grass-roots Party building and targeted medical poverty alleviation through continuously implementing this model, which can benefit more people in remote and ethnic minority areas and contribute to “Healthy China 2030”.
ObjectiveTo summarize the application of circulating free DNA (cfDNA) in the diagnosis and treatment of hepatocellular carcinoma (HCC). MethodThe relevant literature on the application of cfDNA in the diagnosis and treatment of HCC both domestic and international was reviewed and summarized. ResultsThe cfDNA is an emerging biomarker in recent years. At present, the different detection methods had been reported in a large number of studies to detect abnormal methylation, hot spot mutation, gene copy number variation, quantitative detection of cfDNA concentration, etc. It was found that the cfDNA could be used in the management process of early diagnosis, treatment guidance, and efficacy evaluation of HCC patients. ConclusionscfDNA detection is a good tool in the diagnosis and treatment of HCC, which can help clinicians make-decisions and bring more possibilities for the diagnosis and treatment of HCC, which is of great significance for changing the current diagnosis and treatment of HCC. However, there are still many challenges in cost control, technology optimization, and standardization of evaluation indicators. With the continuous progress of molecular biology technology and artificial intelligence, the application of cfDNA in diagnosis and treatment of HCC will be further expanded, its advantages will be better played, and the related shortcomings will be gradually solved.
ObjectiveTo review the progress of radiomics in the field of colorectal cancer in recent years and summarize its value in the imaging diagnosis of colorectal cancer.MethodsEighty English and seven Chinese articles were retrieved through PUBMED, OVID, CNKI, Weipu and Wanfang. The structure and content of these literatures were classified and analyzed.ResultsIn five studies predicting the preoperative stages of colorectal cancer based on CT radiomics, the area under curve (AUC) ranged from 0.736 to 0.817; in two studies predicting the preoperative stages of colorectal cancer based on MRI radiomics, the AUC were 0.87 and 0.827 respectively. In two studies about radiomics analysis for evaluation of pathological complete response to neoadjuvant chemoradiotherapy based on CT, the AUC were 0.79 and 0.72 respectively; in four studies about radiomics analysis for evaluation of pathological complete response to neoadjuvant chemoradiotherapy based on MRI, the AUC ranged from 0.84 to 0.979. In one study evaluating the sensitivity of neoadjuvant chemotherapy based on MRI radiomics, the AUC was 0.79. In one study predicting the postoperative survival rate based on MRI radiomics, the AUC value of the final model was 0.827. In one study, the accuracy of the model based on PET/CT radiomics in 4-year disease-free survival (DSS), progression-free survival (DFS) and overall survival (OS) were 0.87, 0.79 and 0.79 respectively.ConclusionAt present, radiomics has a valuable impact on preoperative staging, neoadjuvant therapy evaluation, and survival analysis of colorectal cancer.
Lung cancer is a most common malignant tumor of the lung and is the cancer with the highest morbidity and mortality worldwide. For patients with advanced non-small cell lung cancer who have undergone epidermal growth factor receptor (EGFR) gene mutations, targeted drugs can be used for targeted therapy. There are many methods for detecting EGFR gene mutations, but each method has its own advantages and disadvantages. This study aims to predict the risk of EGFR gene mutation by exploring the association between the histological features of the whole slides pathology of non-small cell lung cancer hematoxylin-eosin (HE) staining and the patient's EGFR mutant gene. The experimental results show that the area under the curve (AUC) of the EGFR gene mutation risk prediction model proposed in this paper reached 72.4% on the test set, and the accuracy rate was 70.8%, which reveals the close relationship between histomorphological features and EGFR gene mutations in the whole slides pathological images of non-small cell lung cancer. In this paper, the molecular phenotypes were analyzed from the scale of the whole slides pathological images, and the combination of pathology and molecular omics was used to establish the EGFR gene mutation risk prediction model, revealing the correlation between the whole slides pathological images and EGFR gene mutation risk. It could provide a promising research direction for this field.