Along with the popularity of low-dose computed tomography lung cancer screening, an increasing number of early-stage lung cancers are detected. Radical lobectomy with systematic nodal dissection (SND) remains the standard-of-care for operable lung cancer patients. However, whether SND should be performed on non-metastatic lymph nodes remains controversy. Unnecessary lymph node dissection can increase the difficulty of surgery while also causing additional surgical damage. In addition, non-metastatic lymph nodes have been recently reported to play a key role in immunotherapy. How to reduce the surgical damage of mediastinal lymph node dissection for early-stage lung cancer patients is pivotal for modern concept of "minimally invasive surgery for lung cancer 3.0". The selective mediastinal lymph node dissection strategy aims to dissect lymph nodes with tumor metastasis while preserving normal mediastinal lymph nodes. Previous studies have shown that combination of specific tumor segment site, radiology and intraoperative frozen pathology characteristics can accurately predict the pattern of mediastinal lymph node metastasis. The personalized selective mediastinal lymph node dissection strategy formed from this has been successfully validated in a recent prospective clinical trial, providing an important basis for early-stage lung cancer patients to receive more personalized selective lymph node dissection with "precision surgery" strategies.
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.
Neurofibromatosis type 1 (NF1) is an autosomal dominant genetic disease caused by the mutations in the NF1 gene, with an incidence of approximately 1/3 000. Affecting multiple organs and systems throughout the body, NF1 caused a wide variety of clinical symptoms. A comprehensive multidisciplinary diagnostic and treatment model is needed to meet the diverse needs of NF1 patients and improve their quality of life. In recent years, the emergence of targeted therapies has further benefited NF1 patients, and the number of clinical consultations has increased dramatically. However, due to the rarity of the disease itself and insufficient attention previously, the standardized, systematic, and precise diagnosis and treatment model of NF1 still needs to be further improved. In this paper, we reviewed the current status of comprehensive diagnosis and treatment of NF1 in China, combine with our long-term experiences in diagnosis and treatment of this disease. Meanwhile, we propose future directions and several suggestions for the comprehensive diagnosis and treatment model for Chinese NF1 patients.
Lung cancer is one of the leading causes of cancer deaths worldwide. Many options including surgery, radiotherapy, chemotherapy, targeted therapy and immunotherapy have been applied in the treatment for lung cancer patients. However, how to develop individualized treatment plans for patients and accurately determine the prognosis of patients is still a very difficult clinical problem. In recent years, radiomics, as an emerging method for medical image analysis, has gradually received the attention from researchers. It is based on the assumption that medical images contain a vast amount of biological information about patients that is difficult to identify with naked eyes but can be accessed by computer. One of the most common uses of radiomics is the diagnosis and treatment of non-small cell lung cancer (NSCLC). In this review, we reviewed the current researches on chest CT-based radiomics in the diagnosis and treatment of NSCLC and provided a brief summary of the current state of research in this field, covering various aspects of qualitative diagnosis, efficacy prediction, and prognostic analysis of lung cancer. We also briefly described the main current technical limitations of this technology with the aim of gaining a broader understanding of its potential role in the diagnosis and treatment of NSCLC and advancing its development as a tool for individualized management of NSCLC patients.
Liddle syndrome and Gordon syndrome are two rare single-gene inherited hypertension diseases. In patients≤40 years, the prevalence of Liddle syndrome is about 1% and Gordon syndrome is uncertain all over the word, for which is often misdiagnosed and mistreated. The therapies of those diseases are targeted at gene mutation sites, as well as combined with modified lifestyle, and can achieve satisfactory diseases control. This paper reports a patient who is diagnosed with Liddle syndrome and Gordon syndrome at the same time. We aimed to consolidate and improve the diagnosis and accurate treatment of those two diseases by sharing, studying and discussing together with clinical doctors.
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.
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.
ObjectiveTo summarize the recent advances and clinical applications of molecular testing in thyroid cancer, discussing its significance in the era of precision medicine and future perspectives. MethodsA systematic review of relevant domestic and international literature was conducted to identify key molecular events closely associated with the development, progression, and prognosis of thyroid cancer, and to evaluate their clinical utility. ResultsMolecular testing provides critical auxiliary diagnostic information for thyroid nodules with indeterminate fine-needle aspiration results. Furthermore, for diagnosed differentiated thyroid cancer, molecular markers serve as important tools for precise risk stratification, guiding surgical extent, radioactive iodine therapy decisions, and targeted drug applications. ConclusionMolecular testing has become a cornerstone tool in advancing thyroid cancer management toward precision medicine, future efforts should focus on exploring novel molecular markers and optimizing clinical practice guidelines.
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.
Against the backdrop of medical digital transformation, West China Hospital of Sichuan University has conducted a 30-year exploration and practice of colorectal cancer data engineering. This study focuses on the integration of special disease digitization and value-based healthcare, achieving standardized management and in-depth mining of colorectal cancer diagnosis and treatment data through constructing a full-life cycle data governance system, multi-center data platform, and intelligent application scenarios (such as clinical decision support systems). The practical results show that this data engineering has formed a specialized disease database containing more than 9 500 cases of structured data, and promoted the collaborative development of the entire chain of “production–study–research–business–government”, providing a learnable digital paradigm for improving diagnostic and treatment accuracy and optimizing medical resource allocation. The study indicates that special disease digitization is a key path to achieving value-based healthcare, and its experience in data standardization and medical-engineering cross-innovation is of reference significance for other disease fields.