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.
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 explore the value of multidisciplinary team (MDT) discussion in the comprehensive treatment of HER-2 positive breast cancer.MethodThe clinical data of 2 patients with HER-2 positive breast cancer admitted to the Affiliated Hospital of Southwest Medical University after MDT discussions were analyzed retrospectively.ResultsCase 1 was a 32-year-old woman diagnosed with left breast non-special type invasive carcinoma at admission, cT2N1M0, stage ⅡB, WHO grade 2, ER (–), PR (–), HER-2 (+++), Ki-67 (+, 20%). After MDT discussion, the patient was treated with neoadjuvant chemotherapy for 6 cycles, and the efficacy evaluation was partial response, received left breast conserving surgery and axillary lymph node dissection (ALND), postoperative staging ypT1aN1ycM0, stage ⅡA, Miller-Payne grade 4, the patient was satisfied with the shape of breast, received radiotherapy and anti-HER-2 therapy after surgery. At present, there was no recurrence and metastasis during anti-HER-2 therapy. Case 2 was diagnosed with right breast non-special type invasive carcinoma at admission, cT3N0M0, stage ⅡB, WHO grade 3, ER (–), PR (–), HER-2 (+++), Ki-67 (+, 40%), local advanced breast cancer. After MDT discussion, the patient was treated with neoadjuvant chemotherapy for 2 cycles, and the efficacy evaluation was progressive disease. After the replacement of two neoadjuvant chemotherapy regimen, the efficacy evaluation was still progressive disease. Finally after MDT discussion, the patient received right breast mastectomy and ALND, postoperative staging ypT4bN1ycM0, stage ⅢB, Miller-Payne grade 1, received radiotherapy, adjuvant treatment with pyrotinib and capecitabine after surgery. The patient was followed up for 3 months by telephone, the patient did not follow the doctor’ instructions, no recurrence and metastasis was found in the review.ConclusionUnder the precision medical system, comprehensive treatment of breast cancer based on the MDT model could target patients’ disease characteristics, physical conditions, previous diagnosis and treatment, family situation, and other individual factors, formulate the best personal treatment plan for patients, and bring greater benefits to patients.
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.
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.
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.
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.
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.
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.