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.
目的 为避免选择和发表偏倚,系统评价者应采用多种查询技术,并尽力获得未发表的研究.本文试图探讨,英特网检索对鉴定未发表和正在进行的临床试验是否有用.研究设计 利用七个Cochrane系统评价的查询策略回顾性地在英特网上检索未纳入的随机对照试验.方法 检索策略 以普通检索式"研究方法学 NEAR干预措施NERA 条件"、用AltaVista在英特网上搜索.测量指标包括搜索时间、英特网搜索已发表研究的回溯率、精确度(已发表和未发表的随机临床试验链接的网页比例)、英特网检索到的未纳入的未发表和正在进行的研究数.结果 用21小时查询了429个网页,找到14个链接到未发表的、正在进行的或最近完成的试验,至少有9个与4篇系统评价相关.英特网检索已发表研究文献的回溯率在0～43.6%,其链接已发表和未发表研究的精确度在0～20.2%.结论 未发表尤其是正在进行的试验的信息可在英特网上找到.潜在的问题是如何评价未经同行评审的电子出版物的质量.急需更强的搜索工具.建议用"Open Trial Initiative"定义英特网发表试验的语法,以加强试验登记的共同操作性.因此,专门的搜索引擎可找到更多有关正在进行和已完成的临床试验信息.
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.
Objective To evaluate effects of three-dimensional (3D) visualized reconstruction technology on short-term benefits of different extent of resection in treating hepatic alveolar echinococcosis (HAE) as well as some disadvantages. Methods One hundred and fifty-two patients with HAE from January 2014 to December 2016 in the Department Liver Surgery, West China Hospital of Sichuan University were collected, there were 80 patients with ≥4 segments and 72 patients with ≤3 segments of liver resection among these patients, which were designed to 3D reconstruction group and non-3D reconstruction group according to the preference of patients. The imaging data, intraoperative and postoperative indicators were recorded and compared. Results The 3D visualized reconstructions were performed in the 79 patients with HAE, the average time of 3D visualized reconstruction was 19 min, of which 13 cases took more than 30 min and the longest reached 150 min. The preoperative predicted liver resection volume of the 79 patients underwent the 3D visualized reconstruction was (583.6±374.7) mL, the volume of intraoperative actual liver resection was (573.8±406.3) mL, the comparison of preoperative and intraoperative data indicated that both agreed reasonably well (P=0.640). Forty-one cases and 38 cases in the 80 patients with ≥4 segments and 72 patients with ≤3 segments of liverresection respectively were selected for the 3D visualized reconstruction. For the patients with ≥4 segments of liver resection, the operative time was shorter (P=0.021) and the blood loss was less (P=0.047) in the 3D reconstruction group as compared with the non-3D reconstruction group, the status of intraoperative blood transfusion had no significant difference between the 3D reconstruction group and the non-3D reconstruction group (P=0.766). For the patients with ≤3 segments of liver resection, the operative time, the blood loss, and the status of intraoperative blood transfusion had no significant differences between the 3D reconstruction group and the non-3D reconstruction group (P>0.05). For the patients with ≥4 segments or ≤3 segments of liver resection, the laboratory examination results within postoperative 3 d, complications within postoperative 90 d, and the postoperative hospitalization time had no significant differences between the 3D reconstruction group and the non-3D reconstruction group (P>0.05). Conclusion 3D visualized reconstruction technology contributes to patients with HAE ≥4 segments of liver resection, it could reduce intraoperative blood loss and shorten operation time, but it displays no remarkable benefits for ≤3 segments of liver resection.
Sepsis is a critical condition. The key factor affecting the survival of patient is whether standard treatment can be obtained timely. Because of the complexity of its pathogenesis and high heterogeneity, there is no special diagnosis method currently. Early identification is difficult. Delayed diagnosis and treatment is closely related to the mortality of patients. With the continuous updating of the guidelines, sepsis has been included in the “time window” disease, putting forward a great challenge to the early screening and evaluation of sepsis. This article aims to review the application of Sepsis-Related Organ Failure Assessment, sepsis biomarkers and artificial intelligence algorithms in early screening and evaluation of sepsis, so as to provide guidance tools for timely starting standardized treatment of sepsis.
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 treatment strategies and clinical experiences of 5 cases of giant plexiform neurofibromas (PNF) involving the head, face, and neck. MethodsBetween April 2021 and May 2023, 5 patients with giant PNFs involving the head, face, and neck were treated, including 1 male and 4 females, aged 6-54 years (mean, 22.4 years). All tumors showed progressive enlargement, involving multiple regions such as the maxillofacial area, ear, and neck, significantly impacting facial appearance. Among them, 3 cases involved tumor infiltration into deep tissues, affecting development, while 4 cases were accompanied by hearing loss. Imaging studies revealed that all 5 tumors predominantly exhibited an invasive growth pattern, in which 2 and 1 also presenting superficial and displacing pattern, respectively. The surgical procedure followed a step-by-step precision treatment strategy based on aesthetic units, rather than simply aiming for maximal tumor resection in a single operation. Routine preoperative embolization of the tumor-feeding vessels was performed to reduce bleeding risk, followed by tumor resection combined with reconstructive surgery. Results All 5 patients underwent 1-3 preoperative embolization procedures, with no intraoperative hemorrhagic complications reported. Four patients required intraoperative blood transfusion. A total of 10 surgical procedures were performed across the 5 patients. One patient experienced early postoperative flap margin necrosis due to ligation for hemostasis; however, the incisions in the remaining patients healed without complications. All patients were followed up for a period ranging from 6 to 36 months, with a mean follow-up duration of 21.6 months. No significant tumor recurrence was observed during the follow-up period. Conclusion For patients with giant PNF involving the head, face, and neck, precision treatment strategy can effectively control surgical risks and improve the standard of aesthetic reconstruction. This approach enhances overall treatment outcomes by minimizing complications and optimizing functional and cosmetic results.
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.
Image interpolation is often required during medical image processing and analysis. Although interpolation method based on Gaussian radial basis function (GRBF) has high precision, the long calculation time still limits its application in field of image interpolation. To overcome this problem, a method of two-dimensional and three-dimensional medical image GRBF interpolation based on computing unified device architecture (CUDA) is proposed in this paper. According to single instruction multiple threads (SIMT) executive model of CUDA, various optimizing measures such as coalesced access and shared memory are adopted in this study. To eliminate the edge distortion of image interpolation, natural suture algorithm is utilized in overlapping regions while adopting data space strategy of separating 2D images into blocks or dividing 3D images into sub-volumes. Keeping a high interpolation precision, the 2D and 3D medical image GRBF interpolation achieved great acceleration in each basic computing step. The experiments showed that the operative efficiency of image GRBF interpolation based on CUDA platform was obviously improved compared with CPU calculation. The present method is of a considerable reference value in the application field of image interpolation.
ObjectiveTo summarize the application of radiomics in colorectal cancer.MethodsRelevant literatures about the therapeutic decision-making, therapeutic, and prognostic evaluation of colorectal cancer using radiomics were collected to make an review.ResultsRadiomics is of great value in preoperative stages, therapeutic, and prognostic evaluation in colorectal cancer.ConclusionRadiomics is an important part of precision medical imaging for colorectal cancer.