Objective To investigate the safety and feasibility of transcatheter arterial chemoembolization (TACE) assisted by transradial approach and cone beam computed tomography (CBCT) three-dimensional vascular reconstruction in the treatment of primary liver cancer. Methods The clinical data of 124 patients with primary liver cancer who underwent precision TACE via radial artery in our hospital from May 2018 to December 2019 were retrospectively collected. Results Among the 124 patients, 118 patients were successfully punctured through the left radial artery and completed the TACE operation. The operation time was (109.57±31.32) min, and the median of postoperative hospitalization was 3 d. One patient changed to the right radial artery to complete TACE due to chronic renal failure and left brachial artery and vein puncture and catheterization before operation. The operation time was 119 minutes, and the patient was discharged after 5 days of hospitalization. After successful puncture of the left radial artery in one patient, the forearm artery was twisted into a loop and the guide wire catheter failed to pass, and the right femoral artery was used to complete TACE. The operation time was 123 minutes, and the patient was discharged after 4 days of improvement. The radial artery puncture was unsuccessful in four patients, and the right femoral artery approach was used to complete the operation; the operation time was (111.66±32.77) min, and the median of postoperative hospitalization was 3 d. One of the patients successfully completed up to 5 consecutive TACE via the radial artery. All patients underwent precision TACE with superselective cannulation assisted by CBCT three-dimensional vascular reconstruction. No vascular injury andocclusion, urinary retention, subcutaneous hemorrhage, and other complications occurred in all patients. Conclusions Trans-radial arterial precision TACE is safe and effective, which can be repeated many times and has few complications and high patient comfort. It can be used as one of the routine approaches of TACE.
Cancer presents a significant global public health challenge, impacting human health on a broad scale. In recent years, the rapid advancement of big data-based bioinformatics has unveiled crucial potential in precision oncology through various omics research methods. The advent of radiomics has notably expanded the application scope of medical imaging in the field. However, due to the multi-level and multifactorial nature of tumor initiation and progression, a single omics information remains insufficient to meet the demands for advancing precision oncology strategies. Multi-omics research has become an emerging trend. The research paradigm integrating radiomics with other omics offers a novel perspective for personalized decision-making in oncology. Nevertheless, there persists a need to introduce more integrated new technologies and theories to expedite the progress of this field.
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.
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.
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.
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.
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.
Laparoscopic Roux-en-Y gastric bypass (LRYGB) was recommended as the gold standard procedure for metabolic and bariatric surgery by the National Institutes of Health in the 1990s and then had been adopted till now, which could effectively control excess weight and treat metabolic diseases relevant to obesity in a long term. The current LRYGB procedure had been performed more than half a century of development and update, and is still constantly evolving. Standardized and precise surgical procedures are of great significance in ensuring treatment effect and reducing the incidence of complications. Thus, the author reviewed the development process of LRYGB, further understanding and emphasizing the importance of standardized and precise surgical procedures.
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.
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.