Objective To systematically evaluate the short-term efficacy and safety of McKeown and Sweet methods in the treatment of esophageal cancer. Methods PubMed, EMbase, The Cochrane Library, Web of Science, Wanfang, VIP, CNKI and China General Library of Biomedical Literature were searched for literature on the short-term efficacy and safety of McKeown and Sweet methods in the treatment of esophageal cancer published from the establishment to May 2023. Newcastle-Ottawa Scale was used to evaluate the quality of researches, and meta-analysis was performed using RevMan5.4. Results A total of 9 articles were included, involving 3687 patients including 1019 in the McKeown group and 2668 in the Sweet group. NOS score was 8-9 points. There were no statistical differences in the age, sex or American Joint Committee on Cancer stage between the two groups (P>0.05). Patients in the McKeown group had longer operative time and hospital stay, more intraoperative blood loss, and higher Eastern Cooperative Oncology Group scores than those in the Sweet group (P<0.05). However, the McKeown operation could remove more lymph nodes. In terms of safety, the incidences of pulmonary complications [OR=2.20, 95%CI (1.40, 3.46), P＜0.001] and postoperative anastomotic leakage [OR=2.06, 95%CI (1.45, 2.92), P＜0.001] were higher in the McKeown group than those in the Sweet group. In addition, there were no statistical differences between the two groups in the Karnofsky score, cardiac complications, vocal cord injury or paralysis, chylous leakage, or gastric emptying (P>0.05). Conclusion Compared with McKeown, Sweet method has advantages in operation time, intraoperative blood loss and hospital stay, and had lower incidence of postoperative pulmonary complications and anastomotic leakage. However, McKeown has more lymph node dissection.
ObjectiveTo evaluate the efficacy and safety of robot-assisted thymectomy (RATS) versus video-assisted thoracoscopic thymectomy (VATS). MethodsWeb of Science, PubMed, EMbase, The Cochrane Library, Wanfang, VIP and CNKI databases were searched by computer from inception to February 2022. Relevant literatures that compared the efficacy and safety of RATS with those of VATS were screened. The Newcastle-OttawaScale (NOS) was used to evaluate the quality of included cohort studies, and Review Manager 5.4 software was utilized to perform a meta-analysis. ResultsA total of 16 retrospective cohort studies were included, covering a total of 1 793 patients (874 patients in the RATS group and 919 patients in the VATS group). The NOS scores of the included studies were≥7 points. Meta-analysis results revealed that RATS had less intraoperative bleeding (MD=−22.45, 95%CI −34.16 to −10.73, P<0.001), less postoperative chest drainage (MD=−80.29, 95%CI −144.86 to −15.72, P=0.010), shorter postoperative drainage time (MD=−0.69, 95%CI −1.08 to −0.30, P<0.001), shorter postoperative hospital stay (MD=−1.14, 95%CI −1.55 to −0.72, P<0.001) and fewer conversion to thoractomy (OR=0.40, 95%CI 0.23 to 0.69, P=0.001) than VATS; whereas, the operative time (MD=8.37, 95%CI −1.21 to 17.96, P=0.090), incidence of postoperative myasthenia gravis (OR=0.85, 95%CI 0.52 to 1.40, P=0.530), overall postoperative complications rate (OR=0.80, 95%CI 0.42 to 1.50, P=0.480) and tumour size (MD=−0.18, 95%CI −0.38 to 0.03, P=0.090) were not statistically different between the two groups. ConclusionIn the aspects of intraoperative bleeding, postoperative chest drainage, postoperative drainage time, postoperative hospital stay and conversion to thoracotomy, RATS has unique advantages over the VATS.
ObjectiveTo evaluate the short-term outcome of robot-assisted thoracoscopic surgery (RATS) for the treatment of posterior mediastinal neurogenic tumour. MethodsThe clinical data of consecutive patients with mediastinal neurogenic tumors who received RATS treatment completed by the same operator in the Department of Thoracic Surgery, Gansu Provincial People's Hospital from June 2016 to June 2022 were retrospectively analyzed. The tumors were preoperatively localized and evaluated using magnetic resonance imaging or enhanced CT. Results A total of 35 patients were enrolled, including 19 males and 16 females with a mean age of 34.9±7.1 years. All patients successfully completed the resection of posterior mediastinal neurogenic tumors under RATS, and no conversion to thoracotomy occurred during the operation. The average operative time was 62.3±18.0 min, docking time was 10.3±2.6 min, intraoperative bleeding was 33.9±21.6 mL, postoperative 24-hour chest drainage was 69.0±28.9 mL, postoperative chest drainage time was 2.0 (1.0, 3.0) d and the postoperative hospital stay was 3.0 (2.0, 4.0) d. Postoperative complications occurred in 3 patients, including 2 patients with transient Honor syndrome and 1 patient with transient anhidrosis of the affected upper limb. ConclusionRATS for posterior mediastinal neurogenic tumours is safe, effective and feasible, and allows the full benefit of the robotic surgical system to be exploited.
Objective To systematically evaluate the accuracy of endoscopy-based artificial intelligence (AI)-assisted diagnostic systems in the diagnosis of early-stage esophageal cancer and provide a scientific basis for its diagnostic value. MethodsPubMed, EMbase, The Cochrane Library, Web of Science, Wanfang database, VIP database and CNKI database were searched by computer to search for the relevant literature about endoscopy-based AI-assisted diagnostic systems for the diagnosis of early esophageal cancer from inception to March 2022. The QUADAS-2 was used for quality evaluation of included studies. Meta-analysis of the literature was carried out using Stata 16, Meta-Disc 1.4 and RevMan 5.4 softwares. A bivariate mixed effects regression model was utilized to calculate the combined diagnostic efficacy of the AI-assisted system and meta-regression analysis was conducted to explore the sources of heterogeneity. ResultsA total of 17 articles were included, which consisted of 13 retrospective cohort studies and 4 prospective cohort studies. The results of the quality evaluation using QUADAS-2 showed that all included literature was of high quality. The obtained meta-analysis results revealed that the AI-assisted system in the diagnosis of esophageal cancer presented a combined sensitivity of 0.94 (95%CI 0.91 to 0.96), a specificity of 0.85 (95%CI 0.74 to 0.92), a positive likelihood ratio of 6.28 (95%CI 3.48 to 11.33), a negative likelihood ratio of 0.07 (95%CI 0.05 to 0.11), a diagnostic odds ratio of 89 (95%CI 38 to 208) and an area under the curve of 0.96 (95%CI 0.94 to 0.98). ConclusionThe AI-assisted diagnostic system has a high diagnostic value for early stage esophageal cancer. However, most of the included studies were retrospective. Therefore, further high-quality prospective studies are needed for validation.
ObjectiveTo summarize and explore the application of machine learning models to survival data with non-proportional hazards (NPH), and to provide a methodological reference for large-scale, high-dimensional survival data. MethodsFirst, the concept of NPH and related testing methods were outlined. Then the advantages and disadvantages of machine learning algorithm-based NPH survival analysis methods were summarized based on the relevant literature. Finally, using real-world clinical data, a case study was conducted with two ensemble machine learning models and two deep learning models in survival data with NPH: a study of the risk of death within 30 days in stroke patients in the ICU. ResultsEight commonly used machine learning model-based NPH survival analyses were identified, including five traditional machine learning models such as random survival forest and three deep learning models based on artificial neural networks (e.g., DeepHit). The case study found that the random survival forest model performed the best (C-index=0.773, IBS=0.151), and the permutation importance-based algorithm found that age was the most important characteristic affecting the risk of death in stroke patients. ConclusionSurvival big data in the era of precision medicine presenting NPH are common, and machine learning model-based survival analysis can be used when faced with more complex survival data and higher survival analysis needs.