ObjectivesRobot-assisted thoracic surgery (RATS) and video-assisted thoracic surgery (VATS) are the two principal minimally invasive surgical approaches for patients with lung cancer. This study aimed at comparing the long-term and short-term outcomes of RATS and VATS for lung cancer.MethodsA comprehensive search for studies that compared RATS versus VATS for lung cancer published until November 31, 2021, was conducted. Data on perioperative outcomes and oncologic outcomes were subjected to meta-analysis. PubMed, Web of Science, and EMBASE were searched based on a defined search strategy to identify eligible studies before November 2021.ResultsTwenty-six studies comparing 45,733 patients (14,271 and 31,462 patients who underwent RATS and VATS, respectively) were included. The present meta-analysis showed that there were no significant differences in operative time, any complications, tumor size, chest drain duration, R0 resection rate, lymph station, 5-year overall survival, and recurrence rate. However, compared with the VATS group, the RATS group had less blood loss, a lower conversion rate to open, a shorter length of hospital stay, more lymph node dissection, and better 5-year disease-free survival.ConclusionsRATS is a safe and feasible alternative to VATS for patients with lung cancer.

BackgroundAlthough minimally invasive pancreaticoduodenectomy has gained worldwide interest, there are limited comparative studies between two minimally invasive pancreaticoduodenectomy techniques. This meta-analysis aimed to compare the safety and efficacy of robotic and laparoscopic pancreaticoduodenectomy (LPD), especially the difference in the perioperative and short-term oncological outcomes.MethodsPubMed, China National Knowledge Infrastructure (CNKI), Wanfang Data, Web of Science, and EMBASE were searched based on a defined search strategy to identify eligible studies before July 2021. Data on operative times, blood loss, overall morbidity, major complications, vascular resection, blood transfusion, postoperative pancreatic fistula (POPF), delayed gastric emptying (DGE), conversion rate, reoperation, length of hospital stay (LOS), and lymph node dissection were subjected to meta-analysis.ResultsOverall, the final analysis included 9 retrospective studies comprising 3,732 patients; 1,149 (30.79%) underwent robotic pancreaticoduodenectomy (RPD), and 2,583 (69.21%) underwent LPD. The present meta-analysis revealed nonsignificant differences in operative times, overall morbidity, major complications, blood transfusion, POPF, DGE, reoperation, and LOS. Alternatively, compared with LPD, RPD was associated with less blood loss (p = 0.002), less conversion rate (p < 0.00001), less vascular resection (p = 0.0006), and more retrieved lymph nodes (p = 0.01).ConclusionRPD is at least equivalent to LPD with respect to the incidence of complication, incidence and severity of DGE, and reoperation and length of hospital stay. Compared with LPD, RPD seems to be associated with less blood loss, lower conversion rate, less vascular resection, and more retrieved lymph nodes.Systematic Review Registrationhttps://www.crd.york.ac.uk/PROSPERO/#recordDetails, identifier CRD2021274057

BackgroundArtificial intelligence has far surpassed previous related technologies in image recognition and is increasingly used in medical image analysis. We aimed to explore the diagnostic accuracy of the models based on deep learning or radiomics for lung cancer staging.MethodsStudies were systematically reviewed using literature searches from PubMed, EMBASE, Web of Science, and Wanfang Database, according to PRISMA guidelines. Studies about the diagnostic accuracy of radiomics and deep learning, including the identifications of lung cancer, tumor types, malignant lung nodules and lymph node metastase, were included. After identifying the articles, the methodological quality was assessed using the QUADAS-2 checklist. We extracted the characteristic of each study; the sensitivity, specificity, and AUROC for lung cancer diagnosis were summarized for subgroup analysis.ResultsThe systematic review identified 19 eligible studies, of which 14 used radiomics models and 5 used deep learning models. The pooled AUROC of 7 studies to determine whether patients had lung cancer was 0.83 (95% CI 0.78–0.88). The pooled AUROC of 9 studies to determine whether patients had NSCLC was 0.78 (95% CI 0.73–0.83). The pooled AUROC of the 6 studies that determined patients had malignant lung nodules was 0.79 (95% CI 0.77–0.82). The pooled AUROC of the other 6 studies that determined whether patients had lymph node metastases was 0.74 (95% CI 0.66–0.82).ConclusionThe models based on deep learning or radiomics have the potential to improve diagnostic accuracy for lung cancer staging.Systematic Review Registrationhttps://inplasy.com/inplasy-2022-3-0167/, identifier: INPLASY202230167.

Long non-coding RNA Fer-1-like protein 4 (FER1L4) has been reported to play crucial regulatory roles in tumor progression and apoptosis. However, its clinical significance and biological role in non-small cell lung cancer (NSCLC) are completely unknown. The purpose of this study was to investigate the expression of lncRNA FER1L4 in plasma and tissues of patients with NSCLC and study the mechanism of proliferation and apoptosis of lung cancer cells. The expression levels of FER1L4 in plasma and tissues of NSCLC patients and cell lines were analyzed via RT-qPCR. The effects of FER1L4 on cell proliferation, migration and invasion were analyzed by CCK-8, wound healing and Transwell assays, respectively. The expression levels of related proteins were detected by western blot assay, while cell apoptosis was determined by Hoechst staining and flow cytometry. The results revealed that FER1L4 was significantly downregulated in NSCLC plasma and tissues and lung cancer cell lines compared to corresponding controls. Moreover, a significant decrease of cell proliferation, migration and invasion were observed in FER1L4-overexpressed cells. FER1L4 could promote phosphatase and tension homolog deleted on chromosome ten (PTEN) and p53 expression, inhibit AKT phosphorylation expression, thus increasing the proportion of apoptotic cells. The present study indicated that FER1L4 may inhibit cell proliferation and promote apoptosis of NSCLC cells via the PTEN/AKT/p53 pathway, which provides a better understanding of the pathogenesis of NSCLC and may provide a novel potential therapeutic target for clinical treatment.