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BACKGROUND@#Lung cancer is the cancer with the highest mortality at home and abroad at present. The detection of lung nodules is a key step to reducing the mortality of lung cancer. Artificial intelligence-assisted diagnosis system presents as the state of the art in the area of nodule detection, differentiation between benign and malignant and diagnosis of invasive subtypes, however, a validation with clinical data is necessary for further application. Therefore, the aim of this study is to evaluate the effectiveness of artificial intelligence-assisted diagnosis system in predicting the invasive subtypes of early‑stage lung adenocarcinoma appearing as pulmonary nodules.@*METHODS@#Clinical data of 223 patients with early-stage lung adenocarcinoma appearing as pulmonary nodules admitted to the Lanzhou University Second Hospital from January 1st, 2016 to December 31th, 2021 were retrospectively analyzed, which were divided into invasive adenocarcinoma group (n=170) and non-invasive adenocarcinoma group (n=53), and the non-invasive adenocarcinoma group was subdivided into minimally invasive adenocarcinoma group (n=31) and preinvasive lesions group (n=22). The malignant probability and imaging characteristics of each group were compared to analyze their predictive ability for the invasive subtypes of early-stage lung adenocarcinoma. The concordance between qualitative diagnostic results of artificial intelligence-assisted diagnosis of the invasive subtypes of early-stage lung adenocarcinoma and postoperative pathology was then analyzed.@*RESULTS@#In different invasive subtypes of early-stage lung adenocarcinoma, the mean CT value of pulmonary nodules (P<0.001), diameter (P<0.001), volume (P<0.001), malignant probability (P<0.001), pleural retraction sign (P<0.001), lobulation (P<0.001), spiculation (P<0.001) were significantly different. At the same time, it was also found that with the increased invasiveness of different invasive subtypes of early-stage lung adenocarcinoma, the proportion of dominant signs of each group gradually increased. On the issue of binary classification, the sensitivity, specificity, and area under the curve (AUC) values of the artificial intelligence-assisted diagnosis system for the qualitative diagnosis of invasive subtypes of early-stage lung adenocarcinoma were 81.76%, 92.45% and 0.871 respectively. On the issue of three classification, the accuracy, recall rate, F1 score, and AUC values of the artificial intelligence-assisted diagnosis system for the qualitative diagnosis of invasive subtypes of early-stage lung adenocarcinoma were 83.86%, 85.03%, 76.46% and 0.879 respectively.@*CONCLUSIONS@#Artificial intelligence-assisted diagnosis system could predict the invasive subtypes of early‑stage lung adenocarcinoma appearing as pulmonary nodules, and has a certain predictive value. With the optimization of algorithms and the improvement of data, it may provide guidance for individualized treatment of patients.
Sujet(s)
Humains , Adénocarcinome/anatomopathologie , Adénocarcinome pulmonaire/anatomopathologie , Intelligence artificielle , Tumeurs du poumon/anatomopathologie , Nodules pulmonaires multiples , Invasion tumorale , Études rétrospectivesRÉSUMÉ
@#Objective To evaluate the association between pressure-controlled ventilation-volume guaranteed (PCV-VG) mode and volume-controlled ventilation (VCV) mode on postoperative pulmonary complications (PPCs) in patients undergoing thoracoscopic lung resection. Methods A retrospective cohort analysis of 329 patients undergoing elective thoracoscopic lung resection in West China Hospital of Sichuan University between September 2020 and March 2021 was conducted, including 213 females and 116 males, aged 53.6±11.3 years. American Society of Anesthesiologists (ASA) grade wasⅠ-Ⅲ. The patients who received lung-protective ventilation strategy during anesthesia were divided into a PCV-VG group (n=165) and a VCV group (n=164) according to intraoperative ventilation mode. Primary outcome was the incidence of PPCs during hospitalization. Results A total of 73 (22.2%) patients developed PPCs during hospitalization. The PPCs incidence of PCV-VG and VCV was 21.8% and 22.6%, respectively (RR=0.985, 95%CI 0.569-1.611, P=0.871). Multivariate logistic regression analysis showed that there was no statistical difference in the incidence of PPCs between PCV-VG and VCV mode during hospitalization (OR=0.846, 95%CI 0.487-1.470, P=0.553). Conclusion Among patients undergoing thoracoscopic lung resection, intraoperative ventilation mode (PCV-VG or VCV) is not associated with the risk of PPCs during hospitalization.
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@#Objective To evaluate the association of intraoperative ventilation modes with postoperative pulmonary complications (PPCs) in adult patients undergoing selective cardiac surgery under cardiopulmonary bypass (CPB). Methods The clinical data of 604 patients who underwent selective cardiac surgical procedures under CPB in the West China Hospital, Sichuan University from June to December 2020 were retrospectively analyzed. There were 293 males and 311 females with an average age of 52.0±13.0 years. The patients were divided into 3 groups according to the ventilation modes, including a pressure-controlled ventilation-volume guarantee (PCV-VG) group (n=201), a pressure-controlled ventilation (PCV) group (n=200) and a volume-controlled ventilation (VCV) group (n=203). The association between intraoperative ventilation modes and PPCs (defined as composite of pneumonia, respiratory failure, atelectasis, pleural effusion and pneumothorax within 7 days after surgery) was analyzed using modified poisson regression. Results The PPCs were found in a total of 246 (40.7%) patients, including 86 (42.8%) in the PCV-VG group, 75 (37.5%) in the PCV group and 85 (41.9%) in the VCV group. In the multivariable analysis, there was no statistical difference in PPCs risk associated with the use of either PCV-VG mode (aRR=0.951, 95%CI 0.749-1.209, P=0.683) or PCV mode (aRR= 0.827, 95%CI 0.645-1.060, P=0.133) compared with VCV mode. Conclusion Among adults receiving selective cardiac surgery, PPCs risk does not differ significantly by using different intraoperative ventilation modes.
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@# Objective To systematically evaluate the expression of programmed cell death receptor 1 (PD-1) and programmed cell death ligand 1 (PD-L1) in esophageal squamous cell carcinoma and its relationship with prognosis. Methods The literature from PubMed, EMbase, The Cochrane Library, Web of Science, CNKI and Wanfang data from inception to February 22, 2020 was searched by computer. Data were extracted and the quality of literature was evaluated using RevMan 5.3 software for meta-analysis. Egger's and Begg's tests were used to evaluate publication bias, and Stata 15.1 software was used for sensitivity analysis. Results A total of 16 articles were included, and there were 3 378 patients with esophageal squamous cell carcinoma. The methodological index for nonrandomized studies (MINORS) scores were all 12 points and above. The meta-analysis results showed that the positive expression rates of PD-1 and PD-L1 in tumor cells were 37.8% (190/504) and 41.7% (1 407/3 378), respectively. The positive expression of PD-L1 in tumor immune infiltrating cells was 41.7% (412/987). The overall survival (OS) of the tumor cell with high PD-L1 expression was lower than that with low PD-LI expression (HR=1.30, 95%CI 1.01-1.69, P=0.04). The OS of the tumor immune infiltrating cell with high PD-L1 expression was significantly higher than that with low PD-LI expression (HR=0.65, 95%CI 0.53-0.80, P<0.000 1). Conclusion PD-L1 has a high expression rate in esophageal squamous cell carcinoma and is an important factor for the prognosis of esophageal squamous cell carcinoma.
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@#Objective To evaluate the effect of driving pressure-guided lung protective ventilation strategy on lung function in adult patients under elective cardiac surgery with cardiopulmonary bypass. Methods In this randomized controlled trial, 106 patients scheduled for elective valve surgery via median sternal incision under cardiopulmonary bypass from July to October 2020 at West China Hospital of Sichuan University were included in final analysis. Patients were divided into two groups randomly. Both groups received volume-controlled ventilation. A protective ventilation group (a control group, n=53) underwent traditional lung protective ventilation strategy with positive end-expiratory pressure (PEEP) of 5 cm H2O and received conventional protective ventilation with tidal volume of 7 mL/kg of predicted body weight and PEEP of 5 cm H2O, and recruitment maneuver. An individualized PEEP group (a driving pressure group, n=53) received the same tidal volume and recruitment, but with individualized PEEP which produced the lowest driving pressure. The primary outcome was oxygen index (OI) after ICU admission in 30 minutes, and the secondary outcomes were the incidence of OI below 300 mm Hg, the severity of OI descending scale (the Berlin definition), the incidence of pulmonary complications at 7 days after surgery and surgeons’ satisfaction on ventilation. Results There was a statistical difference in OI after ICU admission in 30 minutes between the two groups (273.5±75.5 mm Hg vs. 358.0±65.3 mm Hg, P=0.00). The driving pressure group had lower incidence of postoperative OI<300 mm Hg (16.9% vs. 49.0%, OR=0.21, 95%CI 0.08-0.52, P=0.00) and less severity of OI classification than the control group (P=0.00). The incidence of pulmonary complications at 7 days after surgery was comparable between the driving pressure group and the control group (28.3% vs. 33.9%, OR=0.76, 95%CI 0.33-1.75, P=0.48). The atelectasis rate was lower in the driving pressure group (1.0% vs. 15.0%, OR=0.10, 95%CI 0.01-0.89, P=0.01). Conclusion Application of driving pressure-guided ventilation is associated with a higher OI and less lung injury after ICU admission compared with the conventional protective ventilation in patients having valve surgery.
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Objective:To evaluate the efficacy of artificial intelligence assisted pulmonary nodule diagnosis system in detection pulmonary nodule and predicting the malignant probability of pulmonary nodule.Methods:A retrospectively analyze the clinical data of 199 patients with lung nodules in the Thoracic Surgery Department of Lanzhou University Second Hospital from May 2016 to July 2020. The preoperative chest CT was imported into the artificial intelligence system to record the detected lung nodules, to measure nodal diameter and density classification and malignant probability prediction value of each nodule. The detection rate of pulmonary nodules by artificial intelligence system was calculated, and the sensitivity, specificity, positive likelihood ratio and negative likelihood ratio of artificial intelligence system in the differential diagnosis of benign and malignant pulmonary nodules were calculated and compared with manual film reading. and the sensitivity and specificity in the differential diagnosis of benign and malignant pulmonary nodules under the condition of different size and density of pulmonary nodules.Results:A total of 204 pulmonary nodules were pathologically diagnosed by surgical resection, and the detection rate of pulmonary nodules by artificial intelligence system was 100%. The artificial intelligence system can distinguish benign and malignant pulmonary nodules with a sensitivity of 95.83%(95% CI: 0.8967-0.9883), specificity 25.00%(95% CI: 0.1717-0.3425), and a positive likelihood ratio of 1.27(95% CI: 1.14-1.44), negative likelihood ratio 0.17(95% CI: 0.06-0.46), Manual reading for the differentiation of benign and malignant pulmonary nodules has a sensitivity of 87.36%(95% CI: 0.7850-0.9352), specificity 72.17%(95% CI: 0.6214-0.8079), and a positive likelihood ratio of 3.14(95% CI: 2.26-4.37), the negative likelihood ratio is 0.18(95% CI: 0.10-0.31). 5mm≤diameter of pulmonary nodule<10 mm, sensitivity 100%(95% CI: 0.6637-1.0000), specificity 50.00%(95% CI: 0.01258-0.98740), 10 mm≤diameter of pulmonary nodule <20 mm, sensitivity 94.29%(95% CI: 0.8084-0.9930), specificity 29.83%(95% CI: 0.1843-0.4340), 20 mm≤ diameter of pulmonary nodule ≤30 mm, sensitivity 96.15%(95% CI: 0.8679-0.9953), specificity 18.37%(95% CI: 0.0876-0.9953), sensitivity of subsolid lung nodules: 100%(95% CI: 0.9051-1.0000), specificity 20.00%(95% CI: 0.0051-0.7164), solid lung nodule sensitivity 93.22%(95% CI: 0.8354-0.9812), specificity 25.24%(95% CI: 0.1720-0.3476). Conclusion:The artificial intelligence assistant diagnosis system of pulmonary nodules has a strong performance in the detection of pulmonary nodules, but it can not meet the clinical requirements in the differentiation of benign and malignant pulmonary nodules. At present, the artificial intelligence system can be used as an auxiliary tool for doctors to detect pulmonary nodules and assist in the diagnosis of benign and malignant pulmonary nodules.
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Objective: To study the effects of triptolide-medicated serum on secretory function of adrenocortical cells isolated from rats. Methods: Thirty SD rats were randomly divided into control group, prednisone group, and low-, medium- and high-dose triptolide groups. Rats were administered with normal saline, prednisone and low-, medium- and high-dose triptolide respectively by gastrogavage to prepare sera containing drugs. Primary adrenocortical cells were isolated from normal male rats and cultured with sera containing drug for 48 hours. Expression of proliferating cell nuclear antigen (PCNA) was observed by immunohistochemical method and number of PCNA-positive cells was counted. Ultrastructure of adrenocortical cells was observed under a transmission electron microscope. Content of corticosterone in supernatant of adrenocortical cell culture was detected by enzyme-linked immunosorbent assay, and real-time fluorescence quantitative polymerase chain reaction (PCR) was employed to investigate the expression of 3beta-hydroxysteroid dehydrogenase (3beta-HSD) mRNA. Results: As compared with the control group, content of corticosterone in supernatant of adrenocortical cell culture and expression of 3beta-HSD mRNA were significantly increased in the triptolide-treated groups, and the numbers of PCNA-positive cells were increased in the medium- and high-dose triptolide groups, however, they were decreased in the prednisone group. Conclusion: Triptolide-medicated serum can increase the secretion of corticosterone in rat adrenocortical cells in vitro.