Relationship between DDX46 gene and invasion and metastasis behaviors of TE-1 esophageal squamous cell carcinoma cells / 中国胸心血管外科临床杂志

@#Objective    To investigate the relationship between DDX46 genes and invasion and migration of esophageal squamous cell carcinoma cells. Methods    Human esophageal squamous cell carcinoma cells TE-1 were transfected by fluorescent marker shRNA lentivirus (shDDX46 group), and an empty vector was transfected as a control (shCtrl group). The expression rate of green fluorescent protein under the microscope was used to evaluate the cell transfection efficiency. Real-time fluorescence quantitative polymerase chain reaction (RTFQ-PCR) and Western blotting (WB) detected the knockdown efficiency of the target gene at the mRNA and protein expression levels. Wound healing, invasion assay and migration assay detected the changes of invasion and metastasis ability. Classical pathway analysis was used to explore signaling pathway changes and the possible mechanism of DDX46 in the invasion and metastasis was explored by detecting fibronectin expression. Results    DDX46 gene at mRNA and protein levels was significantly inhibited after lentiviral transfection. Wound healing showed that after 8 h the cell mobility of TE-1 cells decreased significantly (P=0.001). Invasion assay showed that after 24 h the average cell metastasis rate of TE-1 cells was lower in the shDDX46 group than that in the shCtrl group (P<0.001). The cell metastasis rate in the shDDX46 group corresponding to observation points in the transwell assay was lower than that in the shCtrl group (P<0.001) after 24 h culture. The results of the classical pathway analysis showed that the integrin signaling pathway activity was inhibited, further exploration of the mechanism of action found that the expression of fibronectin associated with cell adhesion was decreased. Conclusion    DDX46 gene is related to the invasion and migration ability of esophageal squamous cell carcinoma cells. Knockdown of DDX46 genes may reduce cell adhesion by downregulating the integrin pathway signaling.

Clinical Study of Artificial Intelligence-assisted Diagnosis System in Predicting the Invasive Subtypes of Early-stage Lung Adenocarcinoma Appearing as Pulmonary Nodules / 中国肺癌杂志

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.

Expression of PD-1/PD-L1 in esophageal squamous cell carcinoma and its relationship with survival prognosis: A systematic review and meta-analysis / 中国胸心血管外科临床杂志

@# 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.

Efficacy of surgical management for esophageal squamous cell carcinoma in pathological stage T1b / 中国胸心血管外科临床杂志

@#Objective    To investigate the prognostic survival status and influence factors for surgical treatment of esophageal squamous cell carcinoma (ESCC) in pathological stage T1b (pT1b). Methods    The patients with ESCC in pT1b undergoing Ivor-Lewis or McKeown esophagectomy in Lanzhou University Second Hospital from 2012 to 2015 were collected, including 78 males (78.3%) and 17 females (21.7%) with an average age of 61.4±7.4 years. Results    The most common postoperative complications were pneumonia (15.8%), anastomotic leakage (12.6%) and arrhythmia (8.4%). Ninety-three (97.9%) patients underwent R0 resection, with an average number of lymph node dissections of 14.4±5.6. The rate of lymph node metastasis was 22.1%, and the incidence of lymph vessel invasion was 13.7%. The median follow-up time was 60.4 months, during which 25 patients died and 27 patients relapsed. The overall survival rate at 3 years was 86.3%, and at 5 years was 72.7%. Multivariate Cox regression analysis showed that lymph node metastasis (P=0.012, HR=2.60, 95%CI 1.23-5.50) and lympovascular invasion (P=0.014, HR=2.73, 95%CI 1.22-6.09) were independent risk factors for overall survival of pT1b ESCC. Conclusion    Esophagectomy via right chest approach combined with two-fields lymphadenectomy is safe and feasible for patients with pT1b ESCC. The progress of pT1b ESCC with lymph node metastasis or lymphovascular invasion is relatively poor.

Influencing factors for prognosis of primary tracheal malignancy and establishment of nomogram model for predicting its overall survival based upon SEER database / 中国胸心血管外科临床杂志

@#Objective    To analyze the factors affecting the prognosis of patients with primary tracheal malignancy, and establish a nomogram model for prediction its prognosis. Methods    A total of 557 patients diagnosed with primary tracheal malignancy from 1975 to 2016 in the Surveillance, Epidemiology, and End Results Data were collected. The factors affecting the overall survival rate of primary tracheal malignancy were screened and modeled by univariate and multivariate Cox regression analysis. The nomogram prediction model was performed by R 3.6.2 software. Using the C-index, calibration curves and receiver operating characteristic (ROC) curve to evaluate the consistency and predictive ability of the nomogram prediction model. Results    The median survival time of 557 patients with primary tracheal malignancy was 21 months, and overall survival rates of the 1-year, 3-year and 5-year were 59.1%±2.1%, 42.5%±2.1%, and 35.4%±2.2%. Univariate and multivariate Cox regression analysis showed that age, histology, surgery, radiotherapy, tumor size, tumor extension and the range of lymph node involvement were independent risk factors affecting the prognosis of patients with primary tracheal malignancy (P<0.05). Based on the above 7 risk factors to establish the nomogram prediction model, the C-index was 0.775 (95%CI 0.751-0.799). The calibration curve showed that the prediction model established in this study had a good agreement with the actual survival rate of the 1 year, 3 year and 5 years. The area under curve of 1-year, 3-year and 5-year predicting overall survival rates was 0.837, 0.827 and 0.836, which showed that the model had a high predictive power. Conclusion    The nomogram prediction model established in this study has a good predictive ability, high discrimination and accuracy, and high clinical value. It is useful for the screening of high-risk groups and the formulation of personalized diagnosis and treatment plans, and can be used as an evaluation tool for prognostic monitoring of patients with primary tracheal malignancy.

Study on the application of artificial intelligence system in the detection and differentiation of benign and malignant pulmonary nodules / 中华胸心血管外科杂志

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.