ABSTRACT
@# 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.
ABSTRACT
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.