Objective To compare the efficacy of trimodality therapy and chemoradiation therapy (CRT) alone in patients with locally advanced resectable esophageal squamous cell carcinoma (SCC).Methods A total of 124 cases with locally advanced resectable esophageal SCC were retrospectively analyzed and classified into 2 groups.Fifty-four cases in trimodality group were treated with surgery and preoperative chemoradiation,while 70 cases in CRT alone group only received radiation and chemotherapy.Local tumor control,3-year survival and treatment-related mortality were assessed.Results The local recurrent rate of the resected patients was 18.5％ in trimodality group and 35.7％ in CRT alone group,respectively(x2 =4.445,P ＜ 0.05).The 3-year progression-free survival (PFS) was 65.3％ (95％ CI 50.7-80.5) in trimodality group and31.9％ (95％CI 19.6-44.2) in CRT alone group (P＜0.05),while the overall survival (OS) 66.3％ (95％ CI43.0-89.6) and 34.4％ (95％ CI 21.1-47.7),respectively(P ＜ 0.05).Treatment-related mortality was 1.9％ in trimodality group and 2.9％ in CRT alone group (P ＞ 0.05).For CRT alone group,the sub-group analysis showed that there was no statistically significant difference in the 3-year OS between patients who received 50-50.4 Gy and those who received the dose over 50.4 Gy (39.9％ 95％ CI 18.5-61.3 vs.31.5％ 95％ CI 14.8-48.2,P ＞0.05).Conclusions Compared with CRT alone,trimodality therapy showed the superior local control,PFS and OS,with similar treatment-related mortality in the treatment of patients with SCC of esophagus.The role of surgery could not be replaced by CRT alone even with the augment of radiation dose.

Objective:To evaluate the performance of an artificial intelligent (AI)-based automated digital cell morphology analyzer (hereinafter referred as AI morphology analyzer) in detecting peripheral white blood cells (WBCs).Methods:A multi-center study. 1. A total of 3010 venous blood samples were collected from 11 tertiary hospitals nationwide, and 14 types of WBCs were analyzed with the AI morphology analyzers. The pre-classification results were compared with the post-classification results reviewed by senior morphological experts in evaluate the accuracy, sensitivity, specificity, and agreement of the AI morphology analyzers on the WBC pre-classification. 2. 400 blood samples (no less than 50% of the samples with abnormal WBCs after pre-classification and manual review) were selected from 3 010 samples, and the morphologists conducted manual microscopic examinations to differentiate different types of WBCs. The correlation between the post-classification and the manual microscopic examination results was analyzed. 3. Blood samples of patients diagnosed with lymphoma, acute lymphoblastic leukemia, acute myeloid leukemia, myelodysplastic syndrome, or myeloproliferative neoplasms were selected from the 3 010 blood samples. The performance of the AI morphology analyzers in these five hematological malignancies was evaluated by comparing the pre-classification and post-classification results. Cohen′s kappa test was used to analyze the consistency of WBC pre-classification and expert audit results, and Passing-Bablock regression analysis was used for comparison test, and accuracy, sensitivity, specificity, and agreement were calculated according to the formula.Results:1. AI morphology analyzers can pre-classify 14 types of WBCs and nucleated red blood cells. Compared with the post-classification results reviewed by senior morphological experts, the pre-classification accuracy of total WBCs reached 97.97%, of which the pre-classification accuracies of normal WBCs and abnormal WBCs were more than 96% and 87%, respectively. 2. The post-classification results reviewed by senior morphological experts correlated well with the manual differential results for all types of WBCs and nucleated red blood cells (neutrophils, lymphocytes, monocytes, eosinophils, basophils, immature granulocytes, blast cells, nucleated erythrocytes and malignant cells r>0.90 respectively, reactive lymphocytes r=0.85). With reference, the positive smear of abnormal cell types defined by The International Consensus Group for Hematology, the AI morphology analyzer has the similar screening ability for abnormal WBC samples as the manual microscopic examination. 3. For the blood samples with malignant hematologic diseases, the AI morphology analyzers showed accuracies higher than 84% on blast cells pre-classification, and the sensitivities were higher than 94%. In acute myeloid leukemia, the sensitivity of abnormal promyelocytes pre-classification exceeded 95%. Conclusion:The AI morphology analyzer showed high pre-classification accuracies and sensitivities on all types of leukocytes in peripheral blood when comparing with the post-classification results reviewed by experts. The post-classification results also showed a good correlation with the manual differential results. The AI morphology analyzer provides an efficient adjunctive white blood cell detection method for screening malignant hematological diseases.

Objective: To establish a set of rules for autoverification of blood analysis, in order to provide a way to validate autoverification rules for different analytical systems, which can ensure the accuracy of test results as well as shorten turnaround time (TAT) of test reports. Methods: A total of 34 629 EDTA-K2 anticoagulated blood samples were collected from multicenter cooperative units including the First Hospital of Jinlin University during January 2017 to November 2017. These samples included: 3 478 cases in Autoverification Establishment Group, including 288 cases for Delta check rules; 5 362 cases in Autoverification Validation Group, including 2 494 cases for Delta check; 25 789 cases in Clinical Application Trial Group. All these samples were analyzed for blood routine tests using Sysmex XN series automatic blood analyzers.Blood smears, staining and microscopic examination were done for each sample; then the clinical information, instrument parameters, test results and microscopic results were summarized; screening and determination of autoverification conditions including parameters and cutoff values were done using statistical analysis. The autoverification rules were input into Sysmex Laboman software and undergone stage Ⅰ validation using simulated data, and stage Ⅱ validation for post-analytical samples successively. True negative, false negative, true positive, false positive, autoverification pass rate and passing accuracy were calculated. Autoverification rules were applied to autoverification blood routine results and missed detection rates were validated, and also data of autoverification pass rate and TAT were obtained. Results: (1)The selected autoverification conditions and cutoff values included 43 rules involving WBC, RBC, PLT, Delta check and abnormal characteristics. (2)Validation of 3 190 cases in Autoverification Establishment Group showed the false negative rate was 1.94%(62/3 190)(P<0.001), autoverification pass rate was 76.74%, passing accuracy was 97.47%; Validation of 2 868 cases in Autoverification Validation Group, the false negative rate was 3.38%(97/2 868)(P=0.002), autoverification pass rate was 42.26%, passing accuracy was 92.00%; Validation of Delta check on 288 cases in Autoverification Establishment Group and 2 494 cases in Autoverification Validation Group showed the false negative rates were respectively 1.39% and 2.61%(P<0.001). (3)Three hospitals adopted these rules of autoverification for 25 789 blood routine samples, and found that the average TAT of blood routine test reports were shortened by 24min, 32min and 7min respectively, the rate of samples reported within 30min were elevated by 33%, 53% and 7%. The autoverification pass rates were 72%-74%. Conclusions The application of this set of 43 autoverification rules in blood sample analysis can ensure test quality while shortenTAT and improve work efficiency. It is worth pointing out that for the same analytical systems in this research, validation is necessary before application of this set of rules, and periodic validation is required during application to make necessary adjustment; for different analytical systems, as this research provide a way to establish autoverification rules for blood routine tests.Clinical labs may establish their own suitable autoverification rules on the basis of technological parameters. (Chin J Lab Med, 2018, 41: 601-607)

To explore the pharmacogenomic markers that affect the platinum-based chemotherapy response in non-small-cell lung carcinoma (NSCLC), we performed a two-cohort of genome-wide association studies (GWAS), including 34 for WES-based and 433 for microarray-based analyses, as well as two independent validation cohorts. After integrating the results of two studies, the genetic variations related to the platinum-based chemotherapy response were further determined by fine-mapping in 838 samples, and their potential functional impact were investigated by eQTL analysis and in vitro cell experiments. We found that a total of 68 variations were significant at P < 1 × 10^-3 in cohort 1 discovery stage, of which 3 SNPs were verified in 262 independent samples. A total of 541 SNPs were significant at P < 1 × 10^-4 in cohort 2 discovery stage, of which 8 SNPs were verified in 347 independent samples. Comparing the validated SNPs in two GWAS, ADCY1 gene was verified in both independent studies. The results of fine-mapping showed that the G allele carriers of ADCY1 rs2280496 and C allele carriers of rs189178649 were more likely to be resistant to platinum-based chemotherapy. In conclusion, our study found that rs2280496 and rs189178649 in ADCY1 gene were associated the sensitivity of platinum-based chemotherapy in NSCLC patients.