Objective To investigate the preoperative prediction of the expression level of programmed cell death ligand 1(PD-L1)in non-small cell lung cancer(NSCLC)by a nomogram model constructed with clinical data,conventional CT signs and spectral CT parameters.Methods A retrospective analysis was conducted on 52 patients with pathologically confirmed NSCLC and undergoing preoperative spectral CT examination.The patients were categorized into positive and negative groups according to PD-L1 expression level,and their clinical data,conventional CT signs and spectral CT parameters were collected.Specifically,clinical data included gender,age,Ki-67 and tumor markers;conventional CT signs included tumor density,margins,calcification,spiculation,lobulation,pleural indentation and cavitation;and spectral CT parameters measured in the arterial and venous phases included effective atomic number(Eff-Z),iodine concentration(IC),water concentration(WC)and normalized iodine concentration(NIC).Intergroup differences were analyzed,and multivariate Logistic regression was used to identify independent predictors and establish the prediction model which was evaluated for prediction performance and accuracy using receiver operating characteristic(ROC)curves,calibration curve and decision curve analyses.Results For clinical data,only the difference in gender between two groups had statistical significance(P<0.05).The spectral CT parameters(IC,NIC and Eff-Z)in the arterial and venous phases of PD-L1 positive group were all greater than those of PD-L1 negative group,with statistically significant differences(P<0.05).Multivariate Logistic regression analysis identified gender(P=0.024),venous-phase Eff-Z(P=0.002),and venous-phase IC(P=0.003)as independent predictive factors for PD-L1 expression.The nomogram prediction model constructed with these independent predictors had an area under curve of 0.80,a sensitivity of 88.00%,and a specificity of 59.00%.The calibration curve showed that the predicted values had a high consistency with the actual values.The decision curve revealed that when the high-risk threshold was between 0.10 and 0.83,the model could achieve the maximum net benefit.Conclusion The nomogram model constructed with spectral CT parameters and clinical data has certain value in predicting the expression level of PD-L1 in NSCLC.

Objective To investigate the preoperative prediction of the expression level of programmed cell death ligand 1(PD-L1)in non-small cell lung cancer(NSCLC)by a nomogram model constructed with clinical data,conventional CT signs and spectral CT parameters.Methods A retrospective analysis was conducted on 52 patients with pathologically confirmed NSCLC and undergoing preoperative spectral CT examination.The patients were categorized into positive and negative groups according to PD-L1 expression level,and their clinical data,conventional CT signs and spectral CT parameters were collected.Specifically,clinical data included gender,age,Ki-67 and tumor markers;conventional CT signs included tumor density,margins,calcification,spiculation,lobulation,pleural indentation and cavitation;and spectral CT parameters measured in the arterial and venous phases included effective atomic number(Eff-Z),iodine concentration(IC),water concentration(WC)and normalized iodine concentration(NIC).Intergroup differences were analyzed,and multivariate Logistic regression was used to identify independent predictors and establish the prediction model which was evaluated for prediction performance and accuracy using receiver operating characteristic(ROC)curves,calibration curve and decision curve analyses.Results For clinical data,only the difference in gender between two groups had statistical significance(P<0.05).The spectral CT parameters(IC,NIC and Eff-Z)in the arterial and venous phases of PD-L1 positive group were all greater than those of PD-L1 negative group,with statistically significant differences(P<0.05).Multivariate Logistic regression analysis identified gender(P=0.024),venous-phase Eff-Z(P=0.002),and venous-phase IC(P=0.003)as independent predictive factors for PD-L1 expression.The nomogram prediction model constructed with these independent predictors had an area under curve of 0.80,a sensitivity of 88.00%,and a specificity of 59.00%.The calibration curve showed that the predicted values had a high consistency with the actual values.The decision curve revealed that when the high-risk threshold was between 0.10 and 0.83,the model could achieve the maximum net benefit.Conclusion The nomogram model constructed with spectral CT parameters and clinical data has certain value in predicting the expression level of PD-L1 in NSCLC.

Objective:To investigate the application value of machine learning algorithms and COX nomogram in the survival prediction of hepatocellular carcinoma (HCC) after resection.Methods:The retrospective and descriptive study was conducted. The clinicopathological data of 375 patients with HCC who underwent radical resection in the Cancer Hospital of Chinese Academy of Medical Sciences and Peking Union Medical College from January 2012 to January 2017 were collected. There were 304 males and 71 females, aged from 21 to 79 years, with a median age of 57 years. According to the random numbers showed in the computer, 375 patients were divided into training dataset consisting of 300 patients and validation dataset consisting of 75 patients, with a ratio of 8∶2. Machine learning algorithms including logistic regression (LR), supporting vector machine (SVM), decision tree (DT), random forest (RF), and artificial neural network (ANN) were used to construct survival prediction models for HCC after resection, so as to identify the optimal machine learning algorithm prediction model. A COX nomogram prediction model for predicting postoperative survival in patients with HCC was also constructed. Comparison of performance for predicting postoperative survival of HCC patients was conducted between the optimal machine learning algorithm prediction model and the COX nomogram prediction model. Observation indicators: (1) analysis of clinicopathological data of patients in the training dataset and validation dataset; (2) follow-up and survival of patients in the training dataset and validation dataset; (3) construction and evaluation of machine learning algorithm prediction models; (4) construction and evaluation of COX nomogram prediction model; (5) evaluation of prediction performance between RF machine learning algorithm prediction model and COX nomogram prediction model. Follow-up was performed using outpatient examination or telephone interview to detect survival of patients up to December 2019 or death. Measurement data with normal distribution were expressed as Mean± SD, and comparison between groups was analyzed by the paired t test. Measurement data with skewed distribution were expressed as M ( P25, P75) or M (range), and comparison between groups was analyzed by the Mann-Whitney U test. Count data were represented as absolute numbers. Comparison between groups was performed using the chi-square test when Tmin ≥5 and N ≥40, using the calibration chi-square test when 1≤ Tmin ≤5 and N ≥40, and using Fisher exact probability when Tmin <1 or N <40. The Kaplan-Meier method was used to calculate survival rate and draw survival curve. The COX proportional hazard model was used for univariate analysis, and variables with P<0.2 were included for the Lasso regression analysis. According to the lambda value, variables affecting prognosis were screened for COX proportional hazard model to perform multivariate analysis. Results:(1) Analysis of clinicopathological data of patients in the training dataset and validation dataset: cases without microvascular invasion or with microvascular invasion, cases without liver cirrhosis or with liver cirrhosis of the training dataset were 292, 8, 105, 195, respectively, versus 69, 6, 37, 38 of the validation dataset, showing significant differences between the two groups ( χ2=4.749, 5.239, P<0.05). (2) Follow-up and survival of patients in the training dataset and validation dataset: all the 375 patients received follow-up. The 300 patients in the training dataset were followed up for 1.1-85.5 months, with a median follow-up time of 50.3 months. Seventy-five patients in the validation dataset were followed up for 1.0-85.7 months, with a median follow-up time of 46.7 months. The postoperative 1-, 3-year overall survival rates of the 375 patients were 91.7%, 79.5%. The postoperative 1-, 3-year overall survival rates of the training dataset were 92.0%, 79.7%, versus 90.7%, 81.9% of the validation dataset, showing no significant difference in postoperative survival between the two groups ( χ2=0.113, P>0.05). (3) Construction and evaluation of machine learning algorithm prediction models. ① Selection of the optimal machine learning algorithm prediction model: according to information divergence of variables for prediction of 3 years postoperative survival of HCC, five machine learning algorithms were used to comprehensively rank the variables of clinicopathological factors of HCC, including LR, SVM, DT, RF, and ANN. The main predictive factors were screened out, as hepatitis B e antigen (HBeAg), surgical procedure, maximum tumor diameter, perioperative blood transfusion, liver capsule invasion, and liver segment Ⅳ invasion. The rank sequence 3, 6, 9, 12, 15, 18, 21, 24, 27, 29 variables of predictive factors were introduced into 5 machine learning algorithms in turn. The results showed that the area under curve (AUC) of the receiver operating charateristic curve of LR, SVM, DT, and RF machine learning algorithm prediction models tended to be stable when 9 variables are introduced. When more than 12 variables were introduced, the AUC of ANN machine learning algorithm prediction model fluctuated significantly, the stability of AUC of LR and SVM machine learning algorithm prediction models continued to improve, and the AUC of RF machine learning algorithm prediction model was nearly 0.990, suggesting RF machine learning algorithm prediction model as the optimal machine learning algorithm prediction model. ② Optimization and evaluation of RF machine learning algorithm prediction model: 29 variables of predictive factors were sequentially introduced into the RF machine learning algorithm to construct the optimal RF machine learning algorithm prediction model in the training dataset. The results showed that when 10 variables were introduced, results of grid search method showed 4 as the optimal number of nodes in DT, and 1 000 as the optimal number of DT. When the number of introduced variables were not less than 10, the AUC of RF machine learning algorithm prediction model was about 0.990. When 10 variables were introduced, the RF machine learning algorithm prediction model had an AUC of 0.992 for postoperative overall survival of 3 years, a sensitivity of 0.629, a specificity of 0.996 in the training dataset, an AUC of 0.723 for postoperative overall survival of 3 years, a sensitivity of 0.177, a specificity of 0.948 in the validation dataset. (4) Construction and evaluation of COX nomogram prediction model. ① Analysis of postoperative survival factors of HCC patients in the training dataset. Results of univariate analysis showed that HBeAg, alpha fetoprotein (AFP), preoperative blood transfusion, maximum tumor diameter, liver capsule invasion, and degree of tumor differentiation were related factors for postoperative survival of HCC patients [ hazard ratio ( HR)=1.958, 1.878, 2.170, 1.188, 2.052, 0.222, 95% confidence interval ( CI): 1.185-3.235, 1.147-3.076, 1.389-3.393, 1.092-1.291, 1.240-3.395, 0.070-0.703, P<0.05]. Clinico-pathological data with P<0.2 were included for Lasso regression analysis, and the results showed that age, HBeAg, AFP, surgical procedure, perioperative blood transfusion, maximum tumor diameter, tumor located at liver segment Ⅴ or Ⅷ, liver capsule invasion, and degree of tumor differentiation as high differentiation, moderate-high differentiation, moderate differentiation, moderate-low differentiation were related factors for postoperative survival of HCC patients. The above factors were included for further multivariate COX analysis, and the results showed that HBeAg, surgical procedure, maximum tumor diameter were independent factors affecting postoperative survival of HCC patients ( HR=1.770, 8.799, 1.142, 95% CI: 1.049- 2.987, 1.203-64.342, 1.051-1.242, P<0.05). ② Construction and evaluation of COX nomogram prediction model: the clinicopathological factors of P≤0.1 in the COX multivariate analysis were induced to Rstudio software and rms software package to construct COX nomogram prediction model in the training dataset. The COX nomogram prediction model for predicting postoperative overall survival had an consistency index of 0.723 (se=0.028), an AUC of 0.760 for postoperative overall survival of 3 years in the training dataset, an AUC of 0.795 for postoperative overall survival of 3 years in the validation dataset. The verification of the calibration plot in the training dataset showed that the COX nomogram prediction model had a good prediction performance for postoperative survival. COX nomogram score=0.627 06×HBeAg (normal=0, abnormal=1)+ 0.134 34×maximum tumor diameter (cm)+ 2.107 58×surgical procedure (laparoscopy=0, laparotomy=1)+ 0.545 58×perioperative blood transfusion (without blood transfusion=0, with blood transfusion=1)-1.421 33×high differentiation (non-high differentiation=0, high differentiation=1). The COX nomogram risk scores of all patients were calculated. Xtile software was used to find the optimal threshold of COX nomogram risk scores. Patients with risk scores ≥2.9 were assigned into high risk group, and patients with risk scores <2.9 were assigned into low risk group. Results of Kaplan-Meier overall survival curve showed a significant difference in the postoperative overall survival between low risk group and high risk group of the training dataset ( χ2=33.065, P<0.05). There was a significant difference in the postoperative overall survival between low risk group and high risk group of the validation dataset ( χ2=6.585, P<0.05). Results of further analysis by the decision-making curve showed that COX nomogram prediction model based on the combination of HBeAg, surgical procedure, perioperative blood transfusion, maximum tumor diameter, and degree of tumor differentiation was superior to any of the above individual factors in prediction performance. (5) Evaluation of prediction performance between RF machine learning algorithm prediction model and COX nomogram prediction model: prediction difference between two models was investigated by analyzing maximun tumor diameter (the important variable shared in both models), and by comparing the predictive error curve of both models. The results showed that the postoperative 3-year survival rates predicted by RF machine learning algorithm prediction model and COX nomogram prediction model were 77.17% and 74.77% respectively for tumor with maximum diameter of 2.2 cm ( χ2=0.182, P>0.05), 57.51% and 61.65% for tumor with maximum diameter of 6.3 cm ( χ2=0.394, P>0.05), 51.03% and 27.52% for tumor with maximum diameter of 14.2 cm ( χ2=12.762, P<0.05). With the increase of the maximum tumor diameter, the difference in survival rates predicted between the two models turned larger. In the validation dataset, the AUC for postoperative overall survival of 3 years of RF machine learning algorithm prediction model and COX nomogram prediction model was 0.723 and 0.795, showing a significant difference between the two models ( t=3.353, P<0.05). Resluts of Bootstrap cross-validation for prediction error showed that the integrated Brier scores of RF machine learning algorithm prediction model and COX nomogram prediction model for predicting 3-year survival were 0.139 and 0.134, respectively. The prediction error of COX nomogram prediction model was lower than that of RF machine learning algorithm prediction model. Conclusion:Compared with machine learning algorithm prediction models, the COX nomogram prediction model performs better in predicting 3 years postoperative survival of HCC, with fewer variables, which is easy for clinical use.

Objective:To investigate the definition and influencing factors of early recurrence after resection for synchronous colorectal cancer liver metastases (sCRLM).Methods:Patients with sCRLM in Department of Hepatobiliary Surgery, Cancer Hospital, Chinese Academy of Medical Sciences from December 2008 to December 2016 were included. Restricted cubic spline was used to determine the correlations between the time of recurrence and the long-term prognosis. The univariable and multivariable Cox was performed to measure the feasibility of recurrence within 6 months as the early recurrence. Then apply logistic regression, support vector machine, decision tree, random forest, artificial neural network and XGBoost, these machine learning algorithm to comprehensively rank the importance of every clinicopathological variable to early recurrence, and according to the comprehensively ranks, we introduced variables into the multivariable logistic regression model and observed the receiver operating characteristic curve (ROC) of the logistic regression model, based on the ROC area under curve, Akaike information criterion, and Bayesian information criterion, we identified the best performed variable combination and introduced them into the multivariate logistic regression analysis to confirm the independent risk factors for early recurrence. Subsequently, inverse probability weighting (IPTW) was performed on the therapy-associated independent risk factor to evaluate and validate its influence on the early recurrence of sCRLM patients after reducing the standardized mean difference of all covariates.Results:A total of 228 sCRLM patients who received resection were enrolled and followed up from 2.10 to 108.57 months. There were 142 males and 86 females, aged (55.89±0.67) years old. In 170 (74.6%) patients with recurrence, restricted cube analysis determined that the hazard ratio (HR) of disease free survival (DFS) and overall survival (OS) satisfies a linear relationship ( P<0.05), and Cox analysis indicated that 6 months as the time cutoff for defining early recurrence was feasible ( HR=3.405, 95% CI: 2.098-5.526, P<0.05). Early recurrence was occurred in 93 (40.79%) patients. The survival rate of patients in early recurrence group was significantly lower than that in the late recurrence group ( HR=3.405, 95% CI: 2.098-5.526, P<0.05, and the 5-year survival rate was 14.0% vs 52.0%). Comprehensive analysis of 6 machine learning algorithms identified that the total number of lymph node dissection >22 ( OR=0.258, 95% CI: 0.132-0.506, P<0.05) is an independent protective factor for early recurrence, while the number of liver metastases>3 ( OR=4.715, 95% CI: 2.467-9.011, P<0.05) and postoperative complications ( OR=2.334, 95% CI: 1.269-4.291, P<0.05) are independent risk factors. Finally, the IPTW analysis fully reduced the influence of covariate confounding influence via causal inference to prove lymph node dissection associated with early recurrence (IPTW OR=0.29, P<0.05), benefiting the DFS (IPTW HR=0.4887, P<0.05), but without influence on OS (IPTW HR=0.6951, P>0.05). Conclusion:Six months after sCRLM as the definition of early recurrence, it has significant feasibility. The long-term survival of patients with early recurrence is poor. The independent influencing factors of early recurrence after sCRLM are the total number of lymph node dissection, the number of liver metastases and postoperative complications disease.

Objective:To compare the safety and outcome between total laparoscopic and laparoscopy-assisted synchronous resection for colorectal cancer patients with liver metastases.Methods:The data of patients who underwent total laparoscopic or laparoscopy-assisted simultaneous resection of primary colorectal cancer and liver metastases in our hospital between December 2008 and December 2016 were collected and analyzed. The total laparoscopic surgery patients were matched 1∶2 to the laparoscopy-assisted surgery patients based on the propensity score. 22 patients were classified in the total laparoscopic group and 44 patients were classified in the laparoscopy-assisted group. The intraoperative conditions and postoperative outcomes of the two groups were compared.Results:There was no difference in the preoperative baseline data between the two groups ( P>0.05). The median operative time were 317.50 and 267.50 minutes in the total laparoscopic group and the laparoscopy-assisted group, respectively, and the median intraoperative blood loss were 100 and 200 ml, both with no statistically significant differences ( P>0.05). There were 1 case of intraoperative blood transfusion in the total laparoscopic group and 5 cases in the laparoscopy-assisted group, with no statistically significant difference ( P=0.650). The median postoperative hospital stay in the two groups were 11.0 and 10.0 days, the median postoperative defecation time were 4.0 and 4.0 days and postoperative complication rates were 13.6% and 20.5%, and none of these differences were statistically significant ( P>0.05). However, no Clavien-DindoⅡ level and above complications occurred in total laparoscopic group. The median disease-free survival (DFS) were 15.0 and 15.7 months in the total laparoscopic group and the laparoscopy-assisted group, the overall survival (OS) were 25.9 and 37.6 months, respectively, with no statistically significant differences ( P>0.05). Conclusion:Laparoscopy-assisted approaches are similar, so the appropriate approach should be chosen according to the clinical condition and surgeon′s experience.

Objective:To compare the safety and outcome between total laparoscopic and laparoscopy-assisted synchronous resection for colorectal cancer patients with liver metastases.Methods:The data of patients who underwent total laparoscopic or laparoscopy-assisted simultaneous resection of primary colorectal cancer and liver metastases in our hospital between December 2008 and December 2016 were collected and analyzed. The total laparoscopic surgery patients were matched 1∶2 to the laparoscopy-assisted surgery patients based on the propensity score. 22 patients were classified in the total laparoscopic group and 44 patients were classified in the laparoscopy-assisted group. The intraoperative conditions and postoperative outcomes of the two groups were compared.Results:There was no difference in the preoperative baseline data between the two groups ( P>0.05). The median operative time were 317.50 and 267.50 minutes in the total laparoscopic group and the laparoscopy-assisted group, respectively, and the median intraoperative blood loss were 100 and 200 ml, both with no statistically significant differences ( P>0.05). There were 1 case of intraoperative blood transfusion in the total laparoscopic group and 5 cases in the laparoscopy-assisted group, with no statistically significant difference ( P=0.650). The median postoperative hospital stay in the two groups were 11.0 and 10.0 days, the median postoperative defecation time were 4.0 and 4.0 days and postoperative complication rates were 13.6% and 20.5%, and none of these differences were statistically significant ( P>0.05). However, no Clavien-DindoⅡ level and above complications occurred in total laparoscopic group. The median disease-free survival (DFS) were 15.0 and 15.7 months in the total laparoscopic group and the laparoscopy-assisted group, the overall survival (OS) were 25.9 and 37.6 months, respectively, with no statistically significant differences ( P>0.05). Conclusion:Laparoscopy-assisted approaches are similar, so the appropriate approach should be chosen according to the clinical condition and surgeon′s experience.

OBJECTIVETo screen for carriers of SMN1 gene mutation, which underlies spinal muscular atrophy (SMA), in 4931 pregnant women from Liuzhou region of Guangxi, and to determine the carrier rate.

METHODSCombined denaturing high-performance liquid chromatography (DHPLC) and multiple PCR techniques were used to detect the copy number of SMN1 gene. The carrier frequency was calculated. The spouse of the carrier was also screened, and prenatal diagnosis was provided to the couples who were both positive.

RESULTSAmong the 4931 pregnant women, 61 were found to harbor only one copy of the SMN1 gene, which yielded a carrier rate of 1.2%. Subsequent testing has identified 1 fetus carrying homozygous deletions of the SMN1 gene.

CONCLUSIONThe carrier rate of SMA mutation in Liuzhou region is slightly lower than that of other regions of southern China. DHPLC can effectively screen the carriers of SMA mutation and provide a basis for genetic counseling and prenatal diagnosis.

Objective: To analyze the relationship between inflammatory factors and relevant risk factors in patients of essential hypertension (EH) combining acute coronary syndrome (ACS) with its clinical significance. Methods: Our research included 3 groups: EH group, n=79 patients with standard criteria, EH+ACS group, n=85 and Control group, n=48 normal subjects. Blood levels of lipoprotein-associated phospholipase A2 (Lp-PLA2), tryptase (TPS) and relevant clinical, biochemical parameters were measured; risk factors for cardiovascular disease were examined and the relationship between above parameters, risk factors and ACS occurrence in EH patients was studied by Logistic regression analysis. Results: The OR values were all greater than 1 in fibrinogen (Fbg), high-sensitivity C-reactive protein (hs-CRP), TPS, atherosclerotic plaque, Lp-PLA2 and EH grading. Fbg was the most significant independent risk factor (OR=22.242, 95% CI 6.458-76.609, P<0.0001), the standardized partial regression coefficient b'as absolute value (b') was 1.079 which was the highestone in above 6 variables with the strongest impact for ACS occurrence in EH patients. Conclusion: Fbg, hs-CRP, TPS, atherosclerotic plaque and EH grading were the independent risk factors for ACS occurrence in EH patients; Fbg was the highest risk factor for ACS occurrence with the strongest impact, which provided a new direction for ACS prevention and treatment.

Objective To explore the predictive ability of magnetic resonance spectroscopy (MRS) in overall survival (OS) and progression-free survival (PFS) of patients with glioblastoma multiforme (GBM) before,during,and 2 months after radiotherapy with concomitant/adjuvant temozolomide (TMZ).Methods GBM patients,admitted to our hospital from January 2011 to January 2016 and confirmed by pathology,were chosen in our study;all patients underwent postoperative three-dimensional conformal radiotherapy with concomitant/adjuvant TMZ.And 3D-MRS was performed before,during,and 2 months after radiotherapy,the levels of N-acetyl-aspartic acid (NAA),choline (Cho) and creatine (Cr),and ratios of Cho/NAA,Cho/Cr and NAA/Cr in the GBM/edge of surgery side and the normal brain tissues were observed.The survival curve,median overall survival (mOS) and median progression free survival (mPFS) of patients with standardized Cho decreased＜30％ and patients with standardized Cho decreased＞30％ 2 months after radiotherapy were compared.Results Twenty-one patients finished the scheduled MRS for 3 times.Until the end of our study,16 patients died and 5 survived.Standardized Cho gradually decreased before,during,and 2 months after radiotherapy (2.08±0.22,1.45 ±0.21 and 1.16±0.18),with significant differences (P＜0.05).Standardized Cho after radiotherapy was significantly decreased as compared with that before radiotherapy (P＜0.05).Ratios of Cho/NAA and Cho/Cr in the GBM/edge of surgery side were significantly higher than those in the normal brain tissues (P＜0.05),and ratio of NAA/Cr in the GBM/edge of surgery side was significantly lower than that in the normal brain tissues (P＜0.05).Ratio of Cho/NAA gradually decreased before,during,and 2 months after radiotherapy,with significant differences (P＜0.05).As compared with patients with standardized Cho decreased＜30％ 2 months after radiotherapy,patients with standardized Cho decreased＞30％ 2 months after radiotherapy had significantly decreased rates of OS and PFS,and statistically shorter mPFS and mOS (4.5 vs.13.5,10.9 vs.25.3,P＜0.05).Conclusion The changes of standardized Cho 2 months after radiotherapy have high prognostic significance for PFS and OS.

Esophagus ; pathology ; Foreign Bodies ; Hoarseness ; diagnosis ; Humans