| Objective:The N1 lymph nodes defined in the TNM staging of NSCLC include peribronchial lymph nodes,intrapulmonary lymph nodes,and hilar lymph nodes.The accurate assessment of N1 lymph nodes plays an increasingly important role in refining the staging of NSCLC patients and predicting their prognosis.In the 8th edition of the international TNM staging recommendations,the committee attempted to subcategorize N staging.Although the categorization was not included in the official8 th edition of TNM staging as the given division being unable to distinguish the prognosis of different groups,it was not included in the official 8th edition of TNM staging.More and more scholars are paying attention to the N stage,especially the N1 stage,and the impact of this grouping on patient prognosis.Accurate assessment of N1 lymph nodes not only affects the staging accuracy of patients with N1 stage,but also patients with N2a1 stage(skip mediastinal lymph node metastases)mentioned in the N staging recommendations are also associated with detailed assessment of N1 lymph nodes.In the first chapter of this article,we focus on two categories of patients with non-small cell lung cancer determined by the accuracy of N1 lymph node assessment: patients with pathologic stage N1 and patients with skip lymph node metastases.The important value of accurate N1 lymph node assessment for both categories of patients,the potential subgroup approach for N1 lymph nodes,and a comparison of the prognosis of the two categories of patients separately are discussed.Since the N1 lymph node delineation approach in the staging recommendations does not well differentiate the prognosis among patients with different N1 subgroups and between N1 and N2 patients,the second chapter of this paper explores potential N1 lymph node subgrouping approaches through a retrospective cohort study to provide some reference for subsequent optimization of TNM staging.The accurate assessment of N1 lymph nodes,especially the detection of intrapulmonary lymph nodes among them,requires the full cooperation of surgeons and pathologists.The thorough detection of intrapulmonary lymph nodes is timeconsuming and difficult to achieve in clinical practice.In Chapter 3 of this paper,we attempt to combine preoperative tumor markers,CT imaging features,and clinicopathological features of patients to build predictive models based on multiple machine learning algorithms to identify high-risk patients preoperatively,and facilitate the targeted meticulous and thorough intrapulmonary lymph node detection for such high-risk patients with N1 lymph node metastasis in the future.International guidelines have paid less attention to N1 lymph node dissection,and there is a gap in the specification for the management of N1 lymph nodes,especially intrapulmonary lymph nodes,in lung cancer surgery.The absence of this specification is detrimental to the accurate staging of patients and the development of related studies.Previous studies have illustrated the standard operating protocol for intrapulmonary lymph node detection,but no article has explored the guideline for the management of N1 lymph nodes from a quantitative perspective.In Chapter 4,our study explores the standard of N1 lymph node clearance from a quantitative perspective based on patients in the Surveillance,Epidemiology and End Results(SEER)public database from United States and the thoracic surgery database of the West China Hospital to provide evidence for the development of relevant guidelines in the future.Materials and Methods:Chapter one:The most appropriate subgroup approach for patients with p N1 stage non-small cell lung cancer involved in the eighth edition of N staging recommendations and the prognostic value of skip mediastinal lymph node metastases and related clinicopathological factors were investigated by systematic review and meta-analysis.The important value in the assessment of N1 lymph nodes in the staging of both stages of patients was also analyzed.In this paper,the odds ratio(OR)or relative risk(RR)was applied as an indicator to integrate categorical variables;the risk ratio(HR)was applied as an indicator to integrate survival-related variables.For all included studies,the Newcastle-Ottawa Scale(NOS score)was applied for quality assessment,and funnel plots were applied to determine the publication bias of the studies,while subgroup analysis and sensitivity analysis were performed to determine possible sources of outcome heterogeneity.Chapter two:Data were retrospectively collected from patients with non-small cell lung cancer who underwent surgery at the Department of Thoracic Surgery,West China Hospital from January 2005 to December 2018.Patients were divided into corresponding subgroups with different categories of patients with N1 lymph node metastasis,and the differences in prognosis between patients in different subgroups were compared by Overall survival(OS),and disease-free survival(DFS)illustrating the difference between the anatomical location-based N1 lymph node subgrouping method and the number-based N1 lymph node subgrouping for patients with NSCLC with lymph node metastases.And to explore the potential subgrouping approach for patients with p N1 stage non-small cell lung cancer.Chapter three:5336 patients with pathological stage N0 and N1 non-small cell lung cancer who underwent radical resection at the Department of Thoracic Surgery,West China Hospital were included.Patients were screened for preoperative staging including clinicopathological features,blood-based tumor biomarkers and CT imaging features.Support vector machine(SVM),random forest(RF)and artificial neural network(ANN)methods were applied to establish risk prediction models for N1 lymph node metastasis.The sensitivity,specificity and Area Under Curve(AUC)of the Receiver Operating Characteristic Curve(ROC)were applied to evaluate the prediction effect of the model.Chapter four:Patients with non-small cell lung cancer with pathological stage N1 who underwent radical surgery were included from the SEER database in the United States and the Department of Thoracic Surgery,West China Hospital,respectively.X-tile software was applied to determine the overall number of lymph node evaluations that should be performed for N1 patients and the ratio of N1 to N2 evaluations.In turn,the appropriate lymph node dissection protocol for N1 patients was described and the evaluation criteria for intrapulmonary lymph nodes were standardized from a quantitative perspective.Results:Chapter oneA total of 17 studies were included focusing on the subgroup approach of patients with stage N1 non-small cell lung cancer.The study types were all retrospective cohort,which included a total of 2717 male and 558 female patients.The findings showed that patients with multisite N1 lymph node involvement had a significantly worse prognosis compared to patients with single-site N1 metastases(HR=1.52;95% CI=1.32-1.76;p<0.001;I2=5.1%).In contrast,the number of lymph nodes(single or multiple)did not find a significant effect on patient prognosis(HR=1.25;95%CI=0.96-1.64;p=0.097;I2=72.5%).In contrast,patients with positive lymph nodes in the hilar region had a significantly worse prognosis compared to those with lymph node involvement in the peripheral region only(HR=1.80;95% CI=1.57-2.07;p<0.001;I2=0%).Subgroup analysis performed considering the differences in different lymph node map showed that the application of Naruke lymph node map(HR=2.05;95% CI=1.48-2.86;p<0.001),MD-ATS lymph node map(HR=1.80;95% CI=1.48-2.19;p<0.001)and articles not reporting lymph node map(HR=1.67.95% CI=1.29-2.17;p<0.001)in the analysis all showed that the N1 subgroup approach based on the anatomical location of lymph node substations was predictive of long-term prognosis of patients.A total of 6476 patients from 29 studies focusing on the prognosis of patients with non-small cell lung cancer with skip mediastinal lymph node metastases.The results showed that tumors located in the upper lobe had a higher probability of developing skip mediastinal lymph node metastases compared with those in the lower lobe(RR=1.16,95% CI: 1.00-1.34,p=0.044,I2=39.8%).As for the long-term survival of patients,patients with skip mediastinal lymph node metastases had better overall survival(HR=0.74,95% CI: 0.66-0.83,p<0.001,I2=48.2%)and disease-free survival(HR=0.71,95% CI: 0.61-0.84,p<0.001,I2=18.2%).Further subgroup analyses performed in studies focusing on intrapulmonary lymph node assessment showed similarly stable results(HR=0.67,95%CI: 0.57-0.77,p<0.001,I2=0).Chapter twoA total of 1143 patients with non-small cell lung cancer staged from IIB to IIIB who underwent radical lung cancer resection and systemic lymph node dissection were included in the cohort,including 794(69.5%)men and 349(30.5%)women,with a mean age of 58 years old.The median survival times for patients in N1 a,N1b,N2a1,and N2a2 based on the N1 patient grouping approach based on the number of lymphatic stations involved in the eighth edition of the N staging recommendations were 81.7 months,51.5 months,61.4 months,and 55.4 months,respectively,and the five-year disease-free survival rates were 51%,35%,32%,and 28%.Patients had extremely similar prognosis(OS: N2a1 vs.N1 b,p=0.091;N2a2 vs.N1 b,p=0.757.DFS: N2a1 vs.N1 b,p=0.777;N2a2 vs.N1 b,p=0.187).And the median survival times under the anatomical location-based N-stage grouping were: 88.2 months,71.4 months,61.4 months,and 55.4 months for z N1 a,z N1 b,z N2a1,and z N2a2,respectively;and the five-year disease-free survival rates were: 54%,46%,32%,and 28%.There were substantial differences in disease-free survival between the groups under categorization based on anatomic approach(DFS: z N1 b vs.z N1 a,p=0.433,z N2a1 vs.z N1 b,p=0.061;z N2a2 vs.z N1 b,p=0.0001).Chapter threeThe sensitivity of the prediction model based on the logistic regression algorithm is 84.8%,the specificity is 70.1%,and the area under the curve is 0.832;when all possible influencing factors were applied to build the support vector machine model,the prediction sensitivity is 88.7%,the specificity is 64.5%,and the AUC is 0.826.In the prediction model based on the random forest algorithm,the sensitivity obtained in the prediction model based on the artificial neural network algorithm was 87.6%,the specificity was 70.7%,and the AUC was 0.834.Chapter fourThe results of X-tile software showed that N1 patients had a better prognosis when at least 10 lymph nodes were detected,and this finding was confirmed in both the West China Hospital thoracic surgery cohort and the SEER database cohort from the United States.And considering the significant individual differences in the number of intrapulmonary lymph nodes,this study applied N1/N2 as an observational index for N1 lymph node assessment.The results showed a better long-term survival benefit for N1 patients when N1/N2 ≥ 0.3.Conclusion:The subgrouping approach based on anatomical location was able to differentiate the prognosis of patients with stage N1 non-small cell lung cancer between different subgroups.In patients who underwent intrapulmonary lymph node sorting,skip mediastinal lymph node metastases(stage N2a1)had a significantly better prognosis compared with sequential metastases in stage N2 patients.The prediction model developed in this study can better predict N1 lymph node metastasis by preoperative indicators and facilitate more detailed intrapulmonary lymph node assessment for appropriate patients.In terms of intrapulmonary lymph node assessment standard,our results showed that an intraoperative overview of 10 lymph nodes should be cleared intraoperatively and that an N1/N2 ratio ≥0.3 leads to a better prognosis for N1 patients. |
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