Objective:To develop a deep learning model to predict lymph node(LN)status in clinical stage IA lung adeno-carcinoma patients.Methods:This diagnostic study included 1,009 patients with pathologically confirmed clinica...Objective:To develop a deep learning model to predict lymph node(LN)status in clinical stage IA lung adeno-carcinoma patients.Methods:This diagnostic study included 1,009 patients with pathologically confirmed clinical stage T1N0M0 lung adenocarcinoma from two independent datasets(699 from Cancer Hospital of Chinese Academy of Medical Sciences and 310 from PLA General Hospital)between January 2005 and December 2019.The Cancer Hospital dataset was randomly split into a training cohort(559 patients)and a validation cohort(140 patients)to train and tune a deep learning model based on a deep residual network(ResNet).The PLA Hospital dataset was used as a testing cohort to evaluate the generalization ability of the model.Thoracic radiologists manually segmented tumors and interpreted high-resolution computed tomography(HRCT)features for the model.The predictive performance was assessed by area under the curves(AUCs),accuracy,precision,recall,and F1 score.Subgroup analysis was performed to evaluate the potential bias of the study population.Results:A total of 1,009 patients were included in this study;409(40.5%)were male and 600(59.5%)were female.The median age was 57.0 years(inter-quartile range,IQR:50.0-64.0).The deep learning model achieved AUCs of 0.906(95%CI:0.873-0.938)and 0.893(95%CI:0.857-0.930)for predicting pN0 disease in the testing cohort and a non-pure ground glass nodule(non-pGGN)testing cohort,respectively.No significant difference was detected between the testing cohort and the non-pGGN testing cohort(P=0.622).The precisions of this model for predicting pN0 disease were 0.979(95%CI:0.963-0.995)and 0.983(95%CI:0.967-0.998)in the testing cohort and the non-pGGN testing cohort,respectively.The deep learning model achieved AUCs of 0.848(95%CI:0.798-0.898)and 0.831(95%CI:0.776-0.887)for predicting pN2 disease in the testing cohort and the non-pGGN testing cohort,respectively.No significant difference was detected between the testing cohort and the non-pGGN testing cohort(P=0.657).The recalls of this model for predicting pN2 disease were 0.903(95%CI:0.870-0.936)and 0.931(95%CI:0.901-0.961)in the testing cohort and the non-pGGN testing cohort,respectively.Conclusions:The superior performance of the deep learning model will help to target the extension of lymph node dissection and reduce the ineffective lymph node dissection in early-stage lung adenocarcinoma patients.展开更多
Objective:The proportion of patients with stageⅠlung adenocarcinoma(LUAD)has dramatically increased with the prevalence of low-dose computed tomography use for screening.Up to 30%of patients with stageⅠLUAD experien...Objective:The proportion of patients with stageⅠlung adenocarcinoma(LUAD)has dramatically increased with the prevalence of low-dose computed tomography use for screening.Up to 30%of patients with stageⅠLUAD experience recurrence within 5 years after curative surgery.A robust risk stratification tool is urgently needed to identify patients who might benefit from adjuvant treatment.Methods:In this first investigation of the relationship between metabolic reprogramming and recurrence in stageⅠLUAD,we developed a recurrence-associated metabolic signature(RAMS).This RAMS was based on metabolism-associated genes to predict cancer relapse and overall prognoses of patients with stageⅠLUAD.The clinical significance and immune landscapes of the signature were comprehensively analyzed.Results:Based on a gene expression profile from the GSE31210 database,functional enrichment analysis revealed a significant difference in metabolic reprogramming that distinguished patients with stageⅠLUAD with relapse from those without relapse.We then identified a metabolic signature(i.e.,RAMS)represented by 2 genes(ACADM and RPS8)significantly related to recurrence-free survival and overall survival times of patients with stageⅠLUAD using transcriptome data analysis of a training set.The training set was well validated in a test set.The discriminatory power of the 2 gene metabolic signature was further validated using protein values in an additional independent cohort.The results indicated a clear association between a high risk score and a very poor patient prognosis.Stratification analysis and multivariate Cox regression analysis showed that the RAMS was an independent prognostic factor.We also found that the risk score was positively correlated with inflammatory response,the antigen-presenting process,and the expression levels of many immunosuppressive checkpoint molecules(e.g.,PD-L1,PD-L2,B7-H3,galectin-9,and FGL-1).These results suggested that high risk patients had immune response suppression.Further analysis revealed that anti-PD-1/PD-L1 immunotherapy did not have significant benefits for high risk patients.However,the patients could respond better to chemotherapy.Conclusions:This study is the first to highlight the relationship between metabolic reprogramming and recurrence in stageⅠLUAD,and is the first to also develop a clinically feasible signature.This signature may be a powerful prognostic tool and help further optimize the cancer therapy paradigm.展开更多
Objective: To observe and compare the effect of traditional Chinese medicine (TCM) combined with chemotherapy (CT) on immune function and quality of life (QOL)of patients with non-small cell lung cancer (NSCLC) in sta...Objective: To observe and compare the effect of traditional Chinese medicine (TCM) combined with chemotherapy (CT) on immune function and quality of life (QOL)of patients with non-small cell lung cancer (NSCLC) in stage Ⅲ-Ⅳ. Methods: One hundred cases with stage Ⅲ-Ⅳ NSCLC were randomly divided into two groups. The treated group (n=50) received CT combined with TCM, and the control group received CT alone. The percentage of T lymphocyte subset in peripheral blood and the change of natural killer (NK) cell count were observed after treatment. The QOL and tolerance of CT were also compared between the two groups after treatment. Results: In the treated group, CD3 cell count, CD4 cell count, CD4/ CDg ratio and NK cell activity were higher than those in control group, while CD8 cell count in the treated group was lower than that in the control group (P<0.05), and QOL and tolerance of CT in the treated group were also better (P<0.05). Conclusion: TCM combined with CT could raise the patients' ability in tolerating CT in stage Ⅲ-ⅣNSCLC.展开更多
Lung cancer is responsible for the most cancer deaths worldwide with an incidence that is still rising. One third of patients have unresectable stage ⅢA or stage ⅢB disease. The standard of care for locally advanced...Lung cancer is responsible for the most cancer deaths worldwide with an incidence that is still rising. One third of patients have unresectable stage ⅢA or stage ⅢB disease. The standard of care for locally advanceddisease in patients with good performance status consists of combined modality therapy in particular concurrent chemoradiotherapy. But despite a lot of efforts done in the past, local control and survival of patients with unresectable stage Ⅲ non-small-cell lung cancer(NSCLC) remains poor. Improving outcomes for patients with unresectable stage Ⅲ NSCLC has therefore been an area of ongoing research. Research has focused on improving systemic therapy, improving radiation therapy or adding a maintenance therapy to consolidate the initial therapy. Also implementation of newer targeted therapies and immunotherapy has been investigated as well as the option of prophylactic cranial irradiation. This article reviews the latest literature on improving local control and preventing distant metastases. It seems that we have reached a plateau with conventional chemotherapy. Radiotherapy dose escalation did not improve outcome although increasing radiation dose-intensity with new radiotherapy techniques and the use of newer agents, e.g., immunotherapy might be promising. In the future well-designed clinical trials are necessary to prove those promising results.展开更多
Background:With the popularization of lung cancer screening,more early-stage lung cancers are being detected.This study aims to compare three types of N classifications,including location-based N classification(pathol...Background:With the popularization of lung cancer screening,more early-stage lung cancers are being detected.This study aims to compare three types of N classifications,including location-based N classification(pathologic nodal classification[pN]),the number of lymph node stations(nS)-based N classification(nS classification),and the combined approach proposed by the International Association for the Study of Lung Cancer(IASLC)which incorporates both pN and nS classification to determine if the nS classification is more appropriate for early-stage lung cancer.Methods:We retrospectively reviewed the clinical data of lung cancer patients treated at the Cancer Hospital,Chinese Academy of Medical Sciences between 2005 and 2018.Inclusion criteria was clinical stage IA lung adenocarcinoma patients who underwent resection during this period.Sub-analyses were performed for the three types of N classifications.The optimal cutoffvalues for nS classification were determined with X-tile software.Kaplan‒Meier and multivariate Cox analyses were performed to assess the prognostic significance of the different N classifications.The prediction performance among the three types of N classifications was compared using the concordance index(C-index)and decision curve analysis(DCA).Results:Of the 669 patients evaluated,534 had pathological stage N0 disease(79.8%),82 had N1 disease(12.3%)and 53 had N2 disease(7.9%).Multivariate Cox analysis indicated that all three types of N classifications were independent prognostic factors for prognosis(all P<0.001).However,the prognosis overlaps between pN(N1 and N2,P=0.052)and IASLC-proposed N classification(N1b and N2a1[P=0.407],N2a1 and N2a2[P=0.364],and N2a2 and N2b[P=0.779]),except for nS classification subgroups(nS0 and nS1[P<0.001]and nS1 and nS>1[P=0.006]).There was no significant difference in the C-index values between the three N classifications(P=0.370).The DCA results demonstrated that the nS classification provided greater clinical utility.Conclusion:The nS classification might be a better choice for nodal classification in clinical stage IA lung adeno-carcinoma.展开更多
Objective:Histology grade,subtypes and TNM stage of lung adenocarcinomas are useful predictors of prognosis and survival.The aim of the study was to investigate the relationship between chromosomal instability,morphol...Objective:Histology grade,subtypes and TNM stage of lung adenocarcinomas are useful predictors of prognosis and survival.The aim of the study was to investigate the relationship between chromosomal instability,morphological subtypes and the grading system used in lung non-mucinous adenocarcinoma(LNMA).Methods:We developed a whole genome copy number variation(WGCNV)scoring system and applied next generation sequencing to evaluate CNVs present in 91 LNMA tumor samples.Results:Higher histological grades,aggressive subtypes and more advanced TNM staging were associated with an increased WGCNV score,particularly in CNV regions enriched for tumor suppressor genes and oncogenes.In addition,we demonstrate that 24-chromosome CNV profiling can be performed reliably from specific cell types(<100 cells)isolated by sample laser capture microdissection.Conclusions:Our findings suggest that the WGCNV scoring system we developed may have potential value as an adjunct test for predicting the prognosis of patients diagnosed with LNMA.展开更多
BACKGROUND The clinical role of ground glass opacity(GGO)on computed tomography(CT)in stage I pulmonary adenocarcinoma patients currently remains unclear.AIM To explore the prognostic value of GGO on CT in lung adenoc...BACKGROUND The clinical role of ground glass opacity(GGO)on computed tomography(CT)in stage I pulmonary adenocarcinoma patients currently remains unclear.AIM To explore the prognostic value of GGO on CT in lung adenocarcinoma patients who were pathologically diagnosed with tumor-node-metastasis stage I.METHODS A comprehensive and systematic search was conducted through the PubMed,EMBASE and Web of Science databases up to April 3,2021.The hazard ratio(HR)and corresponding 95%confidence interval(CI)were combined to assess the association between the presence of GGO and prognosis,representing overall survival and disease-free survival.Subgroup analysis based on the ratio of GGO was also conducted.STATA 12.0 software was used for statistical analysis.RESULTS A total of 12 studies involving 4467 patients were included.The pooled results indicated that the GGO predicted favorable overall survival(HR=0.44,95%CI:0.34-0.59,P<0.001)and disease-free survival(HR=0.35,95%CI:0.18-0.70,P=0.003).Subgroup analysis based on the ratio of GGO further demonstrated that the proportion of GGO was a good prognostic indicator in pathological stage I pulmonary adenocarcinoma patients,and patients with a higher ratio of GGO showed better prognosis than patients with a lower GGO ratio did.CONCLUSION This meta-analysis manifested that the presence of GGO on CT predicted favorable prognosis in tumor-node-metastasis stage I lung adenocarcinoma.Patients with a higher GGO ratio were more likely to have a better prognosis than patients with a lower GGO ratio.展开更多
基金supported by the National Key R&D Program of China(grant numbers:2020AAA0109504,2023YFC2415200)CAMS Innovation Fund for Medical Sciences(grant number:2021-I2M-C&T-B-061)+5 种基金Beijing Hope Run Special Fund of Cancer Foundation of China(grant number:LC2022A22)the National Natural Science Foundation of China(grant numbers:81971619,81971580,92259302,82372053,91959205,82361168664,82022036,81971776)Beijing Natural Sci-ence Foundation(grant number:Z20J00105)Key-Area Research and Development Program of Guangdong Province(grant number:2021B0101420005)Strategic Priority Research Program of Chinese Academy of Sciences(grant number:XDB38040200)the Youth In-novation Promotion Association CAS(grant number:Y2021049).
文摘Objective:To develop a deep learning model to predict lymph node(LN)status in clinical stage IA lung adeno-carcinoma patients.Methods:This diagnostic study included 1,009 patients with pathologically confirmed clinical stage T1N0M0 lung adenocarcinoma from two independent datasets(699 from Cancer Hospital of Chinese Academy of Medical Sciences and 310 from PLA General Hospital)between January 2005 and December 2019.The Cancer Hospital dataset was randomly split into a training cohort(559 patients)and a validation cohort(140 patients)to train and tune a deep learning model based on a deep residual network(ResNet).The PLA Hospital dataset was used as a testing cohort to evaluate the generalization ability of the model.Thoracic radiologists manually segmented tumors and interpreted high-resolution computed tomography(HRCT)features for the model.The predictive performance was assessed by area under the curves(AUCs),accuracy,precision,recall,and F1 score.Subgroup analysis was performed to evaluate the potential bias of the study population.Results:A total of 1,009 patients were included in this study;409(40.5%)were male and 600(59.5%)were female.The median age was 57.0 years(inter-quartile range,IQR:50.0-64.0).The deep learning model achieved AUCs of 0.906(95%CI:0.873-0.938)and 0.893(95%CI:0.857-0.930)for predicting pN0 disease in the testing cohort and a non-pure ground glass nodule(non-pGGN)testing cohort,respectively.No significant difference was detected between the testing cohort and the non-pGGN testing cohort(P=0.622).The precisions of this model for predicting pN0 disease were 0.979(95%CI:0.963-0.995)and 0.983(95%CI:0.967-0.998)in the testing cohort and the non-pGGN testing cohort,respectively.The deep learning model achieved AUCs of 0.848(95%CI:0.798-0.898)and 0.831(95%CI:0.776-0.887)for predicting pN2 disease in the testing cohort and the non-pGGN testing cohort,respectively.No significant difference was detected between the testing cohort and the non-pGGN testing cohort(P=0.657).The recalls of this model for predicting pN2 disease were 0.903(95%CI:0.870-0.936)and 0.931(95%CI:0.901-0.961)in the testing cohort and the non-pGGN testing cohort,respectively.Conclusions:The superior performance of the deep learning model will help to target the extension of lymph node dissection and reduce the ineffective lymph node dissection in early-stage lung adenocarcinoma patients.
基金supported by the National Natural Science Foundation of China(Grant Nos.81802299 and 81502514)the Fundamental Research Funds for the Central Universities(Grant No.3332018070)+3 种基金the CAMS Innovation Fund for Medical Sciences(Grant Nos.2016-I2M-1-001 and 2017-I2M-1-005)the National Key R&D Program of China(Grant Nos.2018YFC1312100 and 2018YFC1312102)the National Key Basic Research Development Plan(Grant No.2018YFC1312105)the Graduate Innovation Funds of Peking Union Medical College(Grant No.2019-1002-06)。
文摘Objective:The proportion of patients with stageⅠlung adenocarcinoma(LUAD)has dramatically increased with the prevalence of low-dose computed tomography use for screening.Up to 30%of patients with stageⅠLUAD experience recurrence within 5 years after curative surgery.A robust risk stratification tool is urgently needed to identify patients who might benefit from adjuvant treatment.Methods:In this first investigation of the relationship between metabolic reprogramming and recurrence in stageⅠLUAD,we developed a recurrence-associated metabolic signature(RAMS).This RAMS was based on metabolism-associated genes to predict cancer relapse and overall prognoses of patients with stageⅠLUAD.The clinical significance and immune landscapes of the signature were comprehensively analyzed.Results:Based on a gene expression profile from the GSE31210 database,functional enrichment analysis revealed a significant difference in metabolic reprogramming that distinguished patients with stageⅠLUAD with relapse from those without relapse.We then identified a metabolic signature(i.e.,RAMS)represented by 2 genes(ACADM and RPS8)significantly related to recurrence-free survival and overall survival times of patients with stageⅠLUAD using transcriptome data analysis of a training set.The training set was well validated in a test set.The discriminatory power of the 2 gene metabolic signature was further validated using protein values in an additional independent cohort.The results indicated a clear association between a high risk score and a very poor patient prognosis.Stratification analysis and multivariate Cox regression analysis showed that the RAMS was an independent prognostic factor.We also found that the risk score was positively correlated with inflammatory response,the antigen-presenting process,and the expression levels of many immunosuppressive checkpoint molecules(e.g.,PD-L1,PD-L2,B7-H3,galectin-9,and FGL-1).These results suggested that high risk patients had immune response suppression.Further analysis revealed that anti-PD-1/PD-L1 immunotherapy did not have significant benefits for high risk patients.However,the patients could respond better to chemotherapy.Conclusions:This study is the first to highlight the relationship between metabolic reprogramming and recurrence in stageⅠLUAD,and is the first to also develop a clinically feasible signature.This signature may be a powerful prognostic tool and help further optimize the cancer therapy paradigm.
文摘Objective: To observe and compare the effect of traditional Chinese medicine (TCM) combined with chemotherapy (CT) on immune function and quality of life (QOL)of patients with non-small cell lung cancer (NSCLC) in stage Ⅲ-Ⅳ. Methods: One hundred cases with stage Ⅲ-Ⅳ NSCLC were randomly divided into two groups. The treated group (n=50) received CT combined with TCM, and the control group received CT alone. The percentage of T lymphocyte subset in peripheral blood and the change of natural killer (NK) cell count were observed after treatment. The QOL and tolerance of CT were also compared between the two groups after treatment. Results: In the treated group, CD3 cell count, CD4 cell count, CD4/ CDg ratio and NK cell activity were higher than those in control group, while CD8 cell count in the treated group was lower than that in the control group (P<0.05), and QOL and tolerance of CT in the treated group were also better (P<0.05). Conclusion: TCM combined with CT could raise the patients' ability in tolerating CT in stage Ⅲ-ⅣNSCLC.
文摘Lung cancer is responsible for the most cancer deaths worldwide with an incidence that is still rising. One third of patients have unresectable stage ⅢA or stage ⅢB disease. The standard of care for locally advanceddisease in patients with good performance status consists of combined modality therapy in particular concurrent chemoradiotherapy. But despite a lot of efforts done in the past, local control and survival of patients with unresectable stage Ⅲ non-small-cell lung cancer(NSCLC) remains poor. Improving outcomes for patients with unresectable stage Ⅲ NSCLC has therefore been an area of ongoing research. Research has focused on improving systemic therapy, improving radiation therapy or adding a maintenance therapy to consolidate the initial therapy. Also implementation of newer targeted therapies and immunotherapy has been investigated as well as the option of prophylactic cranial irradiation. This article reviews the latest literature on improving local control and preventing distant metastases. It seems that we have reached a plateau with conventional chemotherapy. Radiotherapy dose escalation did not improve outcome although increasing radiation dose-intensity with new radiotherapy techniques and the use of newer agents, e.g., immunotherapy might be promising. In the future well-designed clinical trials are necessary to prove those promising results.
基金supported by CAMS Innovation Fund for Med-ical Sciences(grant number:2021-I2M-C&T-B-061)Beijing Hope Run Special Fund of Cancer Foundation of China(grant number:LC2022A22)+1 种基金Beijing Municipal Natural Science Foundation(grant num-ber:7184238)National Natural Science Foundation of China(grant number:81701692).
文摘Background:With the popularization of lung cancer screening,more early-stage lung cancers are being detected.This study aims to compare three types of N classifications,including location-based N classification(pathologic nodal classification[pN]),the number of lymph node stations(nS)-based N classification(nS classification),and the combined approach proposed by the International Association for the Study of Lung Cancer(IASLC)which incorporates both pN and nS classification to determine if the nS classification is more appropriate for early-stage lung cancer.Methods:We retrospectively reviewed the clinical data of lung cancer patients treated at the Cancer Hospital,Chinese Academy of Medical Sciences between 2005 and 2018.Inclusion criteria was clinical stage IA lung adenocarcinoma patients who underwent resection during this period.Sub-analyses were performed for the three types of N classifications.The optimal cutoffvalues for nS classification were determined with X-tile software.Kaplan‒Meier and multivariate Cox analyses were performed to assess the prognostic significance of the different N classifications.The prediction performance among the three types of N classifications was compared using the concordance index(C-index)and decision curve analysis(DCA).Results:Of the 669 patients evaluated,534 had pathological stage N0 disease(79.8%),82 had N1 disease(12.3%)and 53 had N2 disease(7.9%).Multivariate Cox analysis indicated that all three types of N classifications were independent prognostic factors for prognosis(all P<0.001).However,the prognosis overlaps between pN(N1 and N2,P=0.052)and IASLC-proposed N classification(N1b and N2a1[P=0.407],N2a1 and N2a2[P=0.364],and N2a2 and N2b[P=0.779]),except for nS classification subgroups(nS0 and nS1[P<0.001]and nS1 and nS>1[P=0.006]).There was no significant difference in the C-index values between the three N classifications(P=0.370).The DCA results demonstrated that the nS classification provided greater clinical utility.Conclusion:The nS classification might be a better choice for nodal classification in clinical stage IA lung adeno-carcinoma.
基金grants from Beijing Hospital Key Research Program(121 Research Program,No.BJ2019-195)。
文摘Objective:Histology grade,subtypes and TNM stage of lung adenocarcinomas are useful predictors of prognosis and survival.The aim of the study was to investigate the relationship between chromosomal instability,morphological subtypes and the grading system used in lung non-mucinous adenocarcinoma(LNMA).Methods:We developed a whole genome copy number variation(WGCNV)scoring system and applied next generation sequencing to evaluate CNVs present in 91 LNMA tumor samples.Results:Higher histological grades,aggressive subtypes and more advanced TNM staging were associated with an increased WGCNV score,particularly in CNV regions enriched for tumor suppressor genes and oncogenes.In addition,we demonstrate that 24-chromosome CNV profiling can be performed reliably from specific cell types(<100 cells)isolated by sample laser capture microdissection.Conclusions:Our findings suggest that the WGCNV scoring system we developed may have potential value as an adjunct test for predicting the prognosis of patients diagnosed with LNMA.
文摘BACKGROUND The clinical role of ground glass opacity(GGO)on computed tomography(CT)in stage I pulmonary adenocarcinoma patients currently remains unclear.AIM To explore the prognostic value of GGO on CT in lung adenocarcinoma patients who were pathologically diagnosed with tumor-node-metastasis stage I.METHODS A comprehensive and systematic search was conducted through the PubMed,EMBASE and Web of Science databases up to April 3,2021.The hazard ratio(HR)and corresponding 95%confidence interval(CI)were combined to assess the association between the presence of GGO and prognosis,representing overall survival and disease-free survival.Subgroup analysis based on the ratio of GGO was also conducted.STATA 12.0 software was used for statistical analysis.RESULTS A total of 12 studies involving 4467 patients were included.The pooled results indicated that the GGO predicted favorable overall survival(HR=0.44,95%CI:0.34-0.59,P<0.001)and disease-free survival(HR=0.35,95%CI:0.18-0.70,P=0.003).Subgroup analysis based on the ratio of GGO further demonstrated that the proportion of GGO was a good prognostic indicator in pathological stage I pulmonary adenocarcinoma patients,and patients with a higher ratio of GGO showed better prognosis than patients with a lower GGO ratio did.CONCLUSION This meta-analysis manifested that the presence of GGO on CT predicted favorable prognosis in tumor-node-metastasis stage I lung adenocarcinoma.Patients with a higher GGO ratio were more likely to have a better prognosis than patients with a lower GGO ratio.