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
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
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 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.
