Objective The development of non-invasive methods for evaluating lymph node metastasis(LNM)preoperatively in gastric cancer(GC)is necessary.In this study,we developed a new radiomics model combining features from the ...Objective The development of non-invasive methods for evaluating lymph node metastasis(LNM)preoperatively in gastric cancer(GC)is necessary.In this study,we developed a new radiomics model combining features from the tumor and peritumor regions for predicting LNM and prognoses.Methods This was a retrospective observational study.In this study,two cohorts of patients with GC treated in Zhongshan Hospital Fudan University(Shanghai,China)were included.In total,193 patients were assigned to the internal training/validation cohort;another 98 patients were assigned to the independent testing cohort.The radiomics features were extracted from venous phase computerized tomography(CT)images.The radiomics model was constructed and the output was defined as the radiomics score(RS).The performance of the RS and CT-defined N status(ctN)for predicting LNM was compared using the area under the curve(AUC).The 5-year overall survival and progression-free survival were compared between different subgroups using Kaplan–Meier curves.Results In both cohorts,the RS was significantly higher in the LNM-positive group than that in the LNM-negative group(all P<0.001).The radiomics model combining features from the tumor and peri-tumor regions achieved the highest AUC in predicting LNM(AUC,0.779 and 0.724,respectively),which performed better than the radiomics model based only on the tumor region and ctN(AUC,0.717,0.622 and 0.710,0.603,respectively).The differences in 5-year overall survival and progression-free survival between high-risk and low-risk groups were significant(both P<0.001).Conclusions The radiomics model combining features from the tumor and peri-tumor regions could effectively predict the LNM in GC.Risk stratification based on the RS was capable of distinguishing patients with poor prognoses.展开更多
In this paper we consider approximate eigenvalues and approximate eigenspaces for the generalized Rayleigh quotient, and present some residual bounds. Our obtained bounds will improve the existing ones.
基金supported by the Clinical Research Project of Zhongshan Hospital from Zhongshan Hospital,Fudan University[Grant No.2020ZSLC15]the National Natural Science Foundation of China[Grant No.91859107].
文摘Objective The development of non-invasive methods for evaluating lymph node metastasis(LNM)preoperatively in gastric cancer(GC)is necessary.In this study,we developed a new radiomics model combining features from the tumor and peritumor regions for predicting LNM and prognoses.Methods This was a retrospective observational study.In this study,two cohorts of patients with GC treated in Zhongshan Hospital Fudan University(Shanghai,China)were included.In total,193 patients were assigned to the internal training/validation cohort;another 98 patients were assigned to the independent testing cohort.The radiomics features were extracted from venous phase computerized tomography(CT)images.The radiomics model was constructed and the output was defined as the radiomics score(RS).The performance of the RS and CT-defined N status(ctN)for predicting LNM was compared using the area under the curve(AUC).The 5-year overall survival and progression-free survival were compared between different subgroups using Kaplan–Meier curves.Results In both cohorts,the RS was significantly higher in the LNM-positive group than that in the LNM-negative group(all P<0.001).The radiomics model combining features from the tumor and peri-tumor regions achieved the highest AUC in predicting LNM(AUC,0.779 and 0.724,respectively),which performed better than the radiomics model based only on the tumor region and ctN(AUC,0.717,0.622 and 0.710,0.603,respectively).The differences in 5-year overall survival and progression-free survival between high-risk and low-risk groups were significant(both P<0.001).Conclusions The radiomics model combining features from the tumor and peri-tumor regions could effectively predict the LNM in GC.Risk stratification based on the RS was capable of distinguishing patients with poor prognoses.
基金Acknowledgments. The authors thank the referees for their helpful comments. The work was supported in part by National Natural Science Foundations of China (No. 10671077, 10971075), Guangdong Provincial Natural Science Foundations (No. 09150631000021, 06025061) and Research Fund for the Doctoral Program of Higher Education of China (No. 20104407110001).
文摘In this paper we consider approximate eigenvalues and approximate eigenspaces for the generalized Rayleigh quotient, and present some residual bounds. Our obtained bounds will improve the existing ones.