BACKGROUND Gastric cancer(GC)is a prevalent malignancy with a substantial health burden and high mortality rate,despite advances in prevention,early detection,and treatment.Compared with the global average,Asia,notabl...BACKGROUND Gastric cancer(GC)is a prevalent malignancy with a substantial health burden and high mortality rate,despite advances in prevention,early detection,and treatment.Compared with the global average,Asia,notably China,reports disproportionately high GC incidences.The disease often progresses asymptoma-tically in the early stages,leading to delayed diagnosis and compromised out-comes.Thus,it is crucial to identify early diagnostic biomarkers and enhance treatment strategies to improve patient outcomes and reduce mortality.METHODS Retrospectively analyzed the clinical data of 148 patients with GC treated at the Civil Aviation Shanghai Hospital between December 2022 and December 2023.The associations of coagulation indices-partial thromboplastin time(APTT),prothrombin time(PT),thrombin time(TT),fibrinogen,fibrinogen degradation products(FDP),fasting blood glucose,and D-dimer(D-D)with TNM stage and distant metastasis were examined.RESULTS Prolongation of APTT,PT,and TT was significantly correlated with the GC TNM stage.Hence,abnormal coagulation system activation was closely related to disease progression.Elevated FDP and D-D were significantly associated with distant metastasis in GC(P<0.05),suggesting that increased fibrinolytic activity contributes to increased metastatic risk.CONCLUSION Our Results reveal coagulation indices,FDPs as GC biomarkers,reflecting abnormal coagulation/fibrinolysis,aiding disease progression,metastasis prediction,and helping clinicians assess thrombotic risk for early intervention and personalized treatment plans.展开更多
BACKGROUND The liver,as the main target organ for hematogenous metastasis of colorectal cancer,early and accurate prediction of liver metastasis is crucial for the diagnosis and treatment of patients.Herein,this study...BACKGROUND The liver,as the main target organ for hematogenous metastasis of colorectal cancer,early and accurate prediction of liver metastasis is crucial for the diagnosis and treatment of patients.Herein,this study aims to investigate the application value of a combined machine learning(ML)based model based on the multiparameter magnetic resonance imaging for prediction of rectal metachronous liver metastasis(MLM).AIM To investigate the efficacy of radiomics based on multiparametric magnetic resonance imaging images of preoperative first diagnosed rectal cancer in predicting MLM from rectal cancer.METHODS We retrospectively analyzed 301 patients with rectal cancer confirmed by surgical pathology at Jingzhou Central Hospital from January 2017 to December 2023.All participants were randomly assigned to the training or validation queue in a 7:3 ratio.We first apply generalized linear regression model(GLRM)and random forest model(RFM)algorithm to construct an MLM prediction model in the training queue,and evaluate the discriminative power of the MLM prediction model using area under curve(AUC)and decision curve analysis(DCA).Then,the robustness and generalizability of the MLM prediction model were evaluated based on the internal validation set between the validation queue groups.RESULTS Among the 301 patients included in the study,16.28%were ultimately diagnosed with MLM through pathological examination.Multivariate analysis showed that carcinoembryonic antigen,and magnetic resonance imaging radiomics were independent predictors of MLM.Then,the GLRM prediction model was developed with a comprehensive nomogram to achieve satisfactory differentiation.The prediction performance of GLRM in the training and validation queue was 0.765[95%confidence interval(CI):0.710-0.820]and 0.767(95%CI:0.712-0.822),respectively.Compared with GLRM,RFM achieved superior performance with AUC of 0.919(95%CI:0.868-0.970)and 0.901(95%CI:0.850-0.952)in the training and validation queue,respectively.The DCA indicated that the predictive ability and net profit of clinical RFM were improved.CONCLUSION By combining multiparameter magnetic resonance imaging with the effectiveness and robustness of ML-based predictive models,the proposed clinical RFM can serve as an insight tool for preoperative assessment of MLM risk stratification and provide important information for individual diagnosis and treatment of rectal cancer patients.展开更多
文摘BACKGROUND Gastric cancer(GC)is a prevalent malignancy with a substantial health burden and high mortality rate,despite advances in prevention,early detection,and treatment.Compared with the global average,Asia,notably China,reports disproportionately high GC incidences.The disease often progresses asymptoma-tically in the early stages,leading to delayed diagnosis and compromised out-comes.Thus,it is crucial to identify early diagnostic biomarkers and enhance treatment strategies to improve patient outcomes and reduce mortality.METHODS Retrospectively analyzed the clinical data of 148 patients with GC treated at the Civil Aviation Shanghai Hospital between December 2022 and December 2023.The associations of coagulation indices-partial thromboplastin time(APTT),prothrombin time(PT),thrombin time(TT),fibrinogen,fibrinogen degradation products(FDP),fasting blood glucose,and D-dimer(D-D)with TNM stage and distant metastasis were examined.RESULTS Prolongation of APTT,PT,and TT was significantly correlated with the GC TNM stage.Hence,abnormal coagulation system activation was closely related to disease progression.Elevated FDP and D-D were significantly associated with distant metastasis in GC(P<0.05),suggesting that increased fibrinolytic activity contributes to increased metastatic risk.CONCLUSION Our Results reveal coagulation indices,FDPs as GC biomarkers,reflecting abnormal coagulation/fibrinolysis,aiding disease progression,metastasis prediction,and helping clinicians assess thrombotic risk for early intervention and personalized treatment plans.
文摘BACKGROUND The liver,as the main target organ for hematogenous metastasis of colorectal cancer,early and accurate prediction of liver metastasis is crucial for the diagnosis and treatment of patients.Herein,this study aims to investigate the application value of a combined machine learning(ML)based model based on the multiparameter magnetic resonance imaging for prediction of rectal metachronous liver metastasis(MLM).AIM To investigate the efficacy of radiomics based on multiparametric magnetic resonance imaging images of preoperative first diagnosed rectal cancer in predicting MLM from rectal cancer.METHODS We retrospectively analyzed 301 patients with rectal cancer confirmed by surgical pathology at Jingzhou Central Hospital from January 2017 to December 2023.All participants were randomly assigned to the training or validation queue in a 7:3 ratio.We first apply generalized linear regression model(GLRM)and random forest model(RFM)algorithm to construct an MLM prediction model in the training queue,and evaluate the discriminative power of the MLM prediction model using area under curve(AUC)and decision curve analysis(DCA).Then,the robustness and generalizability of the MLM prediction model were evaluated based on the internal validation set between the validation queue groups.RESULTS Among the 301 patients included in the study,16.28%were ultimately diagnosed with MLM through pathological examination.Multivariate analysis showed that carcinoembryonic antigen,and magnetic resonance imaging radiomics were independent predictors of MLM.Then,the GLRM prediction model was developed with a comprehensive nomogram to achieve satisfactory differentiation.The prediction performance of GLRM in the training and validation queue was 0.765[95%confidence interval(CI):0.710-0.820]and 0.767(95%CI:0.712-0.822),respectively.Compared with GLRM,RFM achieved superior performance with AUC of 0.919(95%CI:0.868-0.970)and 0.901(95%CI:0.850-0.952)in the training and validation queue,respectively.The DCA indicated that the predictive ability and net profit of clinical RFM were improved.CONCLUSION By combining multiparameter magnetic resonance imaging with the effectiveness and robustness of ML-based predictive models,the proposed clinical RFM can serve as an insight tool for preoperative assessment of MLM risk stratification and provide important information for individual diagnosis and treatment of rectal cancer patients.
