BACKGROUND Sarcopenia is a progressively diminishing state characterized by the reduction of muscle mass and density,which is frequently observed in malignancies of solid organs.AIM To assess how sarcopenia affects th...BACKGROUND Sarcopenia is a progressively diminishing state characterized by the reduction of muscle mass and density,which is frequently observed in malignancies of solid organs.AIM To assess how sarcopenia affects the overall survival of individuals who have been diagnosed with metastatic gastric cancer.METHODS The study retrospectively included individuals who had been diagnosed with metastatic gastric cancer between January 2008 and December 2020.Sarcopenia was identified through the calculation of the average Hounsfield units(HUAC)using computed tomography(CT)images taken at the time of diagnosis in patients.RESULTS A total of 118 patients with metastatic gastric cancer were evaluated.Sarcopenia was detected in 29 patients(24.6%).The median survival of all patients was 8(1-43)mo.The median survival of patients with sarcopenia was 2 mo,while it was 10 mo for those without sarcopenia(P<0.001).A significant relationship was found between sarcopenia and survival.CONCLUSION Sarcopenia has been observed to impact survival outcomes in various types of solid tumor cancers.Sarcopenic patients can be identified in a short time,easily and inexpensively,by HUAC measurements from CT images used for diagnosis,and survival could be promoted with nutritional support.展开更多
BACKGROUND Although accurately evaluating the overall survival(OS)of gastric cancer patients remains difficult,radiomics is considered an important option for studying pro-gnosis.AIM To develop a robust and unbiased b...BACKGROUND Although accurately evaluating the overall survival(OS)of gastric cancer patients remains difficult,radiomics is considered an important option for studying pro-gnosis.AIM To develop a robust and unbiased biomarker for predicting OS using machine learning and computed tomography(CT)image radiomics.METHODS This study included 181 stage II/III gastric cancer patients,141 from Lichuan People's Hospital,and 40 from the Cancer Imaging Archive(TCIA).Primary tumors in the preoperative unenhanced CT images were outlined as regions of interest(ROI),and approximately 1700 radiomics features were extracted from each ROI.The skeletal muscle index(SMI)and skeletal muscle density(SMD)were measured using CT images from the lower margin of the third lumbar vertebra.Using the least absolute shrinkage and selection operator regression with 5-fold cross-validation,36 radiomics features were identified as important predictors,and the OS-associated CT image radiomics score(OACRS)was cal-culated for each patient using these important predictors.RESULTS Patients with a high OACRS had a poorer prognosis than those with a low OACRS score(P<0.05)and those in the TCIA cohort.Univariate and multivariate analyses revealed that OACRS was a risk factor[RR=3.023(1.896-4.365),P<0.001]independent of SMI,SMD,and pathological features.Moreover,OACRS outperformed SMI and SMD and could improve OS prediction(P<0.05).CONCLUSION A novel biomarker based on machine learning and radiomics was developed that exhibited exceptional OS discrimination potential.展开更多
文摘BACKGROUND Sarcopenia is a progressively diminishing state characterized by the reduction of muscle mass and density,which is frequently observed in malignancies of solid organs.AIM To assess how sarcopenia affects the overall survival of individuals who have been diagnosed with metastatic gastric cancer.METHODS The study retrospectively included individuals who had been diagnosed with metastatic gastric cancer between January 2008 and December 2020.Sarcopenia was identified through the calculation of the average Hounsfield units(HUAC)using computed tomography(CT)images taken at the time of diagnosis in patients.RESULTS A total of 118 patients with metastatic gastric cancer were evaluated.Sarcopenia was detected in 29 patients(24.6%).The median survival of all patients was 8(1-43)mo.The median survival of patients with sarcopenia was 2 mo,while it was 10 mo for those without sarcopenia(P<0.001).A significant relationship was found between sarcopenia and survival.CONCLUSION Sarcopenia has been observed to impact survival outcomes in various types of solid tumor cancers.Sarcopenic patients can be identified in a short time,easily and inexpensively,by HUAC measurements from CT images used for diagnosis,and survival could be promoted with nutritional support.
文摘BACKGROUND Although accurately evaluating the overall survival(OS)of gastric cancer patients remains difficult,radiomics is considered an important option for studying pro-gnosis.AIM To develop a robust and unbiased biomarker for predicting OS using machine learning and computed tomography(CT)image radiomics.METHODS This study included 181 stage II/III gastric cancer patients,141 from Lichuan People's Hospital,and 40 from the Cancer Imaging Archive(TCIA).Primary tumors in the preoperative unenhanced CT images were outlined as regions of interest(ROI),and approximately 1700 radiomics features were extracted from each ROI.The skeletal muscle index(SMI)and skeletal muscle density(SMD)were measured using CT images from the lower margin of the third lumbar vertebra.Using the least absolute shrinkage and selection operator regression with 5-fold cross-validation,36 radiomics features were identified as important predictors,and the OS-associated CT image radiomics score(OACRS)was cal-culated for each patient using these important predictors.RESULTS Patients with a high OACRS had a poorer prognosis than those with a low OACRS score(P<0.05)and those in the TCIA cohort.Univariate and multivariate analyses revealed that OACRS was a risk factor[RR=3.023(1.896-4.365),P<0.001]independent of SMI,SMD,and pathological features.Moreover,OACRS outperformed SMI and SMD and could improve OS prediction(P<0.05).CONCLUSION A novel biomarker based on machine learning and radiomics was developed that exhibited exceptional OS discrimination potential.
