DEAR EDITOR,Since the first reported severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)infection in December 2019,coronavirus disease 2019(COVID-19)has become a global pandemic,spreading to more than 200 coun...DEAR EDITOR,Since the first reported severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)infection in December 2019,coronavirus disease 2019(COVID-19)has become a global pandemic,spreading to more than 200 countries and regions worldwide.With continued research progress and virus detection,SARS-CoV-2 genomes and sequencing data have been reported and accumulated at an unprecedented rate.展开更多
BACKGROUND Preoperative risk stratification is significant for the management of endometrial cancer(EC)patients.Radiomics based on magnetic resonance imaging(MRI)in combination with clinical features may be useful to ...BACKGROUND Preoperative risk stratification is significant for the management of endometrial cancer(EC)patients.Radiomics based on magnetic resonance imaging(MRI)in combination with clinical features may be useful to predict the risk grade of EC.AIM To construct machine learning models to predict preoperative risk stratification of patients with EC based on radiomics features extracted from MRI.METHODS The study comprised 112 EC patients.The participants were randomly separated into training and validation groups with a 7:3 ratio.Logistic regression analysis was applied to uncover independent clinical predictors.These predictors were then used to create a clinical nomogram.Extracted radiomics features from the T2-weighted imaging and diffusion weighted imaging sequences of MRI images,the Mann-Whitney U test,Pearson test,and least absolute shrinkage and selection operator analysis were employed to evaluate the relevant radiomic features,which were subsequently utilized to generate a radiomic signature.Seven machine learning strategies were used to construct radiomic models that relied on the screening features.The logistic regression method was used to construct a composite nomogram that incorporated both the radiomic signature and clinical independent risk indicators.RESULTS Having an accuracy of 0.82 along with an area under the curve(AUC)of 0.915[95%confidence interval(CI):0.806-0.986],the random forest method trained on radiomics characteristics performed better than expected.The predictive accuracy of radiomics prediction models surpassed that of both the clinical nomogram(AUC:0.75,95%CI:0.611-0.899)and the combined nomogram(AUC:0.869,95%CI:0.702-0.986)that integrated clinical parameters and radiomic signature.CONCLUSION The MRI-based radiomics model may be an effective tool for preoperative risk grade prediction in EC patients.展开更多
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(XDB38030200,XDB38050300,XDA19090116,XDA19050302)National Key R&D Program of China(2020YFC0848900,2020YFC0847000)。
文摘DEAR EDITOR,Since the first reported severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)infection in December 2019,coronavirus disease 2019(COVID-19)has become a global pandemic,spreading to more than 200 countries and regions worldwide.With continued research progress and virus detection,SARS-CoV-2 genomes and sequencing data have been reported and accumulated at an unprecedented rate.
文摘BACKGROUND Preoperative risk stratification is significant for the management of endometrial cancer(EC)patients.Radiomics based on magnetic resonance imaging(MRI)in combination with clinical features may be useful to predict the risk grade of EC.AIM To construct machine learning models to predict preoperative risk stratification of patients with EC based on radiomics features extracted from MRI.METHODS The study comprised 112 EC patients.The participants were randomly separated into training and validation groups with a 7:3 ratio.Logistic regression analysis was applied to uncover independent clinical predictors.These predictors were then used to create a clinical nomogram.Extracted radiomics features from the T2-weighted imaging and diffusion weighted imaging sequences of MRI images,the Mann-Whitney U test,Pearson test,and least absolute shrinkage and selection operator analysis were employed to evaluate the relevant radiomic features,which were subsequently utilized to generate a radiomic signature.Seven machine learning strategies were used to construct radiomic models that relied on the screening features.The logistic regression method was used to construct a composite nomogram that incorporated both the radiomic signature and clinical independent risk indicators.RESULTS Having an accuracy of 0.82 along with an area under the curve(AUC)of 0.915[95%confidence interval(CI):0.806-0.986],the random forest method trained on radiomics characteristics performed better than expected.The predictive accuracy of radiomics prediction models surpassed that of both the clinical nomogram(AUC:0.75,95%CI:0.611-0.899)and the combined nomogram(AUC:0.869,95%CI:0.702-0.986)that integrated clinical parameters and radiomic signature.CONCLUSION The MRI-based radiomics model may be an effective tool for preoperative risk grade prediction in EC patients.