BACKGROUND Microvascular invasion(MVI)is a significant risk factor for recurrence and metastasis following hepatocellular carcinoma(HCC)surgery.Currently,there is a paucity of preoperative evaluation approaches for MV...BACKGROUND Microvascular invasion(MVI)is a significant risk factor for recurrence and metastasis following hepatocellular carcinoma(HCC)surgery.Currently,there is a paucity of preoperative evaluation approaches for MVI.AIM To investigate the predictive value of texture features and radiological signs based on multiparametric magnetic resonance imaging in the non-invasive preoperative prediction of MVI in HCC.METHODS Clinical data from 97 HCC patients were retrospectively collected from January 2019 to July 2022 at our hospital.Patients were classified into two groups:MVI-positive(n=57)and MVI-negative(n=40),based on postoperative pathological results.The correlation between relevant radiological signs and MVI status was analyzed.MaZda4.6 software and the mutual information method were employed to identify the top 10 dominant texture features,which were combined with radiological signs to construct artificial neural network(ANN)models for MVI prediction.The predictive performance of the ANN models was evaluated using area under the curve,sensitivity,and specificity.ANN models with relatively high predictive performance were screened using the DeLong test,and the regression model of multilayer feedforward ANN with backpropagation and error backpropagation learning method was used to evaluate the models’stability.RESULTS The absence of a pseudocapsule,an incomplete pseudocapsule,and the presence of tumor blood vessels were identified as independent predictors of HCC MVI.The ANN model constructed using the dominant features of the combined group(pseudocapsule status+tumor blood vessels+arterial phase+venous phase)demonstrated the best predictive performance for MVI status and was found to be automated,highly operable,and very stable.CONCLUSION The ANN model constructed using the dominant features of the combined group can be recommended as a noninvasive method for preoperative prediction of HCC MVI status.展开更多
BACKGROUND Despite continuous changes in treatment methods,the survival rate for advanced hepatocellular carcinoma(HCC)patients remains low,highlighting the importance of diagnostic methods for HCC.AIM To explore the ...BACKGROUND Despite continuous changes in treatment methods,the survival rate for advanced hepatocellular carcinoma(HCC)patients remains low,highlighting the importance of diagnostic methods for HCC.AIM To explore the efficacy of texture analysis based on multi-parametric magnetic resonance(MR)imaging(MRI)in predicting microvascular invasion(MVI)in preoperative HCC.METHODS This study included 105 patients with pathologically confirmed HCC,categorized into MVI-positive and MVI-negative groups.We employed Original Data Analysis,Principal Component Analysis,Linear Discriminant Analysis(LDA),and Non-LDA(NDA)for texture analysis using multi-parametric MR images to predict preoperative MVI.The effectiveness of texture analysis was determined using the B11 program of the MaZda4.6 software,with results expressed as the misjudgment rate(MCR).RESULTS Texture analysis using multi-parametric MRI,particularly the MI+PA+F dimensionality reduction method combined with NDA discrimination,demonstrated the most effective prediction of MVI in HCC.Prediction accuracy in the pulse and equilibrium phases was 83.81%.MCRs for the combination of T2-weighted imaging(T2WI),arterial phase,portal venous phase,and equilibrium phase were 22.86%,16.19%,20.95%,and 20.95%,respectively.The area under the curve for predicting MVI positivity was 0.844,with a sensitivity of 77.19%and specificity of 91.67%.CONCLUSION Texture analysis of arterial phase images demonstrated superior predictive efficacy for MVI in HCC compared to T2WI,portal venous,and equilibrium phases.This study provides an objective,non-invasive method for preoperative prediction of MVI,offering a theoretical foundation for the selection of clinical therapy.展开更多
基金Supported by the National Natural Science Foundation of China,No.81560278the Health Commission of Guangxi Zhuang Autonomous Region,No.Z20200953,No.G201903023,and No.Z-A20221157Scientific Research and Technology Development Project of Nanning,No.20213122.
文摘BACKGROUND Microvascular invasion(MVI)is a significant risk factor for recurrence and metastasis following hepatocellular carcinoma(HCC)surgery.Currently,there is a paucity of preoperative evaluation approaches for MVI.AIM To investigate the predictive value of texture features and radiological signs based on multiparametric magnetic resonance imaging in the non-invasive preoperative prediction of MVI in HCC.METHODS Clinical data from 97 HCC patients were retrospectively collected from January 2019 to July 2022 at our hospital.Patients were classified into two groups:MVI-positive(n=57)and MVI-negative(n=40),based on postoperative pathological results.The correlation between relevant radiological signs and MVI status was analyzed.MaZda4.6 software and the mutual information method were employed to identify the top 10 dominant texture features,which were combined with radiological signs to construct artificial neural network(ANN)models for MVI prediction.The predictive performance of the ANN models was evaluated using area under the curve,sensitivity,and specificity.ANN models with relatively high predictive performance were screened using the DeLong test,and the regression model of multilayer feedforward ANN with backpropagation and error backpropagation learning method was used to evaluate the models’stability.RESULTS The absence of a pseudocapsule,an incomplete pseudocapsule,and the presence of tumor blood vessels were identified as independent predictors of HCC MVI.The ANN model constructed using the dominant features of the combined group(pseudocapsule status+tumor blood vessels+arterial phase+venous phase)demonstrated the best predictive performance for MVI status and was found to be automated,highly operable,and very stable.CONCLUSION The ANN model constructed using the dominant features of the combined group can be recommended as a noninvasive method for preoperative prediction of HCC MVI status.
基金Supported by National Natural Science Foundation of China,No.81560278the Health Commission of Guangxi Zhuang Autonomous Region,No.Z-A20221157,No.Z20200953,and No.G201903023.
文摘BACKGROUND Despite continuous changes in treatment methods,the survival rate for advanced hepatocellular carcinoma(HCC)patients remains low,highlighting the importance of diagnostic methods for HCC.AIM To explore the efficacy of texture analysis based on multi-parametric magnetic resonance(MR)imaging(MRI)in predicting microvascular invasion(MVI)in preoperative HCC.METHODS This study included 105 patients with pathologically confirmed HCC,categorized into MVI-positive and MVI-negative groups.We employed Original Data Analysis,Principal Component Analysis,Linear Discriminant Analysis(LDA),and Non-LDA(NDA)for texture analysis using multi-parametric MR images to predict preoperative MVI.The effectiveness of texture analysis was determined using the B11 program of the MaZda4.6 software,with results expressed as the misjudgment rate(MCR).RESULTS Texture analysis using multi-parametric MRI,particularly the MI+PA+F dimensionality reduction method combined with NDA discrimination,demonstrated the most effective prediction of MVI in HCC.Prediction accuracy in the pulse and equilibrium phases was 83.81%.MCRs for the combination of T2-weighted imaging(T2WI),arterial phase,portal venous phase,and equilibrium phase were 22.86%,16.19%,20.95%,and 20.95%,respectively.The area under the curve for predicting MVI positivity was 0.844,with a sensitivity of 77.19%and specificity of 91.67%.CONCLUSION Texture analysis of arterial phase images demonstrated superior predictive efficacy for MVI in HCC compared to T2WI,portal venous,and equilibrium phases.This study provides an objective,non-invasive method for preoperative prediction of MVI,offering a theoretical foundation for the selection of clinical therapy.
