BACKGROUND Synchronous liver metastasis(SLM)is a significant contributor to morbidity in colorectal cancer(CRC).There are no effective predictive device integration algorithms to predict adverse SLM events during the ...BACKGROUND Synchronous liver metastasis(SLM)is a significant contributor to morbidity in colorectal cancer(CRC).There are no effective predictive device integration algorithms to predict adverse SLM events during the diagnosis of CRC.AIM To explore the risk factors for SLM in CRC and construct a visual prediction model based on gray-level co-occurrence matrix(GLCM)features collected from magnetic resonance imaging(MRI).METHODS Our study retrospectively enrolled 392 patients with CRC from Yichang Central People’s Hospital from January 2015 to May 2023.Patients were randomly divided into a training and validation group(3:7).The clinical parameters and GLCM features extracted from MRI were included as candidate variables.The prediction model was constructed using a generalized linear regression model,random forest model(RFM),and artificial neural network model.Receiver operating characteristic curves and decision curves were used to evaluate the prediction model.RESULTS Among the 392 patients,48 had SLM(12.24%).We obtained fourteen GLCM imaging data for variable screening of SLM prediction models.Inverse difference,mean sum,sum entropy,sum variance,sum of squares,energy,and difference variance were listed as candidate variables,and the prediction efficiency(area under the curve)of the subsequent RFM in the training set and internal validation set was 0.917[95%confidence interval(95%CI):0.866-0.968]and 0.09(95%CI:0.858-0.960),respectively.CONCLUSION A predictive model combining GLCM image features with machine learning can predict SLM in CRC.This model can assist clinicians in making timely and personalized clinical decisions.展开更多
BACKGROUND The most important consideration in determining treatment strategies for undifferentiated early gastric cancer(UEGC)is the risk of lymph node metastasis(LNM).Therefore,identifying a potential biomarker that...BACKGROUND The most important consideration in determining treatment strategies for undifferentiated early gastric cancer(UEGC)is the risk of lymph node metastasis(LNM).Therefore,identifying a potential biomarker that predicts LNM is quite useful in determining treatment.AIM To develop a machine learning(ML)-based integral procedure to construct the LNM gray-level co-occurrence matrix(GLCM)prediction model.METHODS We retrospectively selected 526 cases of UEGC confirmed through pathological examination after radical gastrectomy without endoscopic treatment in four tertiary hospitals between January 2015 to December 2021.We extracted GLCM-based features from grayscale images and applied ML to the classification of candidate predictive variables.The robustness and clinical utility of each model were evaluated based on the following factors:Receiver operating characteristic curve(ROC),decision curve analysis,and clinical impact curve.RESULTS GLCM-based feature extraction significantly correlated with LNM.The top 7 GLCM-based factors included inertia value 0°(IV_0),inertia value 45°(IV_45),inverse gap 0°(IG_0),inverse gap 45°(IG_45),inverse gap full angle(IG_all),Haralick 30°(Haralick_30),Haralick full angle(Haralick_all),and Entropy.The areas under the ROC curve(AUCs)of the random forest classifier(RFC)model,support vector machine,eXtreme gradient boosting,artificial neural network,and decision tree ranged from 0.805[95%confidence interval(CI):0.258-1.352]to 0.925(95%CI:0.378-1.472)in the training set and from 0.794(95%CI:0.237-1.351)to 0.912(95%CI:0.355-1.469)in the testing set,respectively.The RFC(training set:AUC:0.925,95%CI:0.378-1.472;testing set:AUC:0.912,95%CI:0.355-1.469)model that incorporates Entropy,Haralick_all,Haralick_30,IG_all,IG_45,IG_0,and IV_45 had the highest predictive accuracy.CONCLUSION The evaluation results indicate that the method of selecting radiological and textural features becomes more effective in the LNM discrimination against UEGC patients.Additionally,the MLbased prediction model developed using the RFC can be used to derive treatment options and identify LNM,which can hence improve clinical outcomes.展开更多
Co-occurrence matrices have been successfully applied in texture classification and segmentation.However,they have poor computation performance in real-time application.In this paper,the efficient co-occurrence matrix...Co-occurrence matrices have been successfully applied in texture classification and segmentation.However,they have poor computation performance in real-time application.In this paper,the efficient co-occurrence matrix solution for defect detection is focused on,and a method of Fuzzy Label Co-occurrence Matrix (FLCM) set is proposed.In this method,all gray levels are supposed to subject to some fuzzy sets called fuzzy tonal sets and three defective features are defined.Features of FLCM set with various parameters are combined for the final judgment.Unlike many methods,image acquired for learning hasn't to be entirely free of defects.It is shown that the method produces high accuracy and can be a competent candidate for plain colour fabric defect detection.展开更多
BACKGROUND There are factors that significantly increase the risk of postoperative pulmonary infections in patients with primary hepatic carcinoma(PHC).Previous reports have shown that over 10%of patients with PHC exp...BACKGROUND There are factors that significantly increase the risk of postoperative pulmonary infections in patients with primary hepatic carcinoma(PHC).Previous reports have shown that over 10%of patients with PHC experience postoperative pulmonary infections.Thus,it is crucial to prioritize the prevention and treatment of postoperative pulmonary infections in patients with PHC.AIM To identify the risk factors for postoperative pulmonary infection in patients with PHC and develop a prediction model to aid in postoperative management.METHODS We retrospectively collected data from 505 patients who underwent hepatobiliary surgery between January 2015 and February 2023 in the Department of Hepatobiliary and Pancreaticospleen Surgery.Radiomics data were selected for statistical analysis,and clinical pathological parameters and imaging data were included in the screening database as candidate predictive variables.We then developed a pulmonary infection prediction model using three different models:An artificial neural network model;a random forest model;and a generalized linear regression model.Finally,we evaluated the accuracy and robustness of the prediction model using the receiver operating characteristic curve and decision curve analyses.RESULTS Among the 505 patients,86 developed a postoperative pulmonary infection,resulting in an incidence rate of 17.03%.Based on the gray-level co-occurrence matrix,we identified 14 categories of radiomic data for variable screening of pulmonary infection prediction models.Among these,energy,contrast,the sum of squares(SOS),the inverse difference(IND),mean sum(MES),sum variance(SUV),sum entropy(SUE),and entropy were independent risk factors for pulmonary infection after hepatectomy and were listed as candidate variables of machine learning prediction models.The random forest model algorithm,in combination with IND,SOS,MES,SUE,SUV,and entropy,demonstrated the highest prediction efficiency in both the training and internal verification sets,with areas under the curve of 0.823 and 0.801 and a 95%confidence interval of 0.766-0.880 and 0.744-0.858,respectively.The other two types of prediction models had prediction efficiencies between areas under the curve of 0.734 and 0.815 and 95%confidence intervals of 0.677-0.791 and 0.766-0.864,respectively.CONCLUSION Postoperative pulmonary infection in patients undergoing hepatectomy may be related to risk factors such as IND,SOS,MES,SUE,SUV,energy,and entropy.The prediction model in this study based on diffusion-weighted images,especially the random forest model algorithm,can better predict and estimate the risk of pulmonary infection in patients undergoing hepatectomy,providing valuable guidance for postoperative management.展开更多
Liver cancer is the second leading cause of cancer death worldwide.Early tumor detection may help identify suitable treatment and increase the survival rate.Medical imaging is a non-invasive tool that can help uncover...Liver cancer is the second leading cause of cancer death worldwide.Early tumor detection may help identify suitable treatment and increase the survival rate.Medical imaging is a non-invasive tool that can help uncover abnormalities in human organs.Magnetic Resonance Imaging(MRI),in particular,uses magnetic fields and radio waves to differentiate internal human organs tissue.However,the interpretation of medical images requires the subjective expertise of a radiologist and oncologist.Thus,building an automated diagnosis computer-based system can help specialists reduce incorrect diagnoses.This paper proposes a hybrid automated system to compare the performance of 3D features and 2D features in classifying magnetic resonance liver tumor images.This paper proposed two models;the first one employed the 3D features while the second exploited the 2D features.The first system uses 3D texture attributes,3D shape features,and 3D graphical deep descriptors beside an ensemble classifier to differentiate between four 3D tumor categories.On top of that,the proposed method is applied to 2D slices for comparison purposes.The proposed approach attained 100%accuracy in discriminating between all types of tumors,100%Area Under the Curve(AUC),100%sensitivity,and 100%specificity and precision as well in 3D liver tumors.On the other hand,the performance is lower in 2D classification.The maximum accuracy reached 96.4%for two classes and 92.1%for four classes.The top-class performance of the proposed system can be attributed to the exploitation of various types of feature selection methods besides utilizing the ReliefF features selection technique to choose the most relevant features associated with different classes.The novelty of this work appeared in building a highly accurate system under specific circumstances without any processing for the images and human input,besides comparing the performance between 2D and 3D classification.In the future,the presented work can be extended to be used in the huge dataset.Then,it can be a reliable,efficient Computer Aided Diagnosis(CAD)system employed in hospitals in rural areas.展开更多
In order to study the temporal and spatial evolution characteristics of gas-liquid two-phase flow structure,a high-speed dynamic camera was utilized to acquire the dynamic image information of seven typical gas-liquid...In order to study the temporal and spatial evolution characteristics of gas-liquid two-phase flow structure,a high-speed dynamic camera was utilized to acquire the dynamic image information of seven typical gas-liquid two-phase flow patterns in vertical and inclined 30° upward pipes with the testing ranges of superficial water velocity 0.02—0.4 m·s-1 and superficial gas velocity 0.005—2.7 m·s-1.The gray level co-occurrence matrix(GLCM)was used to quantitatively characterize 2D information in the local neighborhood of image for analyzing flow pattern image features and the four time-varying characteristic parameter indices which represented image texture structures of different flow patterns were extracted.Then the transition of flow structure in the development process of flow patterns and calculated Lempel-Ziv sequence complexity of the four time-varying characteristic parameter indices were analyzed,and compared with the complexity measurement,fractal scale and recurrence plot determinism calculated by conductance fluctuating signals.The study showed that the dynamic parameter evolution trends of flow pattern image texture structure characteristics described the variation of different flow pattern structures and dynamics complexity,and the correlation index(COR)was more effective to reflect the complexity of flow pattern dynamics than others.It indicated that the proposed dynamic image analysis method was helpful to understanding the flow pattern temporal and spatial evolution characteristics and also was an effective approach to identifying the gas-liquid two-phase flow patterns.展开更多
文摘BACKGROUND Synchronous liver metastasis(SLM)is a significant contributor to morbidity in colorectal cancer(CRC).There are no effective predictive device integration algorithms to predict adverse SLM events during the diagnosis of CRC.AIM To explore the risk factors for SLM in CRC and construct a visual prediction model based on gray-level co-occurrence matrix(GLCM)features collected from magnetic resonance imaging(MRI).METHODS Our study retrospectively enrolled 392 patients with CRC from Yichang Central People’s Hospital from January 2015 to May 2023.Patients were randomly divided into a training and validation group(3:7).The clinical parameters and GLCM features extracted from MRI were included as candidate variables.The prediction model was constructed using a generalized linear regression model,random forest model(RFM),and artificial neural network model.Receiver operating characteristic curves and decision curves were used to evaluate the prediction model.RESULTS Among the 392 patients,48 had SLM(12.24%).We obtained fourteen GLCM imaging data for variable screening of SLM prediction models.Inverse difference,mean sum,sum entropy,sum variance,sum of squares,energy,and difference variance were listed as candidate variables,and the prediction efficiency(area under the curve)of the subsequent RFM in the training set and internal validation set was 0.917[95%confidence interval(95%CI):0.866-0.968]and 0.09(95%CI:0.858-0.960),respectively.CONCLUSION A predictive model combining GLCM image features with machine learning can predict SLM in CRC.This model can assist clinicians in making timely and personalized clinical decisions.
基金Supported by the General Project-Social Development Field of Shaanxi Province Science and Technology Department,No. 2021SF-313Innovation Capability Support Plan of Shaanxi Science and Technology Department-Science and Technology Innovation Team,No. 2020TD-048
文摘BACKGROUND The most important consideration in determining treatment strategies for undifferentiated early gastric cancer(UEGC)is the risk of lymph node metastasis(LNM).Therefore,identifying a potential biomarker that predicts LNM is quite useful in determining treatment.AIM To develop a machine learning(ML)-based integral procedure to construct the LNM gray-level co-occurrence matrix(GLCM)prediction model.METHODS We retrospectively selected 526 cases of UEGC confirmed through pathological examination after radical gastrectomy without endoscopic treatment in four tertiary hospitals between January 2015 to December 2021.We extracted GLCM-based features from grayscale images and applied ML to the classification of candidate predictive variables.The robustness and clinical utility of each model were evaluated based on the following factors:Receiver operating characteristic curve(ROC),decision curve analysis,and clinical impact curve.RESULTS GLCM-based feature extraction significantly correlated with LNM.The top 7 GLCM-based factors included inertia value 0°(IV_0),inertia value 45°(IV_45),inverse gap 0°(IG_0),inverse gap 45°(IG_45),inverse gap full angle(IG_all),Haralick 30°(Haralick_30),Haralick full angle(Haralick_all),and Entropy.The areas under the ROC curve(AUCs)of the random forest classifier(RFC)model,support vector machine,eXtreme gradient boosting,artificial neural network,and decision tree ranged from 0.805[95%confidence interval(CI):0.258-1.352]to 0.925(95%CI:0.378-1.472)in the training set and from 0.794(95%CI:0.237-1.351)to 0.912(95%CI:0.355-1.469)in the testing set,respectively.The RFC(training set:AUC:0.925,95%CI:0.378-1.472;testing set:AUC:0.912,95%CI:0.355-1.469)model that incorporates Entropy,Haralick_all,Haralick_30,IG_all,IG_45,IG_0,and IV_45 had the highest predictive accuracy.CONCLUSION The evaluation results indicate that the method of selecting radiological and textural features becomes more effective in the LNM discrimination against UEGC patients.Additionally,the MLbased prediction model developed using the RFC can be used to derive treatment options and identify LNM,which can hence improve clinical outcomes.
基金Open Fund of the Key Lab of the Ministry of Education for Image Information Processing and Intelligent Control,China(No.200702)
文摘Co-occurrence matrices have been successfully applied in texture classification and segmentation.However,they have poor computation performance in real-time application.In this paper,the efficient co-occurrence matrix solution for defect detection is focused on,and a method of Fuzzy Label Co-occurrence Matrix (FLCM) set is proposed.In this method,all gray levels are supposed to subject to some fuzzy sets called fuzzy tonal sets and three defective features are defined.Features of FLCM set with various parameters are combined for the final judgment.Unlike many methods,image acquired for learning hasn't to be entirely free of defects.It is shown that the method produces high accuracy and can be a competent candidate for plain colour fabric defect detection.
文摘BACKGROUND There are factors that significantly increase the risk of postoperative pulmonary infections in patients with primary hepatic carcinoma(PHC).Previous reports have shown that over 10%of patients with PHC experience postoperative pulmonary infections.Thus,it is crucial to prioritize the prevention and treatment of postoperative pulmonary infections in patients with PHC.AIM To identify the risk factors for postoperative pulmonary infection in patients with PHC and develop a prediction model to aid in postoperative management.METHODS We retrospectively collected data from 505 patients who underwent hepatobiliary surgery between January 2015 and February 2023 in the Department of Hepatobiliary and Pancreaticospleen Surgery.Radiomics data were selected for statistical analysis,and clinical pathological parameters and imaging data were included in the screening database as candidate predictive variables.We then developed a pulmonary infection prediction model using three different models:An artificial neural network model;a random forest model;and a generalized linear regression model.Finally,we evaluated the accuracy and robustness of the prediction model using the receiver operating characteristic curve and decision curve analyses.RESULTS Among the 505 patients,86 developed a postoperative pulmonary infection,resulting in an incidence rate of 17.03%.Based on the gray-level co-occurrence matrix,we identified 14 categories of radiomic data for variable screening of pulmonary infection prediction models.Among these,energy,contrast,the sum of squares(SOS),the inverse difference(IND),mean sum(MES),sum variance(SUV),sum entropy(SUE),and entropy were independent risk factors for pulmonary infection after hepatectomy and were listed as candidate variables of machine learning prediction models.The random forest model algorithm,in combination with IND,SOS,MES,SUE,SUV,and entropy,demonstrated the highest prediction efficiency in both the training and internal verification sets,with areas under the curve of 0.823 and 0.801 and a 95%confidence interval of 0.766-0.880 and 0.744-0.858,respectively.The other two types of prediction models had prediction efficiencies between areas under the curve of 0.734 and 0.815 and 95%confidence intervals of 0.677-0.791 and 0.766-0.864,respectively.CONCLUSION Postoperative pulmonary infection in patients undergoing hepatectomy may be related to risk factors such as IND,SOS,MES,SUE,SUV,energy,and entropy.The prediction model in this study based on diffusion-weighted images,especially the random forest model algorithm,can better predict and estimate the risk of pulmonary infection in patients undergoing hepatectomy,providing valuable guidance for postoperative management.
文摘Liver cancer is the second leading cause of cancer death worldwide.Early tumor detection may help identify suitable treatment and increase the survival rate.Medical imaging is a non-invasive tool that can help uncover abnormalities in human organs.Magnetic Resonance Imaging(MRI),in particular,uses magnetic fields and radio waves to differentiate internal human organs tissue.However,the interpretation of medical images requires the subjective expertise of a radiologist and oncologist.Thus,building an automated diagnosis computer-based system can help specialists reduce incorrect diagnoses.This paper proposes a hybrid automated system to compare the performance of 3D features and 2D features in classifying magnetic resonance liver tumor images.This paper proposed two models;the first one employed the 3D features while the second exploited the 2D features.The first system uses 3D texture attributes,3D shape features,and 3D graphical deep descriptors beside an ensemble classifier to differentiate between four 3D tumor categories.On top of that,the proposed method is applied to 2D slices for comparison purposes.The proposed approach attained 100%accuracy in discriminating between all types of tumors,100%Area Under the Curve(AUC),100%sensitivity,and 100%specificity and precision as well in 3D liver tumors.On the other hand,the performance is lower in 2D classification.The maximum accuracy reached 96.4%for two classes and 92.1%for four classes.The top-class performance of the proposed system can be attributed to the exploitation of various types of feature selection methods besides utilizing the ReliefF features selection technique to choose the most relevant features associated with different classes.The novelty of this work appeared in building a highly accurate system under specific circumstances without any processing for the images and human input,besides comparing the performance between 2D and 3D classification.In the future,the presented work can be extended to be used in the huge dataset.Then,it can be a reliable,efficient Computer Aided Diagnosis(CAD)system employed in hospitals in rural areas.
文摘In order to study the temporal and spatial evolution characteristics of gas-liquid two-phase flow structure,a high-speed dynamic camera was utilized to acquire the dynamic image information of seven typical gas-liquid two-phase flow patterns in vertical and inclined 30° upward pipes with the testing ranges of superficial water velocity 0.02—0.4 m·s-1 and superficial gas velocity 0.005—2.7 m·s-1.The gray level co-occurrence matrix(GLCM)was used to quantitatively characterize 2D information in the local neighborhood of image for analyzing flow pattern image features and the four time-varying characteristic parameter indices which represented image texture structures of different flow patterns were extracted.Then the transition of flow structure in the development process of flow patterns and calculated Lempel-Ziv sequence complexity of the four time-varying characteristic parameter indices were analyzed,and compared with the complexity measurement,fractal scale and recurrence plot determinism calculated by conductance fluctuating signals.The study showed that the dynamic parameter evolution trends of flow pattern image texture structure characteristics described the variation of different flow pattern structures and dynamics complexity,and the correlation index(COR)was more effective to reflect the complexity of flow pattern dynamics than others.It indicated that the proposed dynamic image analysis method was helpful to understanding the flow pattern temporal and spatial evolution characteristics and also was an effective approach to identifying the gas-liquid two-phase flow patterns.
