Objective:To investigate the value of gadoxetate disodium-enhanced MRI combined with T 1 mapping in preoperative prediction of cytokeratin 19 (CK19) and glypican-3 (GPC3) positive dual-phenotype hepatocellular carcinoma (DPHCC). Methods:This case-control study included retrospectively enrolled patients with pathologically confirmed HCC from Central People′s Hospital of Zhanjiang (training set, n=85; December 2020 to July 2022) and the Second Affiliated Hospital, School of Medicine, South China University of Technology (test set, n=35; April 2023 to April 2024). Patients were categorized into CK19 and GPC3 positive DPHCC group (training set=19, test set=11) and non-DPHCC group (training set=66, test set=24) based on postoperative immunohistochemical staining. All patients received preoperative MRI scans, including gadoxetate disodium-enhanced imaging and T 1 mapping. Clinical data were collected, qualitative MRI features were evaluated, and quantitative parameters were measured, including signal intensity, T 1 values, apparent diffusion coefficient (ADC), tumor-to-liver ADC ratio (rADC), tumor-to-liver signal intensity ratio, and T 1 relaxation time reduction rate (ΔT 1%). Statistical comparisons between groups were performed using independent t-tests, Mann-Whitney U tests, or χ2 tests. Multivariate logistic regression identified independent predictors of CK19 and GPC3 positive DPHCC, and a combined model was constructed. Predictive performance was evaluated using area under the receiver operating characteristic curve (AUC), with DeLong test comparing model performance. Results:Significant intergroup differences were observed in alpha-fetoprotein (AFP), total bilirubin, direct bilirubin, DWI target sign, rADC, hepatobiliary-phase T 1 (T 1HBP), and ΔT 1% ( P<0.05). Multivariate analysis identified AFP>20 ng/ml ( OR=5.835, 95% CI 1.019-33.397, P=0.048), DWI target sign ( OR=13.408, 95% CI 2.216-81.131, P=0.005), and ΔT1%≤31% ( OR=14.429, 95% CI 2.166-96.125, P=0.006) as independent predictors of DPHCC. The AUC values of the aforementioned three independent predictors and the combined model for predicting DPHCC were 0.641 (95% CI 0.530-0.742), 0.679 (95% CI 0.569-0.777), 0.740 (95% CI 0.634-0.829), and 0.886 (95% CI 0.799-0.945) in the training set, and 0.568 (95% CI 0.390-0.743), 0.669 (95% CI 0.490-0.818), 0.689 (95% CI 0.511-0.843), and 0.824 (95% CI 0.658-0.931) in the test set, respectively. The DeLong test results showed that in the training set, the diagnostic performance of the combined model was superior to those of the three individual features ( Z=3.68, P<0.001; Z=3.15, P=0.002; Z=3.15, P=0.002). In the test cohort, the combined model demonstrated better diagnostic performance than AFP>20 ng/ml and ΔT 1%≤31% ( Z=2.15, P=0.032; Z=2.12, P=0.034), while no statistically significant difference was observed compared with the DWI target sign ( Z=1.77, P=0.076). Conclusion:The integrated model incorporating clinical data, gadoxetate disodium-enhanced MRI, and T 1 mapping parameters effectively predicts CK19 and GPC3 positive DPHCC.

Objective:To investigate the value of gadoxetate disodium-enhanced MRI combined with T 1 mapping in preoperative prediction of cytokeratin 19 (CK19) and glypican-3 (GPC3) positive dual-phenotype hepatocellular carcinoma (DPHCC). Methods:This case-control study included retrospectively enrolled patients with pathologically confirmed HCC from Central People′s Hospital of Zhanjiang (training set, n=85; December 2020 to July 2022) and the Second Affiliated Hospital, School of Medicine, South China University of Technology (test set, n=35; April 2023 to April 2024). Patients were categorized into CK19 and GPC3 positive DPHCC group (training set=19, test set=11) and non-DPHCC group (training set=66, test set=24) based on postoperative immunohistochemical staining. All patients received preoperative MRI scans, including gadoxetate disodium-enhanced imaging and T 1 mapping. Clinical data were collected, qualitative MRI features were evaluated, and quantitative parameters were measured, including signal intensity, T 1 values, apparent diffusion coefficient (ADC), tumor-to-liver ADC ratio (rADC), tumor-to-liver signal intensity ratio, and T 1 relaxation time reduction rate (ΔT 1%). Statistical comparisons between groups were performed using independent t-tests, Mann-Whitney U tests, or χ2 tests. Multivariate logistic regression identified independent predictors of CK19 and GPC3 positive DPHCC, and a combined model was constructed. Predictive performance was evaluated using area under the receiver operating characteristic curve (AUC), with DeLong test comparing model performance. Results:Significant intergroup differences were observed in alpha-fetoprotein (AFP), total bilirubin, direct bilirubin, DWI target sign, rADC, hepatobiliary-phase T 1 (T 1HBP), and ΔT 1% ( P<0.05). Multivariate analysis identified AFP>20 ng/ml ( OR=5.835, 95% CI 1.019-33.397, P=0.048), DWI target sign ( OR=13.408, 95% CI 2.216-81.131, P=0.005), and ΔT1%≤31% ( OR=14.429, 95% CI 2.166-96.125, P=0.006) as independent predictors of DPHCC. The AUC values of the aforementioned three independent predictors and the combined model for predicting DPHCC were 0.641 (95% CI 0.530-0.742), 0.679 (95% CI 0.569-0.777), 0.740 (95% CI 0.634-0.829), and 0.886 (95% CI 0.799-0.945) in the training set, and 0.568 (95% CI 0.390-0.743), 0.669 (95% CI 0.490-0.818), 0.689 (95% CI 0.511-0.843), and 0.824 (95% CI 0.658-0.931) in the test set, respectively. The DeLong test results showed that in the training set, the diagnostic performance of the combined model was superior to those of the three individual features ( Z=3.68, P<0.001; Z=3.15, P=0.002; Z=3.15, P=0.002). In the test cohort, the combined model demonstrated better diagnostic performance than AFP>20 ng/ml and ΔT 1%≤31% ( Z=2.15, P=0.032; Z=2.12, P=0.034), while no statistically significant difference was observed compared with the DWI target sign ( Z=1.77, P=0.076). Conclusion:The integrated model incorporating clinical data, gadoxetate disodium-enhanced MRI, and T 1 mapping parameters effectively predicts CK19 and GPC3 positive DPHCC.

AIM:To explore the efficacy of lenvatinib(Len)in enhancing the therapeutic effects of immune checkpoint inhibitor for hepatocellular carcinoma(HCC)and to delve into its immunomodulatory mechanisms within the tumor microenvironment.METHODS:The effects of various concentrations of Len on the migration of human umbilical vein endothelial cells(HUVECs)and the secretion of CXC chemokine ligand 10(CXCL10)were investigated,and the mechanism by which Len modulates CXCL10 secretion was validated.An orthotopic HCC model was established,and the mice bearing tumors were randomly allocated into 4 groups:PBS group,BMS-202(PD-1/PD-L1 inhibitor)group,Len group,and Len/BMS-202 group.The progression of the orthotopic liver tumors was monitored with small animal in vivo im-aging techniques.On the 13th day after the treatment,mice were sacrificed and tumor tissues were harvested for analysis.Immunofluorescence was employed to identify apoptosis,vascular architecture,and hypoxic status within the tumor tis-sue.The expression levels of proliferation marker Ki67,transforming growth factor-β(TGF-β),and the infiltration de-grees of CD4+T cells and CD8+T cells in the tumor tissue were monitored with immunohistochemistry.The secretion of im-mune factors interferon-γ(IFN-γ),CXCL10 and TGF-α in the mouse serum was quantified with ELISA.Above all data were followed by statistical analysis.RESULTS:(1)Len could facilitate endothelial cell migration within a specific range and potentiated the response of tumor cells to IFN-γ by blocking fibroblast growth factor receptor(FGFR),thereby increasing the secretion of CXCL10 from the tumor cells.(2)Compared with PBS group,tumor growth was slower in all treatment groups,with Len/BMS-202 group showing the most significant inhibition of tumor growth in tumor-bearing mice(P<0.05).(3)Compared with PBS group and monotherapy groups,Len/BMS-202 significantly promoted tumor tissue apoptosis and inhibited tumor cell proliferation(P<0.05).(4)Compared with PBS group and BMS-202 group,both Len group and Len/BMS-202 group manifested a substantial enhancement in pericytes coverage rate(P<0.01),concomitantly showing a marked improvement in hypoxic conditions(P<0.01).(5)Compared with PBS group and monotherapy groups,Len/BMS-202 group showed a significant increase in the infiltration of CD4+T cells and CD8+T cells within the tumor(P<0.01),along with a marked decrease in the expression of TGF-β(P<0.01).(6)Compared with PBS group,all treatment groups collectively induced varying degrees of secretion of IFN-γ,CXCL10 and TGF-α in mouse serum(P<0.05),with Len/BMS-202 group demonstrating the most pronounced effects(P<0.01).CONCLUSION:Lenvatinib may augment the therapeutic efficacy of BMS-202 in HCC by facilitating tumor vascular normalization,alleviating hypoxic conditions,and enhancing the secretion of CXCL10,thereby synergistically activating the tumor immune microenvironment.

Objective To explore the performance of a new multimodal feature fusion classification model based on distance matching and discriminative representation learning for differentiating high-grade glioma(HGG)from solitary brain metastasis(SBM).Methods We collected multi-parametric magnetic resonance imaging(MRI)data from 61 patients with HGG and 60 with SBM,and delineated regions of interest(ROI)on T1WI,T2WI,T2-weighted fluid attenuated inversion recovery(T2_FLAIR)and post-contrast enhancement T1WI(CE_T1WI)images.The radiomics features were extracted from each sequence using Pyradiomics and fused using a multimodal feature fusion classification model based on distance matching and discriminative representation learning to obtain a classification model.The discriminative performance of the classification model for differentiating HGG from SBM was evaluated using five-fold cross-validation with metrics of specificity,sensitivity,accuracy,and the area under the ROC curve(AUC)and quantitatively compared with other feature fusion models.Visual experiments were conducted to examine the fused features obtained by the proposed model to validate its feasibility and effectiveness.Results The five-fold cross-validation results showed that the proposed multimodal feature fusion classification model had a specificity of 0.871,a sensitivity of 0.817,an accuracy of 0.843,and an AUC of 0.930 for distinguishing HGG from SBM.This feature fusion method exhibited excellent discriminative performance in the visual experiments.Conclusion The proposed multimodal feature fusion classification model has an excellent ability for differentiating HGG from SBM with significant advantages over other feature fusion classification models in discrimination and classification tasks between HGG and SBM.

Objective To predict microvascular invasion(MVI)in hepatocellular carcinoma(HCC)using a model based on multi-phase dynamic-enhanced CT(DCE-CT)radiomics feature and hierarchical fusion of multiple classifiers.Methods We retrospectively collected preoperative DCE-CT images from 111 patients with pathologically confirmed HCC in Guangzhou First People's Hospital between January,2016 and April,2020.The volume of interest was outlined in the early arterial phase,late arterial phase,portal venous phase and equilibrium phase,and radiomics features of these 4 phases were extracted.Seven classifiers based on different algorithms were trained using the filtered feature subsets to obtain multiple base classifiers under each phase.According to the hierarchical fusion strategy,a multi-criteria decision-making-based weight assignment algorithm was used for fusing each base classifier under the same phase with the model after extracting the phase information to obtain the prediction model.The proposed model was evaluated using a 5-fold cross-validation and assessed for area under the ROC curve(AUC),accuracy,sensitivity,and specificity.The prediction model was also compared with the fusion models using a single phase or multiple phases,models based on a single phase with a single classifier,models with different base classifier diversities,and 8 classifier models based on other ensemble methods.Results The experimental results showed that the performance of the proposed model for predicting HCCMVI was optimal after incorporating the 4 phases and 7 classifiers,with AUC,accuracy,sensitivity,and specificity of 0.828,0.766,0.877,and 0.648,respectively.Comparative experiments showed that this prediction model outperformed the models based on a single phase with a single classifier and other ensemble models.Conclusion The proposed prediction model is effective for predicting MVI in HCC with superior performance to other models.

Objective To evaluate the performance of magnetic resonance imaging(MRI)multi-sequence feature imputation and fusion mutual model based on sequence deletion in differentiating high-grade glioma(HGG)from low-grade glioma(LGG).Methods We retrospectively collected multi-sequence MR images from 305 glioma patients,including 189 HGG patients and 116 LGG patients.The region of interest(ROI)of T1-weighted images(T1WI),T2-weighted images(T2WI),T2 fluid attenuated inversion recovery(T2_FLAIR)and post-contrast enhancement T1WI(CE_T1WI)were delineated to extract the radiomics features.A mutual-aid model of MRI multi-sequence feature imputation and fusion based on sequence deletion was used for imputation and fusion of the feature matrix with missing data.The discriminative ability of the model was evaluated using 5-fold cross-validation method and by assessing the accuracy,balanced accuracy,area under the ROC curve(AUC),specificity,and sensitivity.The proposed model was quantitatively compared with other non-holonomic multimodal classification models for discriminating HGG and LGG.Class separability experiments were performed on the latent features learned by the proposed feature imputation and fusion methods to observe the classification effect of the samples in two-dimensional plane.Convergence experiments were used to verify the feasibility of the model.Results For differentiation of HGG from LGG with a missing rate of 10%,the proposed model achieved accuracy,balanced accuracy,AUC,specificity,and sensitivity of 0.777,0.768,0.826,0.754 and 0.780,respectively.The fused latent features showed excellent performance in the class separability experiment,and the algorithm could be iterated to convergence with superior classification performance over other methods at the missing rates of 30%and 50%.Conclusion The proposed model has excellent performance in classification task of HGG and LGG and outperforms other non-holonomic multimodal classification models,demonstrating its potential for efficient processing of non-holonomic multimodal data.

Objective To explore the performance of a new multimodal feature fusion classification model based on distance matching and discriminative representation learning for differentiating high-grade glioma(HGG)from solitary brain metastasis(SBM).Methods We collected multi-parametric magnetic resonance imaging(MRI)data from 61 patients with HGG and 60 with SBM,and delineated regions of interest(ROI)on T1WI,T2WI,T2-weighted fluid attenuated inversion recovery(T2_FLAIR)and post-contrast enhancement T1WI(CE_T1WI)images.The radiomics features were extracted from each sequence using Pyradiomics and fused using a multimodal feature fusion classification model based on distance matching and discriminative representation learning to obtain a classification model.The discriminative performance of the classification model for differentiating HGG from SBM was evaluated using five-fold cross-validation with metrics of specificity,sensitivity,accuracy,and the area under the ROC curve(AUC)and quantitatively compared with other feature fusion models.Visual experiments were conducted to examine the fused features obtained by the proposed model to validate its feasibility and effectiveness.Results The five-fold cross-validation results showed that the proposed multimodal feature fusion classification model had a specificity of 0.871,a sensitivity of 0.817,an accuracy of 0.843,and an AUC of 0.930 for distinguishing HGG from SBM.This feature fusion method exhibited excellent discriminative performance in the visual experiments.Conclusion The proposed multimodal feature fusion classification model has an excellent ability for differentiating HGG from SBM with significant advantages over other feature fusion classification models in discrimination and classification tasks between HGG and SBM.

Objective To predict microvascular invasion(MVI)in hepatocellular carcinoma(HCC)using a model based on multi-phase dynamic-enhanced CT(DCE-CT)radiomics feature and hierarchical fusion of multiple classifiers.Methods We retrospectively collected preoperative DCE-CT images from 111 patients with pathologically confirmed HCC in Guangzhou First People's Hospital between January,2016 and April,2020.The volume of interest was outlined in the early arterial phase,late arterial phase,portal venous phase and equilibrium phase,and radiomics features of these 4 phases were extracted.Seven classifiers based on different algorithms were trained using the filtered feature subsets to obtain multiple base classifiers under each phase.According to the hierarchical fusion strategy,a multi-criteria decision-making-based weight assignment algorithm was used for fusing each base classifier under the same phase with the model after extracting the phase information to obtain the prediction model.The proposed model was evaluated using a 5-fold cross-validation and assessed for area under the ROC curve(AUC),accuracy,sensitivity,and specificity.The prediction model was also compared with the fusion models using a single phase or multiple phases,models based on a single phase with a single classifier,models with different base classifier diversities,and 8 classifier models based on other ensemble methods.Results The experimental results showed that the performance of the proposed model for predicting HCCMVI was optimal after incorporating the 4 phases and 7 classifiers,with AUC,accuracy,sensitivity,and specificity of 0.828,0.766,0.877,and 0.648,respectively.Comparative experiments showed that this prediction model outperformed the models based on a single phase with a single classifier and other ensemble models.Conclusion The proposed prediction model is effective for predicting MVI in HCC with superior performance to other models.

Objective To evaluate the performance of magnetic resonance imaging(MRI)multi-sequence feature imputation and fusion mutual model based on sequence deletion in differentiating high-grade glioma(HGG)from low-grade glioma(LGG).Methods We retrospectively collected multi-sequence MR images from 305 glioma patients,including 189 HGG patients and 116 LGG patients.The region of interest(ROI)of T1-weighted images(T1WI),T2-weighted images(T2WI),T2 fluid attenuated inversion recovery(T2_FLAIR)and post-contrast enhancement T1WI(CE_T1WI)were delineated to extract the radiomics features.A mutual-aid model of MRI multi-sequence feature imputation and fusion based on sequence deletion was used for imputation and fusion of the feature matrix with missing data.The discriminative ability of the model was evaluated using 5-fold cross-validation method and by assessing the accuracy,balanced accuracy,area under the ROC curve(AUC),specificity,and sensitivity.The proposed model was quantitatively compared with other non-holonomic multimodal classification models for discriminating HGG and LGG.Class separability experiments were performed on the latent features learned by the proposed feature imputation and fusion methods to observe the classification effect of the samples in two-dimensional plane.Convergence experiments were used to verify the feasibility of the model.Results For differentiation of HGG from LGG with a missing rate of 10%,the proposed model achieved accuracy,balanced accuracy,AUC,specificity,and sensitivity of 0.777,0.768,0.826,0.754 and 0.780,respectively.The fused latent features showed excellent performance in the class separability experiment,and the algorithm could be iterated to convergence with superior classification performance over other methods at the missing rates of 30%and 50%.Conclusion The proposed model has excellent performance in classification task of HGG and LGG and outperforms other non-holonomic multimodal classification models,demonstrating its potential for efficient processing of non-holonomic multimodal data.

Objective To explore the CT dominant phase and optimal classification model in differenting clear cell renal cell carcinoma (ccRCC) from fat‐poor angiomyolipoma (fpAML) through quantitative multiple‐phase CT radiomic features analysis. Methods Clinical and imaging data of 195 cases pathologically confirmed ccRCC (n=131) and fpAML (n=64) were retrospectively studied. All the patients underwent non‐contrast enhanced CT scans and dynamic multi‐phase (corticomedullary phase, medullary phase and excretion phase) contrast‐enhanced CT scans. Regions of interest (ROIs) were manually delineated based on the selected image slices with the maximal diameter of the lesion using ITK‐SNAP software, followed by the acquisition of candidate CT radiomic feature sets from each phase with statistically significant differences by using Mann‐Whitney U test. Then, using the synthetic minority oversampling technique (SMOTE), 232 classification models which are composed of 29 different feature selection algorithms (top 10 features were chosen by the backward elimination method) and 8 different classifiers were constructed. Employing the 5‐fold cross‐validation method, the performance of each classification models for each phase was evaluated using accuracy (ACC), sensitivity (SEN), specificity (SPE) and area under receiver operating characteristic curve (AUC), to acquire dominant CT phases and the optimal classification models for distingushing ccRCC and fpAML, along with the key imaging radiomic features. Results In this study, the mean maximal diameter of ccRCC and fpAML lesions were (3.9±1.4) cm, and (3.5±1.7) cm, respectively, and there was no statistically significant difference in the size of the tumor between two groups (P>0.05). From 102 initial imaging feature sets, the total number of candidate imaging feature sets (P<0.05) were:non‐enhanced phase (n=26), corticomedullary phase (n=71), medullary phase (n=68), excretion phase (n=62). Among the 232 classification models through different combination of classifiers and feature selectors, the amount of classification models which achieved the maximum of AUC value (AUCmax) from different CT phases were: non‐enhanced phase (n=106, 45.7%), corticomedullary phase (n=94, 40.5%), medullary phase (n=23, 9.9%), excretion phase (n=9, 3.9%). Imaging features from non‐enhanced phase and corticomedullary phase yielded higher performance compared with medullary phase and excretion phase, with the corresponding optimal prediction models were SVM‐fisher_score (AUC: 0.897, ACC: 83%, SEN: 84%, SPE:80%) and Logistic Regression‐RFS (AUC: 0.891, ACC: 83%, SEN: 81%, SPE: 89%), respectively. Conclusions The quantitative imaging features from non‐enhanced and corticomedullary phase have better performance among proposed classification models than that from medullary phase and excretion phase. Furthermore, it is feasible to acquire proper combination of feature selection and classifiers to achieve high performance in identifying ccRCC and fpAML.