scholarly journals Preoperative Prediction of Microvascular Invasion in Patients With Hepatocellular Carcinoma Based on Radiomics Nomogram Using Contrast-Enhanced Ultrasound

2021 ◽  
Vol 11 ◽  
Author(s):  
Di Zhang ◽  
Qi Wei ◽  
Ge-Ge Wu ◽  
Xian-Ya Zhang ◽  
Wen-Wu Lu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Tumor Size ◽  
Multivariate Logistic Regression Analysis ◽  
Microvascular Invasion ◽  
Validation Dataset ◽  
Contrast Enhanced Ultrasound ◽  
Clinical Model ◽  
Portal Venous Phase ◽  
Preoperative Prediction ◽  
Contrast Enhanced

PurposeThis study aimed to develop a radiomics nomogram based on contrast-enhanced ultrasound (CEUS) for preoperatively assessing microvascular invasion (MVI) in hepatocellular carcinoma (HCC) patients.MethodsA retrospective dataset of 313 HCC patients who underwent CEUS between September 20, 2016 and March 20, 2020 was enrolled in our study. The study population was randomly grouped as a primary dataset of 192 patients and a validation dataset of 121 patients. Radiomics features were extracted from the B-mode (BM), artery phase (AP), portal venous phase (PVP), and delay phase (DP) images of preoperatively acquired CEUS of each patient. After feature selection, the BM, AP, PVP, and DP radiomics scores (Rad-score) were constructed from the primary dataset. The four radiomics scores and clinical factors were used for multivariate logistic regression analysis, and a radiomics nomogram was then developed. We also built a preoperative clinical prediction model for comparison. The performance of the radiomics nomogram was evaluated via calibration, discrimination, and clinical usefulness.ResultsMultivariate analysis indicated that the PVP and DP Rad-score, tumor size, and AFP (alpha-fetoprotein) level were independent risk predictors associated with MVI. The radiomics nomogram incorporating these four predictors revealed a superior discrimination to the clinical model (based on tumor size and AFP level) in the primary dataset (AUC: 0.849 vs. 0.690; p < 0.001) and validation dataset (AUC: 0.788 vs. 0.661; p = 0.008), with a good calibration. Decision curve analysis also confirmed that the radiomics nomogram was clinically useful. Furthermore, the significant improvement of net reclassification index (NRI) and integrated discriminatory improvement (IDI) implied that the PVP and DP radiomics signatures may be very useful biomarkers for MVI prediction in HCC.ConclusionThe CEUS-based radiomics nomogram showed a favorable predictive value for the preoperative identification of MVI in HCC patients and could guide a more appropriate surgical planning.

scholarly journals Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma via Multi-Parametric MRI Radiomics

2021 ◽  
Vol 11 ◽  
Author(s):  
Yang Zhang ◽  
Zhenyu Shu ◽  
Qin Ye ◽  
Junfa Chen ◽  
Jianguo Zhong ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Characteristic Curve ◽  
Microvascular Invasion ◽  
Training Dataset ◽  
Validation Dataset ◽  
Clinical Tool ◽  
Combined Model ◽  
Clinical Model ◽  
Portal Venous Phase ◽  
Preoperative Prediction

ObjectivesTo systematically evaluate and compare the predictive capability for microvascular invasion (MVI) in hepatocellular carcinoma (HCC) patients based on radiomics from multi-parametric MRI (mp-MRI) including six sequences when used individually or combined, and to establish and validate the optimal combined model.MethodsA total of 195 patients confirmed HCC were divided into training (n = 136) and validation (n = 59) datasets. All volumes of interest of tumors were respectively segmented on T2-weighted imaging, diffusion-weighted imaging, apparent diffusion coefficient, artery phase, portal venous phase, and delay phase sequences, from which quantitative radiomics features were extracted and analyzed individually or combined. Multivariate logistic regression analyses were undertaken to construct clinical model, respective single-sequence radiomics models, fusion radiomics models based on different sequences and combined model. The accuracy, sensitivity, specificity and area under the receiver operating characteristic curve (AUC) were calculated to evaluate the performance of different models.ResultsAmong nine radiomics models, the model from all sequences performed best with AUCs 0.889 and 0.822 in the training and validation datasets, respectively. The combined model incorporating radiomics from all sequences and effective clinical features achieved satisfactory preoperative prediction of MVI with AUCs 0.901 and 0.840, respectively, and could identify the higher risk population of MVI (P < 0.001). The Delong test manifested significant differences with P < 0.001 in the training dataset and P = 0.005 in the validation dataset between the combined model and clinical model.ConclusionsThe combined model can preoperatively and noninvasively predict MVI in HCC patients and may act as a usefully clinical tool to guide subsequent individualized treatment.

Preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma based on kupffer phase radiomic features of sonazoid contrast-enhanced ultrasound (SCEUS): A prospective study

2022 ◽  
pp. 1-11
Author(s):  
Yi Dong ◽  
Dan Zuo ◽  
Yi-Jie Qiu ◽  
Jia-Ying Cao ◽  
Han-Zhang Wang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Microvascular Invasion ◽  
Ultrasound Images ◽  
Contrast Enhanced Ultrasound ◽  
Model Based ◽  
Preoperative Prediction ◽  
Contrast Enhanced ◽  
Receiver Operating

OBJECTIVES: To establish and evaluate a machine learning radiomics model based on grayscale and Sonazoid contrast enhanced ultrasound images for the preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) patients. METHODS: 100 cases of histopathological confirmed HCC lesions were prospectively included. Regions of interest were segmented on both grayscale and Kupffer phase of Sonazoid contrast enhanced (CEUS) images. Radiomic features were extracted from tumor region and region containing 5 mm of peritumoral liver tissues. Maximum relevance minimum redundancy (MRMR) and Least Absolute Shrinkage and Selection Operator (LASSO) were used for feature selection and Support Vector Machine (SVM) classifier was trained for radiomic signature calculation. Radiomic signatures were incorporated with clinical variables using univariate-multivariate logistic regression for the final prediction of MVI. Receiver operating characteristic curves, calibration curves and decision curve analysis were used to evaluate model’s predictive performance of MVI. RESULTS: Age were the only clinical variable significantly associated with MVI. Radiomic signature derived from Kupffer phase images of peritumoral liver tissues (kupfferPT) displayed a significantly better performance with an area under the receiver operating characteristic curve (AUROC) of 0.800 (95% confidence interval: 0.667, 0.834), the final prediction model using Age and kupfferPT achieved an AUROC of 0.804 (95% CI: 0.723, 0.878), accuracy of 75.0%, sensitivity of 87.5% and specificity of 69.1%. CONCLUSIONS: Radiomic model based on Kupffer phase ultrasound images of tissue adjacent to HCC lesions showed an observable better predictive value compared to grayscale images and has potential value to facilitate preoperative identification of HCC patients at higher risk of MVI.

Potential application of dynamic contrast enhanced ultrasound in predicting microvascular invasion of hepatocellular carcinoma

2021 ◽  
pp. 1-9
Author(s):  
Yi Dong ◽  
Yijie Qiu ◽  
Daohui Yang ◽  
Lingyun Yu ◽  
Dan Zuo ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Area Under The Curve ◽  
Microvascular Invasion ◽  
Contrast Enhanced Ultrasound ◽  
Negative Group ◽  
Clinical Value ◽  
Positive Group ◽  
Quantitative Perfusion ◽  
Dynamic Contrast Enhanced ◽  
Contrast Enhanced

OBJECTIVE: To investigate the clinical value of dynamic contrast enhanced ultrasound (D-CEUS) in predicting the microvascular invasion (MVI) of hepatocellular carcinoma (HCC). PATIENTS AND METHODS: In this retrospective study, 16 patients with surgery and histopathologically proved HCC lesions were included. Patients were classified according to the presence of MVI: MVI positive group (n = 6) and MVI negative group (n = 10). Contrast enhanced ultrasound (CEUS) examinations were performed within a week before surgery. Dynamic analysis was performed by VueBox ® software (Bracco, Italy). Three regions of interests (ROIs) were set in the center of HCC lesions, at the margin of HCC lesions and in the surrounding liver parenchyma accordingly. Time intensity curves (TICs) were generated and quantitative perfusion parameters including WiR (wash-in rate), WoR (wash-out rate), WiAUC (wash-in area under the curve), WoAUC (wash-out area under the curve) and WiPi (wash-in perfusion index) were obtained and analyzed. RESULTS: All of HCC lesions showed arterial hyperenhancement (100 %) and at the late phase as hypoenhancement (75 %) in CEUS. Among all CEUS quantitative parameters, the WiAUC and WoAUC were higher in MVI positive group than in MVI negative group in the center HCC lesions (P <  0.05), WiAUC, WoAUC and WiPI were higher in MVI positive group than in MVI negative group at the margin of HCC lesions. WiR and WoR were significant higher in MVI positive group. CONCLUSIONS: D-CEUS with quantitative perfusion analysis has potential clinical value in predicting the existence of MVI in HCC lesions.

scholarly journals Use of Contrast-Enhanced Ultrasound with Sonazoid for Evaluating the Radiotherapy Efficacy for Hepatocellular Carcinoma

Diagnostics ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 486
Author(s):  
Akihiro Funaoka ◽  
Kazushi Numata ◽  
Atsuya Takeda ◽  
Yusuke Saigusa ◽  
Yuichirou Tsurugai ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Retrospective Study ◽  
Local Recurrence ◽  
Tumor Size ◽  
Size Reduction ◽  
Contrast Enhanced Ultrasound ◽  
Tumor Vascularity ◽  
Contrast Enhanced

Radiotherapy is one of the available curative therapies for hepatocellular carcinoma (HCC). We investigate the use of contrast-enhanced ultrasound using Sonazoid (SCEUS) in evaluating the efficacy of radiotherapy for HCC. We enrolled 59 patients with 59 HCCs in this retrospective study. Tumor size and tumor vascularity were evaluated using SCEUS before and 1, 3, 7, 10, and 13 months after radiotherapy. The median follow-up period was 44.5 months (range: 16–82 months). Of the HCCs, 95% (56/59) had no local recurrence, while 5% (3/59) did. At 13 months after radiotherapy, in cases with no local recurrence, SCEUS showed a reduction in tumor vascularity in all cases, while tumor size reduction (>30% reduction, compared with pre-radiotherapy) was observed in 82.1% (46/56). In all three cases of local recurrence, vascularity and tumor size reduction were not observed during the follow-up period and residual HCCs were demonstrated pathologically. Compared with cases with local recurrence, tumor size reduction and reduction in tumor vascularity (p < 0.001) were significantly greater in cases with no local recurrence at 13 months after radiotherapy. SCEUS may be useful in evaluating radiotherapy efficacy for HCC.

Deep-Learning Based Analysis of Preoperative MRI Predicts Microvascular Invasion and Outcome in Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Bao-Ye sun ◽  
Pei-Yi Gu ◽  
Ruo-Yu Guan ◽  
Cheng Zhou ◽  
Jian-Wei Lu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Deep Learning ◽  
Predictive Model ◽  
Clinical Outcomes ◽  
Area Under The Curve ◽  
Microvascular Invasion ◽  
Dce Mri ◽  
Preoperative Prediction ◽  
Model Combining ◽  
Contrast Enhanced

Abstract Background & Aims: Preoperative prediction of microvascular invasion (MVI) is critical for treatment strategy making in patients with hepatocellular carcinoma (HCC). We aimed to develop a deep learning (DL) model based on preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict the MVI status and clinical outcomes in patients with HCC. Methods We retrospectively included a total of 321 HCC patients with pathologically confirmed MVI status. Preoperative DCE-MRI of these patients were collected, annotated and further analyzed by DL in this study. A predictive model for MVI integrating DL-predicted MVI status (DL-MVI) and clinical parameters was constructed with multivariate logistic regression. Results Of 321 HCC patients, 136 patients were pathologically MVI absent and 185 patients were MVI present. Recurrence-free survival (RFS) and overall survival (OS) were significantly different between the DL-predicted MVI-absent and MVI-present. Among all clinical variables, only DL-predicted MVI status and AFP were independently associated with MVI: DL-MVI (odds ratio [OR]=35.738; 95% confidence interval [CI]: 14.027-91.056; p<0.001), AFP (OR=4.634, 95% CI: 2.576-8.336; p<0.001). To predict the presence of MVI, DL-MVI combined with AFP achieved an area under the curve (AUC) of 0.824. Conclusions Our predictive model combining DL-MVI and AFP achieved good performance for predicting MVI and clinical outcomes in patients with HCC.

scholarly journals A Nomogram Based on Combining Clinical Features and Contrast Enhanced Ultrasound LI-RADS Improves Prediction of Microvascular Invasion in Hepatocellular Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Hang Zhou ◽  
Jiawei Sun ◽  
Tao Jiang ◽  
Jiaqi Wu ◽  
Qunying Li ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Predictive Model ◽  
Clinical Features ◽  
Diagnostic Performance ◽  
Clinical Information ◽  
Microvascular Invasion ◽  
Liver Imaging ◽  
Contrast Enhanced Ultrasound ◽  
Training Cohort ◽  
Contrast Enhanced

PurposesTo establish a predictive model incorporating clinical features and contrast enhanced ultrasound liver imaging and reporting and data system (CEUS LI-RADS) for estimation of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) patients.MethodsIn the retrospective study, 127 HCC patients from two hospitals were allocated as training cohort (n=98) and test cohorts (n=29) based on cutoff time-point, June 2020. Multivariate regression analysis was performed to identify independent indicators for developing predictive nomogram models. The area under receiver operating characteristic (AUC) curve was also determined to establish the diagnostic performance of different predictive models. Corresponding sensitivities and specificities of different models at the cutoff nomogram value were compared.ResultsIn the training cohort, clinical information (larger tumor size, higher AFP level) and CEUS LR-M were significantly correlated with the presence of MVI (all p&lt;0.05). By incorporating clinical information and CEUS LR-M, the predictive model (LR-M+Clin) achieved a desirable diagnostic performance (AUC=0.80 and 0.84) in both cohorts at nomogram cutoff score value of 89. The sensitivity of LR-M+Clin when predicting MVI in HCC patients was higher than that of the clinical model alone (86.7% vs. 46.7%, p=0.027), while specificities were 78.6% and 85.7% (p=0.06), respectively, in the test cohort. In addition, LR-M+Clin exhibited similar AUC and specificity, but a significantly higher sensitivity (86.7%) than those of LR-M alone and LR-5(No)+Clin (both sensitivities=73.3%, both p=0.048).ConclusionThe predictive model incorporating CEUS LR-M and clinical features was able to predict the MVI status of HCC and is a potential reliable preoperative tool for informing treatment.

scholarly journals An MR-based radiomics model for differentiation between hepatocellular carcinoma and focal nodular hyperplasia in non-cirrhotic liver

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Zongren Ding ◽  
Kongying Lin ◽  
Jun Fu ◽  
Qizhen Huang ◽  
Guoxu Fang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Focal Nodular Hyperplasia ◽  
Clinical Decision Making ◽  
Cirrhotic Liver ◽  
Arterial Phase ◽  
Combined Model ◽  
Clinical Model ◽  
Portal Venous Phase ◽  
Contrast Enhanced ◽  
Nodular Hyperplasia

Abstract Purpose We aimed to develop and validate a radiomics model for differentiating hepatocellular carcinoma (HCC) from focal nodular hyperplasia (FNH) in non-cirrhotic livers using Gd-DTPA contrast-enhanced magnetic resonance imaging (MRI). Methods We retrospectively enrolled 149 HCC and 75 FNH patients treated between May 2015 and May 2019 at our center. Patients were randomly allocated to a training (n=156) and validation set (n=68). In total, 2260 radiomics features were extracted from the arterial phase and portal venous phase of Gd-DTPA contrast-enhanced MRI. Using Max-Relevance and Min-Redundancy, random forest, least absolute shrinkage, and selection operator algorithm for dimensionality reduction, multivariable logistic regression was used to build the radiomics model. A clinical model and combined model were also established. The diagnostic performance of the models was compared. Results Eight radiomics features were chosen for the radiomics model, and four clinical factors (age, sex, HbsAg, and enhancement pattern) were chosen for the clinical model. A combined model was built using the factors from the previous models. The classification accuracy of the combined model differentiated HCC from FNH in both the training and validation sets (0.956 and 0.941, respectively). The area under the receiver operating characteristic curve of the combined model was significantly better than that of the clinical model for both the training (0.984 vs. 0.937, p=0.002) and validation (0.972 vs. 0.903, p=0.032) sets. Conclusions The combined model provided a non-invasive quantitative method for differentiating HCC from FNH in non-cirrhotic liver with high accuracy. Our model may assist clinicians in the clinical decision-making process.

Application of real-time contrast-enhanced ultrasound in differential diagnosis of liver malignancies

2019 ◽  
Vol 97 (5) ◽  
pp. 341-344 ◽  
Author(s):  
Hongling Li ◽  
Jin Li
Keyword(s):  
Hepatocellular Carcinoma ◽  
Differential Diagnosis ◽  
Liver Cancer ◽  
Real Time ◽  
Contrast Enhanced Ultrasound ◽  
Portal Venous Phase ◽  
Contrast Enhanced ◽  
Different Types ◽  
Venous Phase ◽  
Malignant Liver

The objective of this study was to evaluate the value of real-time contrast-enhanced ultrasound in differential diagnosis of malignant liver tumors. Sixty patients pathologically diagnosed with liver cancer from May 2016 to May 2017 were selected as the subjects. All patients received ultrasonic diagnosis followed by the observation on dynamic contrast enhancement of the image and the analysis on perfusion characteristics and difference in contrast-enhanced ultrasound results in patients with different types of liver cancer. The signal during the arterial phase of 21 patients with well-differentiated hepatocellular carcinoma increased, including 12 cases of slow withdrawal of portal venous phase. The relative height of artery of 30 patients with middle-low differentiation of hepatocellular carcinoma increased along with the speed at which the contrast media was washed out at the end of the portal phase. In 9 patients with intrahepatic bile duct cancer, the enhancement of the arteries was slightly increased in the periphery of the tumor and low enhancement was found in the portal venous phase. There were some differences in the timing and the intensity of the enhancement of the ultrasound signals with 3 different types of diseases (P < 0.05). There are obvious differences in real-time contrast-enhanced ultrasound features of patients with different types of malignant liver tumor, which may provide references for clinical diagnosis.

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