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<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.

Contrast-Enhanced Ultrasound Liver Imaging Reporting and Data System in Diagnosing Hepatocellular Carcinoma: Diagnostic Performance and Interobserver Agreement

2020 ◽  
Author(s):  
Hang Zhou ◽  
Chao Zhang ◽  
Linyao Du ◽  
Jiapeng Jiang ◽  
Qing Zhao ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Diagnostic Performance ◽  
Data System ◽  
Liver Imaging ◽  
Contrast Enhanced Ultrasound ◽  
High Risk Patients ◽  
Contrast Enhanced ◽  
Sensitivity Range ◽  
Risk Patients

Abstract Objectives To determine the diagnostic performance and inter-reader agreement of the contrast-enhanced ultrasound liver imaging reporting and data system (CEUS-LI-RADS) for diagnosing hepatocellular carcinoma (HCC) in high-risk patients. Methods In this prospective study, CEUS-LI-RADS categories (LR-5 for predicting HCC) were assigned by six blinded readers and compared to the definitive HCC diagnosis in patients with liver cirrhosis per the 2017 China Liver Cancer Guidelines (CLCG). CEUS features were recorded in 96 patients with 96 histology-proven lesions. The diagnostic performance of LR-5 was described by the sensitivity, specificity and accuracy. Multi-reader agreement was assessed by using intraclass correlation coefficients (ICC). Results In cirrhotic patients, the specificity of LR-5 (range: 92.7–100.0 %) was statistically higher than that of CLCG for each reader (range: 28.6–64.3 %). However, the sensitivity (range: 38.6–63.6 %) and accuracy (range: 53.4–70.7 %) were statistically lower in CEUS-LIRADS than in CLCG (sensitivity range: 88.6–100.0 %; accuracy range: 77.6–86.2 %). Only fair to moderate inter-reader agreement was achieved for the CEUS-LI-RADS category (ICC = 0.595) and washout appearance (ICC range: 0.338 to 0.555). Neither nodule-in-nodule nor mosaic architecture was observed more often in HCC (all P > 0.05), with poor inter-reader consistency for both (both ICC < 0.20). Conclusion CEUS-LI-RADS category 5 has a high specificity but a low accuracy for identifying HCC in high-risk patients. Inter-reader agreement is not satisfactory concerning CEUS-LIRADS category and washout appearance. Moreover, the clinical value of ancillary features favoring HCC is quite limited.

Predictive model for microvascular invasion of hepatocellular carcinoma among candidates for either hepatectomy or liver transplantation.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 4079-4079
Author(s):  
Hidetoshi Nitta ◽  
Marc Antoine Allard ◽  
Mylene Sebagh ◽  
Gabriella Pittau ◽  
Oriana Ciacio ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Liver Transplantation ◽  
Predictive Model ◽  
Validation Cohort ◽  
Microvascular Invasion ◽  
Roc Curve Analysis ◽  
Training Cohort ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct

4079 Background: Microvascular invasion (MVI) is the strongest prognostic factor following surgery of hepatocellular carcinoma (HCC). However, it is usually not available on the preoperative setting. A predictive model of MVI in patients scheduled for hepatic resection (HR) or liver transplantation (LT) would thus help guiding treatment strategy. The aim of this study was to develop a predictive model for MVI of HCC before either HR or LT. Methods: HCC patients who consecutively performed HR or LT from January 1994 to June 2016 at a single institution were subdivided into a training and validation cohort. Risk factors for MVI in the training cohort were used to develop a predictive model for MVI, to be validated in the validation cohort. The outcomes of the HR and LT patients with high or low MVI probability based on the model, were compared using propensity score matching (PSM). Cut-off values for continuous factors were determined based on ROC curve analysis. Results: A total of 910 patients (425 HR, 485 LT) were included in the training (n = 637) and validation (n = 273) cohorts. In the training cohort, multivariate analysis demonstrated that alpha-fetoprotein ≥100ng/ml ( p < 0.0001), largest tumor size ≥40mm ( p = 0.0002), non-boundary HCC type on contrast-enhanced CT ( p = 0.001), neutrophils-to-lymphocytes ratio ≥3.2 ( p = 0.002), aspartate aminotransferase ≥62U/l ( p = 0.02) were independently associated with MVI. Combinations of these 5 factors varied the MVI probability from 15.5% to 91.1%. This predictive model achieved a good c-index of 0.76 in the validation cohort. In PSM (109 HR, 109 LT), there was no difference in survival between HR and LT patients among the high MVI probability (≥50%) patients, (5y-OS; 46.3% vs 42.2%, p = 0.77, 5y-RFS; 54.0% vs 28.8%, p = 0.21). Among the low probability ( < 50%), survival was significantly decreased following HR compared with LT (5y-OS; 54.1% vs 78.8%, p = 0.007, 5y-RFS; 17.3% vs 86.1%, p< 0.0001). Conclusions: This model developed from preoperative data allows reliable prediction of MVI, and may thus help with preoperative decisions about the suitability of HR or LT in patients with HCC.

Diagnostic performance of intravascular perfusion based contrast-enhanced ultrasound LI-RADS in the evaluation of hepatocellular carcinoma

2021 ◽  
pp. 1-9
Author(s):  
Dongsheng Zuo ◽  
Kefeng Yang ◽  
Size Wu
Keyword(s):  
Hepatocellular Carcinoma ◽  
Diagnostic Performance ◽  
Focal Liver Lesions ◽  
Liver Imaging ◽  
Diagnostic Efficiency ◽  
Liver Lesions ◽  
Contrast Enhanced Ultrasound ◽  
Predictive Values ◽  
Contrast Enhanced ◽  
Sensitivity Specificity

BACKGROUND: The contrast-enhanced ultrasound (CEUS) liver imaging reporting and data system (LI-RADS) is a relative new algorithm for hepatocellular carcinoma (HCC) assessment. OBJECTIVE: To validate the diagnostic efficiency of the intravascular perfusion based CEUS LI-RADS for HCC. METHODS: Archives of 873 patients with focal liver lesions (FLLs) undergoing CEUS were reviewed, and target images were read by two sonologists independently according to the CEUS LI-RADS. The diagnostic performance was calculated and compared. RESULTS: Assessment with reference to CEUS LI-RADS, 87 of 218 FLLs (39.9%) were categorized as LR-5, 131 of 218 FLLs (60.1%) were categorized as non-LR-5, 19 of 99 HCCs were categorized as non-LR-5, and 7 of 119 non-HCCs were categorized as LR-5. The sensitivity, specificity, AUROC, positive and negative predictive values of CEUS LI-RADS for diagnosing HCC were 80.81%(95%CI: 71.7%–88.0%), 94.1%(95%CI: 88.3%–97.6%), 0.87 (95%CI: 0.82–0.92), 91.9%(95%CI: 84.1%–96.7%), and 85.5%(95%CI: 78.3%–91.0%), respectively. CONCLUSIONS: The diagnostic efficiency of the intravascular perfusion based CEUS LI-RADS for the evaluation of HCCs is very good.

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 Noninvasive Diagnosis of Hepatocellular Carcinoma on Sonazoid-Enhanced US: Value of the Kupffer Phase

Diagnostics ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 141
Author(s):  
Hiroshi Takahashi ◽  
Katsutoshi Sugimoto ◽  
Naohisa Kamiyama ◽  
Kentaro Sakamaki ◽  
Tatsuya Kakegawa ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Diagnostic Performance ◽  
Late Phase ◽  
Imaging Feature ◽  
Arterial Phase ◽  
Liver Imaging ◽  
Noninvasive Diagnosis ◽  
Contrast Enhanced ◽  
Treatment Naïve ◽  
Hepatic Lesions

The aim of this study was to compare the diagnostic performance of Contrast-Enhanced US Liver Imaging Reporting and Data System (CEUS LI-RADS) version 2017, which includes portal- and late-phase washout as a major imaging feature, with that of modified CEUS LI-RADS, which includes Kupffer-phase findings as a major imaging feature. Participants at risk of hepatocellular carcinoma (HCC) with treatment-naïve hepatic lesions (≥1 cm) were recruited and underwent Sonazoid-enhanced US. Arterial phase hyperenhancement (APHE), washout time, and echogenicity in the Kupffer phase were evaluated using both criteria. The diagnostic performance of both criteria was analyzed using the McNemar test. The evaluation was performed on 102 participants with 102 lesions (HCCs (n = 52), non-HCC malignancies (n = 36), and benign (n = 14)). Among 52 HCCs, non-rim APHE was observed in 92.3% (48 of 52). By 5 min, 73.1% (38 of 52) of HCCs showed mild washout, while by 10 min or in the Kupffer phase, 90.4% (47 of 52) of HCCs showed hypoenhancement. The sensitivity (67.3%; 35 of 52; 95% CI: 52.9%, 79.7%) of modified CEUS LI-RADS criteria was higher than that of CEUS LI-RADS criteria (51.9%; 27 of 52; 95% CI: 37.6%, 66.0%) (p = 0.0047). In conclusion, non-rim APHE with hypoenhancement in the Kupffer phase on Sonazoid-enhanced US is a feasible criterion for diagnosing 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.

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.

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