Perfusion quantification of contrast-enhanced ultrasound images based on coherence enhancing diffusion and competitive clustering

Intraplaque neovascularization (IPNV) is a characteristic feature of the vulnerable plaques. In this study of neovessels of carotid plaques, we assessed intraplaque echogenicity and plaque surface morphology, and performed contrast-enhanced ultrasound (CEUS) to observe the location and grading of neovessels to identify the vulnerability of plaques. The results showed that plaque with a ruptured fibrous cap on the histopathological images presented as a sunken or fissured surface on corresponding ultrasound images. Both in the symptomatic and asymptomatic groups, plaque echogenicity did not correlate with neovessels grading. The neovessels that appeared in the tunica media and base of the plaque in the symptomatic and asymptomatic group on CEUS had no statistical difference ( p > 0.05), but those located in the fibrous cap and shoulders had a significant statistical difference ( p = 0.000). Statistical differences were not found in the locations of IPNV on CEUS and histopathology (all p > 0.05). The sensitivity (82.4%, 56/68) and specificity (77.4%, 24/31) of IPNV location were higher than those (77.9%, 53/68; 45.2%, 14/31) of IPNV grading in the identification of plaque vulnerability. IPNVs located at the fibrous cap and shoulders on CEUS is a reliable indicator for identifying plaque vulnerability.

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

Clinical Hemorheology and Microcirculation ◽

10.3233/ch-211363 ◽

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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