Effect of contrast-enhanced ultrasound on differential diagnosis of intrahepatic cholangiocarcinoma and arterial phase enhanced hepatic inflammatory lesions

Ultrasound and contrast-enhanced ultrasound (CEUS) in infective liver lesions

Infektiöse fokale Leberläsionen (FLL) treten in der klinischen Praxis häufig auf, wobei bakterielle Leberabszesse die Hälfte ausmachen. Eine genaue Diagnose der FLL ist für die Auswahl der am besten geeigneten Therapie und zur Vorbeugung von Komplikationen unerlässlich. Ziel der aktuellen Arbeit ist es, den Nutzen von Ultraschall und kontrastmittelverstärktem Ultraschall (CEUS) zur Erkennung und Charakterisierung infektiöser Leberläsionen zu beschreiben.

Cholangiocarcinoma Evaluation via Imaging and Artificial Intelligence

BACKGROUND

Cholangiocarcinoma (CCA) is a relatively rare malignant biliary system tumor, and yet it represents the second most common primary hepatic neoplasm, following hepatocellular carcinoma. Regardless of the type, location, or etiology, the survival prognosis of these tumors remains poor. The only method of cure for CCA is complete surgical resection, but part of patients with complete resection are still subject to local recurrence or distant metastasis.

SUMMARY

Over the last several decades, our understanding of the molecular biology of CCA has increased tremendously, diagnostic and evaluative techniques have evolved, and novel therapeutic approaches have been established. Key Messages: This review provides an overview of preoperative imaging evaluations of CCA. Furthermore, relevant information about artificial intelligence (AI) in medical imaging is discussed, as well as the development of AI in CCA treatment.

Differentiation of regenerative nodule, dysplastic nodule, and small hepatocellular carcinoma in cirrhotic patients: a contrast-enhanced ultrasound-based multivariable model analysis

OBJECTIVE

To develop a contrast-enhanced ultrasound (CEUS)-based model for differentiating cirrhotic liver lesions and for active surveillance of hepatocellular carcinoma (HCC).

METHODS

Patients with focal liver lesions (FLLs) with biopsy/resection-proven pathology and pre-procedure CEUS were enrolled from our institution between January 2011 and November 2014. Univariable and multivariable regression models were constructed using qualitative CEUS features and/or contrast arrival time ratio (CAT_R). The optimism-adjusted Harrell's generalized concordance index (C_H) was used to quantify the discriminatory ability of each CEUS feature and model.

RESULTS

A total of 149 patients (113 men and 36 women) with 162 FLLs were enrolled with mean age 53.4 ± 12.7 years. A 0.1-unit reduction in CAT_R was associated with a 68% increase in the odds of having a higher nodule ranking (RN < DN < small HCC) (OR, 0.32; 95% CI, 0.20-0.50, p < .001). Arterial phase hypoenhancement and isoenhancement were inversely associated with a higher nodule ranking compared to hyperenhancement. Late-phase isoenhancement was associated with lower odds of a higher nodule ranking. The CEUS + CAT_R model (C_H 0.92, 0.89-0.95) provided greater discriminatory ability when compared to the CAT_R model (ΔC_H 0.09, 0.04-0.13, p < .001) and the CEUS model (ΔC_H 0.03, 0.01-0.05, p = .02).

CONCLUSIONS

Our results provide preliminary evidence that multivariable regression model constructed using both qualitative CEUS features and CAT_R provides the greatest discriminatory ability to differentiate RN, DN, and small HCC in patients with cirrhosis, and might allow for active surveillance of the progression of cirrhotic liver lesions.

KEY POINTS

• Proportional odds logistic regression models based on qualitative CEUS features and/or CAT_R can be used for differentiating cirrhotic liver lesions and for active surveillance of HCC. • The reduction of CAT_R (RN < DN < small HCC) was strongly associated with high-risk cirrhotic liver nodules. • Inclusion of CAT_R in the CEUS prediction model significantly improved its performance for cirrhotic liver lesions risk-stratification.

Comparative Diagnostic Accuracy of Contrast-Enhanced Ultrasound and Shear Wave Elastography in Differentiating Benign and Malignant Lesions: A Network Meta-Analysis

Background: We performed a network meta-analysis to compare the diagnostic accuracy of contrast-enhanced ultrasound (CEUS) and shear wave elastography (SWE) in differentiating benign and malignant lesions in different body sites.

Methods: A computerized literature search of Medline, Embase, SCOPUS, and Web of Science was performed using relevant keywords. Following data extraction, we calculated sensitivity, specificity, positive likelihood ratio (LR), negative LR, and diagnostic odds ratio (DOR) for CEUS, and SWE compared to histopathology as a reference standard. Statistical analyses were conducted by MetaDiSc (version 1.4) and R software (version 3.4.3).

Results: One hundred and fourteen studies (15,926 patients) were pooled in the final analyses. Network meta-analysis showed that CEUS had significantly higher DOR than SWE (DOR = 27.14, 95%CI [2.30, 51.97]) in breast cancer detection. However, there were no significant differences between CEUS and SWE in hepatic (DOR = −6.67, 95%CI [−15.08, 1.74]) and thyroid cancer detection (DOR = 3.79, 95%CI [−3.10, 10.68]). Interestingly, ranking analysis showed that CEUS achieved higher DOR in detecting breast and thyroid cancer, while SWE achieved higher DOR in detecting hepatic cancer. The overall DOR for CEUS in detecting renal cancer was 53.44, 95%CI [29.89, 95.56] with an AUROC of 0.95, while the overall DOR for SWE in detecting prostate cancer was 25.35, 95%CI [7.15, 89.89] with an AUROC of 0.89.

Conclusion: Both diagnostic tests showed relatively high sensitivity and specificity in detecting malignant tumors in different organs. Network meta-analysis showed that CEUS had higher diagnostic accuracy than SWE in detecting breast and thyroid cancer, while SWE had higher accuracy in detecting hepatic cancer. However, the results were not statistically significant in hepatic and thyroid malignancies. Further head-to-head comparisons are needed to confirm the optimal imaging technique to differentiate each cancer type.