Characterization of Focal Liver Masses: A Multicenter Comparison of Contrast-Enhanced Ultrasound, Computed Tomography, and Magnetic Resonance Imaging

Imaging features and management of focal liver lesions

Notably, the number of incidentally detected focal liver lesions (FLLs) has increased dramatically in recent years due to the increased use of radiological imaging. The diagnosis of FLLs can be made through a well-documented medical history, physical examination, laboratory tests, and appropriate imaging methods. Although benign FLLs are more common than malignant ones in adults, even in patients with primary malignancy, accurate diagnosis of incidental FLLs is of utmost clinical significance. In clinical practice, FLLs are frequently evaluated non-invasively using ultrasound (US), computed tomography (CT), and magnetic resonance imaging (MRI). Although US is a cost-effective and widely used imaging method, its diagnostic specificity and sensitivity for FLL characterization are limited. FLLs are primarily characterized by obtaining enhancement patterns through dynamic contrast-enhanced CT and MRI. MRI is a problem-solving method with high specificity and sensitivity, commonly used for the evaluation of FLLs that cannot be characterized by US or CT. Recent technical advancements in MRI, along with the use of hepatobiliary-specific MRI contrast agents, have significantly improved the success of FLL characterization and reduced unnecessary biopsies. The American College of Radiology (ACR) appropriateness criteria are evidence-based recommendations intended to assist clinicians in selecting the optimal imaging or treatment option for their patients. ACR Appropriateness Criteria Liver Lesion-Initial Characterization guideline provides recommendations for the imaging methods that should be used for the characterization of incidentally detected FLLs in various clinical scenarios. The American College of Gastroenterology (ACG) Clinical Guideline offers evidence-based recommendations for both the diagnosis and management of FLL. American Association for the Study of Liver Diseases (AASLD) Practice Guidance provides an approach to the diagnosis and management of patients with hepatocellular carcinoma. In this article, FLLs are reviewed with a comprehensive analysis of ACR Appropriateness Criteria, ACG Clinical Guideline, AASLD Practice Guidance, and current medical literature from peer-reviewed journals. The article includes a discussion of imaging methods used for the assessment of FLL, current recommended imaging techniques, innovations in liver imaging, contrast agents, imaging features of common nonmetastatic benign and malignant FLL, as well as current management recommendations.

A multicenter clinical AI system study for detection and diagnosis of focal liver lesions

Early and accurate diagnosis of focal liver lesions is crucial for effective treatment and prognosis. We developed and validated a fully automated diagnostic system named Liver Artificial Intelligence Diagnosis System (LiAIDS) based on a diverse sample of 12,610 patients from 18 hospitals, both retrospectively and prospectively. In this study, LiAIDS achieved an F1-score of 0.940 for benign and 0.692 for malignant lesions, outperforming junior radiologists (benign: 0.830-0.890, malignant: 0.230-0.360) and being on par with senior radiologists (benign: 0.920-0.950, malignant: 0.550-0.650). Furthermore, with the assistance of LiAIDS, the diagnostic accuracy of all radiologists improved. For benign and malignant lesions, junior radiologists' F1-scores improved to 0.936-0.946 and 0.667-0.680 respectively, while seniors improved to 0.950-0.961 and 0.679-0.753. Additionally, in a triage study of 13,192 consecutive patients, LiAIDS automatically classified 76.46% of patients as low risk with a high NPV of 99.0%. The evidence suggests that LiAIDS can serve as a routine diagnostic tool and enhance the diagnostic capabilities of radiologists for liver lesions.

Unique portal venous phase imaging discordance between CEUS and MRI: a valuable predictor of intrahepatic cholangiocarcinoma?

PURPOSE

We have long noted unique portal venous phase (PVP) imaging discordance of focal liver masses between CEUS, showing rapid marked washout, and MRI, showing progressive or sustained enhancement. We postulate association of this unique discordance with intrahepatic cholangiocarcinoma (ICC) and causal relationship to different contrast agent behavior. We investigate this unique discordance, propose its clinical significance for ICC diagnosis, and confirm further histologic associations.

METHODS

Cases were collected within our CEUS department and from pathology records over a ten-year interval. This retrospective review includes 99 patients, 73 with confirmed ICC and 26 other diagnoses, showing unique PVP discordance. The CEUS and MRI enhancement characteristics were compared for all patients.

RESULTS

Unique discordance is identified in 67/73 (92%) ICC and difference between the PVP appearance on MRI and CEUS is statistically significant (p <  0.0001). Arterial phase enhancement did not show statistically significant difference between CEUS and MRI, p >  0.05. Other diagnoses showing unique discordance include especially lymphoma (n = 7), sclerosed hemangioma (n = 6), HCC (n = 4), metastases (n = 2), and other rare entities.

CONCLUSION

ICC shows this discrepant intermodality enhancement pattern in a statistically significant number of cases and should be considered along with other LR-M features in at-risk patients. Discordance is also rarely seen in a number of other liver lesions.

Diagnostic Performance of an Artificial Intelligence Model Based on Contrast-Enhanced Ultrasound in Patients with Liver Lesions: A Comparative Study with Clinicians

Contrast-enhanced ultrasound (CEUS) is widely used in the characterization of liver tumors; however, the evaluation of perfusion patterns using CEUS has a subjective character. This study aims to evaluate the accuracy of an automated method based on CEUS for classifying liver lesions and to compare its performance with that of two experienced clinicians. The system used for automatic classification is based on artificial intelligence (AI) algorithms. For an interpretation close to the clinical setting, both clinicians knew which patients were at high risk for hepatocellular carcinoma (HCC), but only one was aware of all the clinical data. In total, 49 patients with 59 liver tumors were included. For the benign and malignant classification, the AI model outperformed both clinicians in terms of specificity (100% vs. 93.33%); still, the sensitivity was lower (74% vs. 93.18% vs. 90.91%). In the second stage of multiclass diagnosis, the automatic model achieved a diagnostic accuracy of 69.93% for HCC and 89.15% for liver metastases. Readers demonstrated greater diagnostic accuracy for HCC (83.05% and 79.66%) and liver metastases (94.92% and 96.61%) compared to the AI system; however, both were experienced sonographers. The AI model could potentially assist and guide less-experienced clinicians to discriminate malignant from benign liver tumors with high accuracy and specificity.

A Novel Technology for Resolution of CEUS Imaging Problems in Patients With High BMI and Fatty Liver

OBJECTIVES

In high-BMI patients with and without fatty liver, we evaluate performance of a commercially available specially designed ultrasound probe (SDP) for scanning at depth. Greyscale and contrast-enhanced ultrasound (CEUS) capability of SDP for parenchymal assessment and liver mass characterization, emphasizing HCC, is compared with standard curvilinear probes.

METHODS

This retrospective study included 60 patients. Fifty-five with measured BMI included 46/55 (84%) overweight or obese, and 9/55(16%) in the normal range with severe fatty liver. Fifty-six patients with focal liver abnormality included 37 with a mass and 19 with post-ablative treatment site. Masses included 23 confirmed malignancies, 15 HCC, 4 ICC, and 4 metastases. SDP followed suboptimal ultrasound using a standard probe. Images with varying fat content were compared for depth of penetration on greyscale and ability of CEUS to diagnose tumors.

RESULTS

SDP showed statistically significant improvement P = <.05 in CEUS penetration for all degrees of fatty liver (mild, moderate, and severe). In malignant tumors, SDP improved detection of lesion washout in the portal venous/late phase (PVP/LP) at depth >10 cm, and in all malignant masses (P < .05). Fifteen confirmed deep HCC showed arterial phase hyperenhancement on standard probe in 10/15 (67%) and 15/15 (100%) on SDP. PVP/LP washout on standard probe was shown in 4/15 (26%) and on SDP, 14/15, (93%). Therefore, 93% of LR-5 tumors were diagnosed with SDP. Removing necessity for biopsy.

CONCLUSIONS

Metabolic syndrome and obesity challenge ultrasound, especially CEUS. SDP overcame limitations of standard probes for CEUS penetration especially in fatty liver. SDP was optimal for the liver mass characterization by detecting washout.

[Ultrasound diagnostics of the liver : Principles and important pathologies]

Diffuse changes in the liver parenchyma, focal lesions and blood flow in hepatic vessels can be assessed using ultrasound. Screening by ultrasound can be used to detect hepatocellular carcinomas as possible malignant sequelae of liver cirrhosis. As metastases are far more frequent than primary malignant liver tumors, secondary malignant neoplasms should be taken into consideration as a differential diagnosis in the presence of focal liver lesions. This particularly concerns patients with a known metastatic disease. Benign focal liver lesions are often incidentally discovered in women of childbearing age. Cysts, hemangiomas and focal nodular hyperplasia mostly show typical morphological features in ultrasound and do not require further follow-up; however, with hepatic adenomas, regular follow-up is recommended due to the risk of bleeding and/or malignant transformation.

Shear-wave elastography combined with contrast-enhanced ultrasound algorithm for noninvasive characterization of focal liver lesions

PURPOSE

To establish shear-wave elastography (SWE) combined with contrast-enhanced ultrasound (CEUS) algorithm (SCCA) and improve the diagnostic performance in differentiating focal liver lesions (FLLs).

MATERIAL AND METHODS

We retrospectively selected patients with FLLs between January 2018 and December 2019 at the First Affiliated Hospital of Sun Yat-sen University. Histopathology was used as a standard criterion except for hemangiomas and focal nodular hyperplasia. CEUS with SonoVue (Bracco Imaging) and SCCA combining CEUS and maximum value of elastography with < 20 kPa and > 90 kPa thresholds were used for the diagnosis of FLLs. The diagnostic performance of CEUS and SCCA was calculated and compared.

RESULTS

A total of 171 FLLs were included, with 124 malignant FLLs and 47 benign FLLs. The area under curve (AUC), sensitivity, and specificity in detecting malignant FLLs were 0.83, 91.94%, and 74.47% for CEUS, respectively, and 0.89, 91.94%, and 85.11% for SCCA, respectively. The AUC of SCCA was significantly higher than that of CEUS (P = 0.019). Decision curves indicated that SCCA provided greater clinical benefits. The SCCA provided significantly improved prediction of clinical outcomes, with a net reclassification improvement index of 10.64% (P = 0.018) and integrated discrimination improvement of 0.106 (P = 0.019). For subgroup analysis, we divided the FLLs into a chronic-liver-disease group (n = 88 FLLs) and a normal-liver group (n = 83 FLLs) according to the liver background. In the chronic-liver-disease group, there were no differences between the CEUS-based and SCCA diagnoses. In the normal-liver group, the AUC of SCCA and CEUS in the characterization of FLLs were 0.89 and 0.83, respectively (P = 0.018).

CONCLUSION

SCCA is a feasible tool for differentiating FLLs in patients with normal liver backgrounds. Further investigations are necessary to validate the universality of this algorithm.

Interpretable Machine Learning for Characterization of Focal Liver Lesions by Contrast-Enhanced Ultrasound

This work proposes an interpretable radiomics approach to differentiate between malignant and benign focal liver lesions (FLLs) on contrast-enhanced ultrasound (CEUS). Although CEUS has shown promise for differential FLLs diagnosis, current clinical assessment is performed only by qualitative analysis of the contrast enhancement patterns. Quantitative analysis is often hampered by the unavoidable presence of motion artifacts and by the complex, spatiotemporal nature of liver contrast enhancement, consisting of multiple, overlapping vascular phases. To fully exploit the wealth of information in CEUS, while coping with these challenges, here we propose combining features extracted by the temporal and spatiotemporal analysis in the arterial phase enhancement with spatial features extracted by texture analysis at different time points. Using the extracted features as input, several machine learning classifiers are optimized to achieve semiautomatic FLLs characterization, for which there is no need for motion compensation and the only manual input required is the location of a suspicious lesion. Clinical validation on 87 FLLs from 72 patients at risk for hepatocellular carcinoma (HCC) showed promising performance, achieving a balanced accuracy of 0.84 in the distinction between benign and malignant lesions. Analysis of feature relevance demonstrates that a combination of spatiotemporal and texture features is needed to achieve the best performance. Interpretation of the most relevant features suggests that aspects related to microvascular perfusion and the microvascular architecture, together with the spatial enhancement characteristics at wash-in and peak enhancement, are important to aid the accurate characterization of FLLs.