Computer-aided diagnosis of focal liver lesions by use of physicians' subjective classification of echogenic patterns in baseline and contrast-enhanced ultrasonography

Diagnostic Performance of Artificial Intelligence in Detection of Hepatocellular Carcinoma: A Meta-analysis

Due to the increasing interest in the use of artificial intelligence (AI) algorithms in hepatocellular carcinoma detection, we performed a systematic review and meta-analysis to pool the data on diagnostic performance metrics of AI and to compare them with clinicians' performance. A search in PubMed and Scopus was performed in January 2024 to find studies that evaluated and/or validated an AI algorithm for the detection of HCC. We performed a meta-analysis to pool the data on the metrics of diagnostic performance. Subgroup analysis based on the modality of imaging and meta-regression based on multiple parameters were performed to find potential sources of heterogeneity. The risk of bias was assessed using Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) and Prediction Model Study Risk of Bias Assessment Tool (PROBAST) reporting guidelines. Out of 3177 studies screened, 44 eligible studies were included. The pooled sensitivity and specificity for internally validated AI algorithms were 84% (95% CI: 81,87) and 92% (95% CI: 90,94), respectively. Externally validated AI algorithms had a pooled sensitivity of 85% (95% CI: 78,89) and specificity of 84% (95% CI: 72,91). When clinicians were internally validated, their pooled sensitivity was 70% (95% CI: 60,78), while their pooled specificity was 85% (95% CI: 77,90). This study implies that AI can perform as a diagnostic supplement for clinicians and radiologists by screening images and highlighting regions of interest, thus improving workflow.

Artificial intelligence for the classification of focal liver lesions in ultrasound - a systematic review

Focal liver lesions are detected in about 15% of abdominal ultrasound examinations. The diagnosis of frequent benign lesions can be determined reliably based on the characteristic B-mode appearance of cysts, hemangiomas, or typical focal fatty changes. In the case of focal liver lesions which remain unclear on B-mode ultrasound, contrast-enhanced ultrasound (CEUS) increases diagnostic accuracy for the distinction between benign and malignant liver lesions. Artificial intelligence describes applications that try to emulate human intelligence, at least in subfields such as the classification of images. Since ultrasound is considered to be a particularly examiner-dependent technique, the application of artificial intelligence could be an interesting approach for an objective and accurate diagnosis. In this systematic review we analyzed how artificial intelligence can be used to classify the benign or malignant nature and entity of focal liver lesions on the basis of B-mode or CEUS data. In a structured search on Scopus, Web of Science, PubMed, and IEEE, we found 52 studies that met the inclusion criteria. Studies showed good diagnostic performance for both the classification as benign or malignant and the differentiation of individual tumor entities. The results could be improved by inclusion of clinical parameters and were comparable to those of experienced investigators in terms of diagnostic accuracy. However, due to the limited spectrum of lesions included in the studies and a lack of independent validation cohorts, the transfer of the results into clinical practice is limited.

Assessing Detection Accuracy of Computerized Sonographic Features and Computer-Assisted Reading Performance in Differentiating Thyroid Cancers

For ultrasound imaging of thyroid nodules, medical guidelines are all based on findings of sonographic features to provide clinicians management recommendations. Due to the recent development of artificial intelligence and machine learning (AI/ML) technologies, there have been computer-assisted detection (CAD) software devices available for clinical use to detect and quantify the sonographic features of thyroid nodules. This study is to validate the accuracy of the computerized sonographic features (CSF) by a CAD software device, namely, AmCAD-UT, and then to assess how the reading performance of clinicians (readers) can be improved providing the computerized features. The feature detection accuracy is tested against the ground truth established by a panel of thyroid specialists and a multiple-reader multiple-case (MRMC) study is performed to assess the sequential reading performance with the assistance of the CSF. Five computerized features, including anechoic area, hyperechoic foci, hypoechoic pattern, heterogeneous texture, and indistinct margin, were tested, with AUCs ranging from 0.888~0.946, 0.825~0.913, 0.812~0.847, 0.627~0.77, and 0.676~0.766, respectively. With the five CSFs, the sequential reading performance of 18 clinicians is found significantly improved, with the AUC increasing from 0.720 without CSF to 0.776 with CSF. Our studies show that the computerized features are consistent with the clinicians’ findings and provide additional value in assisting sonographic diagnosis.

The feasibility to use artificial intelligence to aid detecting focal liver lesions in real-time ultrasound: a preliminary study based on videos

Despite the wide availability of ultrasound machines for hepatocellular carcinoma surveillance, an inadequate number of expert radiologists performing ultrasounds in remote areas remains a primary barrier for surveillance. We demonstrated feasibility of artificial intelligence (AI) to aid in the detection of focal liver lesions (FLLs) during ultrasound. An AI system for FLL detection in ultrasound videos was developed. Data in this study were prospectively collected at a university hospital. We applied a two-step training strategy for developing the AI system by using a large collection of ultrasound snapshot images and frames from full-length ultrasound videos. Detection performance of the AI system was evaluated and then compared to detection performance by 25 physicians including 16 non-radiologist physicians and 9 radiologists. Our dataset contained 446 videos (273 videos with 387 FLLs and 173 videos without FLLs) from 334 patients. The videos yielded 172,035 frames with FLLs and 1,427,595 frames without FLLs for training on the AI system. The AI system achieved an overall detection rate of 89.8% (95%CI: 84.5-95.0) which was significantly higher than that achieved by non-radiologist physicians (29.1%, 95%CI: 21.2-37.0, p < 0.001) and radiologists (70.9%, 95%CI: 63.0-78.8, p < 0.001). Median false positive detection rate by the AI system was 0.7% (IQR: 1.3%). AI system operation speed reached 30-34 frames per second, showing real-time feasibility. A further study to demonstrate whether the AI system can assist operators during ultrasound examinations is warranted.

One stop shop approach for the diagnosis of liver hemangioma

Hepatic hemangioma is usually detected on a routine ultrasound examination because of silent clinical behaviour. The typical ultrasound appearance of hemangioma is easily recognizable and quickly guides the diagnosis without the need for further investigation. But there is also an entire spectrum of atypical and uncommon ultrasound features and our review comes to detail these particular aspects. An atypical aspect in standard ultrasound leads to the continuation of explorations with an imaging investigation with contrast substance [ultrasound/ computed tomography/or magnetic resonance imaging (MRI)]. For a clinician who practices ultrasound and has an ultrasound system in the room, the easiest, fastest, non-invasive and cost-effective method is contrast enhanced ultrasound (CEUS). Approximately 85% of patients are correctly diagnosed with this method and the patient has the correct diagnosis in about 30 min without fear of malignancy and without waiting for a computer tomography (CT)/MRI appointment. In less than 15% of patients CEUS does not provide a conclusive appearance; thus, CT scan or MRI becomes mandatory and liver biopsy is rarely required. The aim of this updated review is to synthesize the typical and atypical ultrasound aspects of hepatic hemangioma in the adult patient and to propose a fast, non-invasive and cost-effective clinical-ultrasound algorithm for the diagnosis of hepatic hemangioma.

Preoperative Pathological Grading of Hepatocellular Carcinoma Using Ultrasomics of Contrast-Enhanced Ultrasound

RATIONALE AND OBJECTIVES

To develop an ultrasomics model for preoperative pathological grading of hepatocellular carcinoma (HCC) using contrast-enhanced ultrasound (CEUS).

MATERIAL AND METHODS

A total of 235 HCCs were retrospectively enrolled, including 65 high-grade and 170 low-grade HCCs. Representative images of four-phase CEUS were selected from the baseline sonography, arterial, portal venous, and delayed phase images. Tumor ultrasomics features were automatically extracted using Ultrasomics-Platform software. Models were built via the classifier support vector machine, including an ultrasomics model using the ultrasomics features, a clinical model using the clinical factors, and a combined model using them both. Model performances were tested in the independent validation cohort considering efficiency and clinical usefulness.

RESULTS

A total of 1502 features were extracted from each image. After the reproducibility test and dimensionality reduction, 25 ultrasomics features and 3 clinical factors were selected to build the models. In the validation cohort, the combined model showed the best predictive power, with an area under the curve value of 0.785 (95% confidence interval [CI] 0.662-0.909), compared to the ultrasomics model of 0.720 (95% CI 0.576-0.864) and the clinical model of 0.665 (95% CI 0.537-0.793). Decision curve analysis suggested that the combined model was clinically useful, with a corresponding net benefit of 0.760 compared to the other two models.

CONCLUSION

We presented an ultrasomics-clinical model based on multiphase CEUS imaging and clinical factors, which showed potential value for the preoperative discrimination of HCC pathological grades.

The AFSUMB Consensus Statements and Recommendations for the Clinical Practice of Contrast-Enhanced Ultrasound using Sonazoid

The first edition of the guidelines for the use of ultrasound contrast agents was published in 2004, dealing with liver applications. The second edition of the guidelines in 2008 reflected changes in the available contrast agents and updated the guidelines for the liver, as well as implementing some nonliver applications. The third edition of the contrast-enhanced ultrasound (CEUS) guidelines was the joint World Federation for Ultrasound in Medicine and Biology-European Federation of Societies for Ultrasound in Medicine and Biology (WFUMB-EFSUMB) venture in conjunction with other regional US societies such as Asian Federation of Societies for Ultrasound in Medicine and Biology, resulting in a simultaneous duplicate on liver CEUS in the official journals of both WFUMB and EFSUMB in 2013. However, no guidelines were described mainly for Sonazoid due to limited clinical experience only in Japan and Korea. The new proposed consensus statements and recommendations provide general advice on the use of Sonazoid and are intended to create standard protocols for the use and administration of Sonazoid in hepatic and pancreatobiliary applications in Asian patients and to improve patient management.

The AFSUMB Consensus Statements and Recommendations for the Clinical Practice of Contrast-Enhanced Ultrasound using Sonazoid

The first edition of the guidelines for the use of ultrasound contrast agents was published in 2004, dealing with liver applications. The second edition of the guidelines in 2008 reflected changes in the available contrast agents and updated the guidelines for the liver, as well as implementing some nonliver applications. The third edition of the contrast-enhanced ultrasound (CEUS) guidelines was the joint World Federation for Ultrasound in Medicine and Biology-European Federation of Societies for Ultrasound in Medicine and Biology (WFUMB-EFSUMB) venture in conjunction with other regional US societies such as Asian Federation of Societies for Ultrasound in Medicine and Biology, resulting in a simultaneous duplicate on liver CEUS in the official journals of both WFUMB and EFSUMB in 2013. However, no guidelines were described mainly for Sonazoid due to limited clinical experience only in Japan and Korea. The new proposed consensus statements and recommendations provide general advice on the use of Sonazoid and are intended to create standard protocols for the use and administration of Sonazoid in hepatic and pancreatobiliary applications in Asian patients and to improve patient management.

Contrast-Enhanced Ultrasound Quantification: From Kinetic Modeling to Machine Learning

Ultrasound contrast agents (UCAs) have opened up immense diagnostic possibilities by combined use of indicator dilution principles and dynamic contrast-enhanced ultrasound (DCE-US) imaging. UCAs are microbubbles encapsulated in a biocompatible shell. With a rheology comparable to that of red blood cells, UCAs provide an intravascular indicator for functional imaging of the (micro)vasculature by quantitative DCE-US. Several models of the UCA intravascular kinetics have been proposed to provide functional quantitative maps, aiding diagnosis of different pathological conditions. This article is a comprehensive review of the available methods for quantitative DCE-US imaging based on temporal, spatial and spatiotemporal analysis of the UCA kinetics. The recent introduction of novel UCAs that are targeted to specific vascular receptors has advanced DCE-US to a molecular imaging modality. In parallel, new kinetic models of increased complexity have been developed. The extraction of multiple quantitative maps, reflecting complementary variables of the underlying physiological processes, requires an integrative approach to their interpretation. A probabilistic framework based on emerging machine-learning methods represents nowadays the ultimate approach, improving the diagnostic accuracy of DCE-US imaging by optimal combination of the extracted complementary information. The current value and future perspective of all these advances are critically discussed.

Contrast-enhanced US for characterization of focal liver lesions: a comprehensive meta-analysis

OBJECTIVES

This meta-analysis was performed to evaluate the accuracy of contrast-enhanced ultrasound (CEUS) in differentiating malignant from benign focal liver lesions (FLLs).

METHODS

Cochrane Library, PubMed and Web of Science databases were systematically searched and checked for studies using CEUS in characterization of FLLs. Data necessary to construct 2×2 contingency tables were extracted from included studies. The QUADAS tool was utilized to assess the methodologic quality of the studies. Meta-analysis included data pooling, subgroup analyses, meta-regression and investigation of publication bias was comprehensively performed.

RESULTS

Fifty-seven studies were included in this meta-analysis and the overall diagnostic accuracy in characterization of FLLs was as follows: pooled sensitivity, 0.92 (95%CI: 0.91-0.93); pooled specificity, 0.87 (95%CI: 0.86-0.88); diagnostic odds ratio, 104.20 (95%CI: 70.42-154.16). Subgroup analysis indicated higher diagnostic accuracy of the second-generation contrast agents (CAs) than the first-generation CA (Levovist; DOR: 118.27 vs. 62.78). Furthermore, Sonazoid demonstrated the highest diagnostic accuracy among three major CAs (SonoVue, Levovist and Sonazoid; DOR: 118.82 vs. 62.78 vs. 227.39). No potential publication bias was observed of the included studies.

CONCLUSION

CEUS is an accurate tool to stratify the risk of malignancy in FLLs. The second-generation CAs, especially Sonazoid may greatly improve diagnostic performance.

KEY POINTS

• CEUS shows excellent diagnostic accuracy in differentiating malignant from benign FLLs. • The second-generation CAs have higher diagnostic accuracy than first-generation CAs. • Sonazoid demonstrates the highest diagnostic accuracy among three major CAs.

Automatic Identification of the Optimal Reference Frame for Segmentation and Quantification of Focal Liver Lesions in Contrast-Enhanced Ultrasound

Post-examination interpretation of contrast-enhanced ultrasound (CEUS) cineloops of focal liver lesions (FLLs) requires offline manual assessment by experienced radiologists, which is time-consuming and generates subjective results. Such assessment usually starts by manually identifying a reference frame, where FLL and healthy parenchyma are well-distinguished. This study proposes an automatic computational method to objectively identify the optimal reference frame for distinguishing and hence delineating an FLL, by statistically analyzing the temporal intensity variation across the spatially discretized ultrasonographic image. Level of confidence and clinical value of the proposed method were quantitatively evaluated on retrospective multi-institutional data (n = 64) and compared with expert interpretations. Results support the proposed method for facilitating easier, quicker and reproducible assessment of FLLs, further increasing the radiologists' confidence in diagnostic decisions. Finally, our method yields a useful training tool for radiologists, widening CEUS use in non-specialist centers, potentially leading to reduced turnaround times and lower patient anxiety and healthcare costs.

Diagnostic value of contrast-enhanced ultrasound in hepatocellular carcinoma: a meta-analysis with evidence from 1998 to 2016

Background

This meta-analysis is aimed at determining the diagnostic value of hepatocellular carcinoma (HCC) with contrast-enhanced ultrasound (CEUS).

Materials and Methods

A comprehensive literature search of Pubmed, Web of Science, and the Cochrane Library was performed to identify published studies. The methodological quality of the included studies was evaluated. Data from eligible studies were used to estimate the pooled sensitivity, specificity, diagnostic odds ratio (DOR), positive and negative likelihood ratio (LR) and summary receiver operating characteristic (SROC) curve. Meta-Disc and STATA softwares were utilized for all statistical analyses.

Results

Fifty-three eligible studies (publication years ranged from 1998 to 2016) were selected according to inclusion criteria. The meta-analysis showed that the pooled sensitivity and specificity of CEUS to detect HCC were 0.85 (95% CI: 0.84–0.86) and 0.91 (95% CI: 0.90–0.92), respectively. The pooled positive and negative LRs were 6.28 (95% CI: 4.49–8.77) and 0.16 (95% CI: 0.12–0.22), respectively. The pooled DOR was 55.01 (95% CI: 35.25–83.47). The area under the SCOR curve was 0.9432. Meta-regression and funnel plot indicated that sample size, type of contrast agents and publication bias might be the major sources of heterogeneity.

Conclusions

CEUS is a valuable diagnostic tool for identifying HCC in clinic with highly sensitive and specific.

Computer-Aided Diagnosis of Focal Liver Lesions Using Contrast-Enhanced Ultrasonography With Perflubutane Microbubbles

This paper proposes an automatic classification method based on machine learning in contrast-enhanced ultrasonography (CEUS) of focal liver lesions using the contrast agent Sonazoid. This method yields spatial and temporal features in the arterial phase, portal phase, and post-vascular phase, as well as max-hold images. The lesions are classified as benign or malignant and again as benign, hepatocellular carcinoma (HCC), or metastatic liver tumor using support vector machines (SVM) with a combination of selected optimal features. Experimental results using 98 subjects indicated that the benign and malignant classification has 94.0% sensitivity, 87.1% specificity, and 91.8% accuracy, and the accuracy of the benign, HCC, and metastatic liver tumor classifications are 84.4%, 87.7%, and 85.7%, respectively. The selected features in the SVM indicate that combining features from the three phases are important for classifying FLLs, especially, for the benign and malignant classifications. The experimental results are consistent with CEUS guidelines for diagnosing FLLs. This research can be considered to be a validation study, that confirms the importance of using features from these phases of the examination in a quantitative manner. In addition, the experimental results indicate that for the benign and malignant classifications, the specificity without the post-vascular phase features is significantly lower than the specificity with the post-vascular phase features. We also conducted an experiment on the operator dependency of setting regions of interest and observed that the intra-operator and inter-operator kappa coefficients were 0.45 and 0.77, respectively.

Role of contrast-enhanced ultrasonography with Sonazoid for hepatocellular carcinoma: evidence from a 10-year experience

Hepatocellular carcinoma (HCC) represents primary liver cancer. Because the development of HCC limits the prognosis as well as the quality of life of the patients, its management should be properly conducted based on an accurate diagnosis. The liver is the major target organ of ultrasound (US), which is the simple, non-invasive, and real-time imaging method available worldwide. Microbubble-based contrast agents are safe and reliable and have become popular, which has resulted in the improvement of diagnostic performances of US due to the increased detectability of the peripheral blood flow. Sonazoid (GE Healthcare, Waukesha, WI, USA), a second-generation contrast agent, shows the unique property of accumulation in the liver and spleen. Contrast-enhanced US with Sonazoid is now one of the most frequently used modalities in the practical management of liver tumors, including the detection and characterization of the nodule, evaluation of the effects of non-surgical treatment, intraoperative support, and post-treatment surveillance. This article reviews the 10-year evidence for contrast-enhanced US with Sonazoid in the practical management of HCC.

Evaluation of contrast Sonazoid-enhanced ultrasonography for the detection of hepatic metastases in breast cancer

Background

The present study was aimed to evaluate the usefulness of contrast Sonazoid-enhanced ultrasonography (US) for the detection of hepatic metastases in breast cancer patients and compare the clinical efficacy and sensitivity of this technique with conventional contrast unenhanced B-mode US in follow-up examinations of breast cancer patients with liver metastasis.

Methods

We assessed a total of 84 hepatic tumors from 24 patients diagnosed with or suspected of having metastatic cancer. These hepatic nodules were diagnosed through imaging, including dynamic magnetic resonance imaging (MRI), contrast-enhanced computed tomography (CECT) scan, B-mode US or contrast Sonazoid-enhanced US (SEUS). Differences in the sensitivity between US and SEUS were compared using MR imaging, CECT, and follow-up imaging.

Results

A total of 79 nodules were diagnosed as metastatic tumors. The remaining nodules were diagnosed as benign tumors (hepatic hemangioma: n = 3; local fatty change: n = 2). SEUS precisely detected the presence or absence of hepatic tumors in the 24 patients examined, showing a sensitivity of 98.8 % (83 of 84 lesions) for total imaged solid liver lesions, with an accuracy of 98.7 % (78 of 79 lesions) for total metastatic breast cancer lesions. In contrast, conventional B-mode US imaging revealed hepatic tumor lesions at a sensitivity of 66.7 % (56 of 84 lesions) and an accuracy of 64.6 % (51 of 79 lesions), respectively. Furthermore, the false positive and false negative rates were, respectively, 6.33 and 29.1 % for B-mode US and 0 and 1.3 % for SEUS. Moreover, twenty-seven metastatic tumors and five benign lesions (3 hemangiomas and 2 focal fatty changes/sparings) were imaged using SEUS but not conventional B-mode US. Significant differences in diagnostic accuracy rates between contrast Sonazoid-enhanced US and conventional B-mode US were observed (Wilcoxon signed rank test: p = 0.0009). No severe adverse events occurred during SEUS after the administration of Sonazoid, except for a grade 1 skin reaction and nausea in one patient.

Conclusion

These results suggested that Sonazoid could be safely administrated to breast cancer patients with liver metastatic disease. Thus, contrast Sonazoid-enhanced US is a feasible and more effective method than B-mode US for the detection of hepatic metastasis, particularly for small metastatic breast cancer lesions less than 14 mm in diameter, showing significant high sensitivity and accuracy.

Contrast enhanced ultrasound in the detection of liver metastases: a prospective multi-centre dose testing study using a perfluorobutane microbubble contrast agent (NC100100)

OBJECTIVE

To conduct a dose testing analysis of perfluorobutane microbubble (NC100100) contrast-enhanced ultrasound (CEUS) to determine the optimal dose for detection of liver metastases in patients with extra-hepatic primary malignancy.

METHODS

157 patients were investigated with conventional US and CEUS. CEUS was performed following intravenous administration of perfluorobutane microbubbles (using one dose of either 0.008, 0.08, 0.12 or 0.36 μL/kg body weight). Three blinded off-site readers recorded the number and locations of metastatic lesions detected by US and CEUS. Contrast enhanced CT and MRI were used as the "Standard Of Reference" (SOR). Sensitivity, specificity and accuracy of liver metastasis detection with US versus CEUS, for each dose group were obtained. Dose group analysis was performed using the Chi-square test.

RESULTS

165 metastases were present in 92 patients who each had 1-7 lesions present on the SOR. Sensitivity of US versus CEUS (for all doses combined) was 38% and 67% (p = 0.0001). The 0.12 dose group with CEUS (78%) had significantly higher sensitivity and accuracy (70%) compared to other dose groups (p < 0.05).

CONCLUSION

The diagnostic performance of CEUS is dose dependent with the 0.12 μL/kg NC100100 dose group showing the greatest sensitivity and accuracy in detection of liver metastases.

Intraoperative ultrasound score to predict recurrent hepatocellular carcinoma after radical treatments

Despite the high complete necrosis rate of radio-frequency ablation (RFA) or the complete removal following curative hepatic resection (HR), recurrent hepatocellular carcinoma (HCC) remains a significant problem. The aim of the study is to identify some intraoperative ultrasound (IOUS) patterns, predicting intrahepatic recurrences. From January 1997 to July 2009, 410 patients with HCC were treated (162 HR and 248 RFA through a surgical access). All patients were submitted to IOUS examination: 148 IOUS were performed during the laparotomic access while 262 IOUS were performed during the laparoscopic access. Primary HCC was classified according to diameter, HCC pattern (nodular or infiltrative), echogenicity (hyper- or hypo-echoic), echotexture (homogeneous or inhomogeneous), capsular invasion, mosaic pattern, nodule in nodule aspect and infiltration of portal vessels. Number of HCC nodules was also considered. Multivariate analysis (Cox model) was performed to determine features associated with recurrent HCC using IOUS patterns that independently predicted recurrent HCC, a IOUS score was developed. The patients were followed for 3-127 months, (median follow-up: 21.5 months). In 220 patients (54%), intrahepatic recurrences occurred. In 155 patients (38%), distant intrahepatic recurrences arose in different segments at the primary tumor site. In 65 HCC cases (16%), local recurrences were found. At multivariate analysis, multiple nodules, HCC diameter (>20 mm), HCC pattern (infiltrative), hyperechoic nodule and portal infiltration were statistically significant for risk factor of intrahepatic recurrences. Therefore, a IOUS scoring system was calculated on the basis of multivariate analysis and identified three risk categories of patients: in group 1 recurrences occurred in 37%, group 2 in 46% and group 3 in 66% (p = 0.0001). IOUS is an accurate staging tool during "surgical" procedures. This study showed an added value of IOUS: it permitted to identify ultrasound patterns, which can predict the risk of HCC recurrences. The calculated IOUS score permits to intraoperatively evaluate the actual surgical choice and to program the best treatment strategies during the follow-up period.

Intraoperative ultrasound patterns predict recurrences after surgical treatments for hepatocellular carcinoma()

INTRODUCTION

Hepatocellular carcinoma (HCC) is associated with a high incidence of postoperative recurrence, despite high rates of complete necrosis with radiofrequency ablation (RFA) and curative hepatic resections (HR). The aim of this study was to identify intraoperative ultrasound patterns observed during HR or RFA that predicting intrahepatic HCC recurrence.

MATERIALS AND METHODS

From January 1997 through August 2008, we treated 377 patients with HCC (158 with HR and 219 with surgical RFA). All patients underwent intraoperative ultrasound (IOUS) examination. Primary HCCs was classified according to diameter, HCC pattern (nodular or infiltrative), echogenicity (hyper- or hypo-), echotexture (homogeneous or inhomogeneous), capsular invasion, mosaic pattern, nodule-in-nodule appearance, and infiltration of portal vessels. Number of HCC nodules was also considered. Comparisons between the groups of possible factors for intrahepatic recurrence of treated tumors were performed using the Kaplan-Meier method and compared using the log-rank test.

RESULTS

Patients were followed for 9-127 months (median: 18.6 months), and intrahepatic recurrence was observed in 198 (52.5%). In 138 patients (36.5%), recurrences were located in different segments with respect to the primary tumor. In 60 HCC tumors (16%), local recurrences were found in the same segment as the primary tumor. At univariate analysis, primary HCC echogenicity and mosaic pattern were the only factors not significant associated with intrahepatic recurrences.

CONCLUSION

IOUS is an accurate staging tool for use during "surgical" resection or RFA. This study shows that IOUS patterns can also be used to estimate the risk of post-treatment HCC recurrence. In patients at high risk for this outcome, closer follow-up and use of adjuvant therapies could be useful.

Computer-aided diagnosis for contrast-enhanced ultrasound in the liver

Computer-aided diagnosis (CAD) has become one of the major research subjects in medical imaging and diagnostic radiology. The basic concept of CAD is to provide computer output as a second opinion to assist radiologists’ image interpretations by improving the accuracy and consistency of radiologic diagnosis and also by reducing the image-reading time. To date, research on CAD in ultrasound (US)-based diagnosis has been carried out mostly for breast lesions and has been limited in the fields of gastroenterology and hepatology, with most studies being conducted using B-mode US images. Two CAD schemes with contrast-enhanced US (CEUS) that are used in classifying focal liver lesions (FLLs) as liver metastasis, hemangioma, or three histologically differentiated types of hepatocellular carcinoma (HCC) are introduced in this article: one is based on physicians’ subjective pattern classifications (subjective analysis) and the other is a computerized scheme for classification of FLLs (quantitative analysis). Classification accuracies for FLLs for each CAD scheme were 84.8% and 88.5% for metastasis, 93.3% and 93.8% for hemangioma, and 98.6% and 86.9% for all HCCs, respectively. In addition, the classification accuracies for histologic differentiation of HCCs were 65.2% and 79.2% for well-differentiated HCCs, 41.7% and 50.0% for moderately differentiated HCCs, and 80.0% and 77.8% for poorly differentiated HCCs, respectively. There are a number of issues concerning the clinical application of CAD for CEUS, however, it is likely that CAD for CEUS of the liver will make great progress in the future.