A new proposal for secondary surveillance following potentially curative therapy of HCC: alternating MRI and CEUS.
Abdominal radiology (New York)
PURPOSE:A high recurrence rate following ablative therapy of hepatocellular carcinoma (HCC) necessitates routine follow-up imaging (secondary surveillance) to facilitate early re-treatment. We evaluate our unique secondary surveillance algorithm (with use of alternating MRI and CEUS) by assessment of the relative diagnostic accuracy of MRI and CEUS in detection of residual/recurrent tumor. Potential benefits of alternating surveillance are compared to the use of MRI alone. MATERIALS AND METHODS:This prospective observational IRB approved study included 231 patients with 354 treated tumors between January 2017 and June 2020. Treated lesions underwent secondary surveillance for a minimum of 7 months and up to 3 years, median follow-up 14 months. Secondary surveillance involved MRI performed at 1 month after treatment, followed by CEUS and MRI at alternate 3-month intervals (i.e., CEUS at month 4, MRI at month 7, etc.), for a total of 2 years. An equivocal finding on one imaging modality triggered expeditious evaluation with the alternate modality. Arterial phase hyperenhancement and washout comprise the classic features of recurrent tumor on both modalities. RESULTS:A total of 746 MRI and 712 CEUS examinations were performed, and a total of 184 tumor recurrences detected, MRI (n = 82) and CEUS (n = 102) (p = 0.19). There was no difference in the sensitivity (71.0-85.0% and 80.9-92.0%), specificity (97.4-99.2% and 98.5-99.9%), and area under the ROC curve (0.85-0.92 and 0.91-0.96) between MRI and CEUS, respectively. 23 of 82 recurrent tumors identified on MRI were equivocal and confirmed with expedited CEUS. 9 equivocal cases on MRI were disproved by expedited CEUS. On CEUS, 1 of the 102 recurrent tumors was equivocal and confirmed on MRI, and 2 equivocal CEUS cases were disproved by MRI. CONCLUSION:MRI and CEUS performed similarly in our secondary surveillance algorithm for HCC in their ability to detect tumor recurrence, and showed no significant difference in their relative diagnostic test accuracy measures. Of greater interest, equivocal results on MRI (typically due to difficulty in distinguishing tumor recurrence from post-treatment change/shunting) were either confirmed or disproven by CEUS in all cases. Secondary surveillance of treated HCC with alternating MRI and CEUS shows equivalent performance of each modality. CEUS resolves equivocal MRI and optimally demonstrates APHE and washout in tumor recurrence.
10.1007/s00261-021-03331-1
Contrast-enhanced US Evaluation of Hepatocellular Carcinoma Response to Chemoembolization: A Prospective Multicenter Trial.
Radiology
Background Contrast-enhanced (CE) US has been studied for use in the detection of residual viable hepatocellular carcinoma (HCC) after locoregional therapy, but multicenter data are lacking. Purpose To compare two-dimensional (2D) and three-dimensional (3D) CE US diagnostic performance with that of CE MRI or CT, the current clinical standard, in the detection of residual viable HCC after transarterial chemoembolization (TACE) in a prospective multicenter trial. Materials and Methods Participants aged at least 21 years with US-visible HCC scheduled for TACE were consecutively enrolled at one of three participating academic medical centers from May 2016 to March 2022. Each underwent baseline 2D and 3D CE US before TACE, 2D and 3D CE US 1-2 weeks and/or 4-6 weeks after TACE, and CE MRI or CT 4-6 weeks after TACE. CE US and CE MRI or CT were evaluated by three fellowship-trained radiologists for the presence or absence of viable tumors and were compared with reference standards of pathology (18%), angiography on re-treatment after identification of residual disease at 1-2-month follow-up imaging (31%), 4-8-month CE MRI or CT (42%), or short-term (approximately 1-2 months) CE MRI or CT if clinically decompensated and estimated viability was greater than 50% at imaging (9%). Diagnostic performance criteria, including sensitivity and specificity, were obtained for each modality and time point with generalized estimating equation analysis. Results A total of 132 participants were included (mean age, 64 years ± 7 [SD], 87 male). Sensitivity of 2D CE US 4-6 weeks after TACE was 91% (95% CI: 84, 95), which was higher than that of CE MRI or CT (68%; 95% CI: 58, 76; < .001). Sensitivity of 3D CE US 4-6 weeks after TACE was 89% (95% CI: 81, 94), which was higher than that of CE MRI or CT ( < .001), with no evidence of a difference from 2D CE US ( = .22). CE MRI or CT had 85% (95% CI: 76, 91) specificity, higher than that of 4-6-week 2D and 3D CE US (70% [95% CI: 56, 80] and 67% [95% CI: 53, 78], respectively; = .046 and = .023, respectively). No evidence of differences in any diagnostic criteria were observed between 1-2-week and 4-6-week 2D CE US ( > .21). Conclusion The 2D and 3D CE US examinations 4-6 weeks after TACE revealed higher sensitivity in the detection of residual HCC than CE MRI or CT, albeit with lower specificity. Importantly, CE US performance was independent of follow-up time. Clinical trial registration no. NCT02764801 © RSNA, 2023
10.1148/radiol.230727
Contrast-enhanced ultrasound pattern of hepatocellular carcinoma in noncirrhotic liver - results from the prospective multicentre DEGUM CEUS HCC study.
European journal of gastroenterology & hepatology
OBJECTIVES:Contrast-enhanced ultrasound (CEUS) has a high diagnostic accuracy for the noninvasive diagnosis of hepatocellular carcinoma (HCC) in cirrhosis. However, as HCC in noncirrhosis becomes an emerging clinical concern, our study aimed to assess the diagnostic value of CEUS and the CEUS algorithms CEUS LI-RADS and ESCULAP in noncirrhotic liver in a prospective multicentre real-life setting. METHODS:High-risk patients for HCC with focal liver lesions upon B-mode ultrasound were recruited prospectively in a multicentre real-life approach to undergo standardized CEUS. Diagnostic accuracies of CEUS and the CEUS algorithms were assessed for the sub-collective of noncirrhotic patients. Histology, MRI and CT served as the reference standard. RESULTS:In total 47/517 patients were noncirrhotic. The reference standard of the lesions showed 30 HCCs (63.8%), four intrahepatic cholangiocellular carcinomas (iCCAs), two other malignancies and 11 benign lesions. HCCs in noncirrhosis showed a tendency towards larger tumor size and better differentiation. A typical CEUS pattern of arterial phase hyperenhancement and late-onset (>60 s), mild washout occurred in 22/30 HCCs (73.3%). Very late onset of washout > 4-6 min was not seen in noncirrhotic liver. The CEUS algorithm ESCULAP showed a perfect sensitivity (100 vs. 68% with CEUS LI-RADS), whereas CEUS LI-RADS had a superior specificity (83 vs. 53%). The positive predictive value was high with both algorithms. CONCLUSION:The CEUS patterns of HCCs in noncirrhotic liver resembled those in cirrhosis. Our findings suggest that although designed for the application in cirrhosis only, the diagnostic accuracies of the CEUS algorithms in noncirrhotic liver seem comparable to the findings in cirrhosis.
10.1097/MEG.0000000000002491
Contrast-Enhanced Ultrasound for Differentiation Between Poorly Differentiated Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma.
Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine
PURPOSE:To evaluate the diagnostic performance of LR-5 for diagnosing poorly differentiated hepatocellular carcinoma (p-HCC). To build a contrast-enhanced ultrasound (CEUS) signature for improving the differential diagnostic performance between p-HCC and intrahepatic cholangiocarcinoma (ICC). METHODS:The B-mode ultrasound (BUS) and CEUS features of 60 p-HCCs and 56 ICCs were retrospectively analyzed. The CEUS LI-RADS category was assigned according to CEUS LI-RADS v2017. A diagnostic CEUS signature was built based on the independent significant features. An ultrasound (US) signature combining both BUS and CEUS features was also built. The diagnostic performances of the CEUS signature, US signature, and LR-5 were evaluated by receiver operating characteristic (ROC) analysis. RESULTS:One (1.7%) p-HCC and 26 (46.4%) ICC patients presented cholangiectasis or cholangiolithiasis (P < .001). Fifty-four (90.0%) p-HCCs and 8 (14.3%) ICCs showed clear boundaries in the artery phase (P < .001). The washout times of p-HCCs and ICCs were 81.0 ± 42.5 s and 34.7 ± 8.6 s, respectively (P < .001). The AUC, sensitivity, and specificity of the CEUS signature, US signature, and LR-5 were 0.955, 91.67%, and 90.57% versus 0.976, 96.67%, and 92.45% versus 0.758, 51.67%, and 100%, respectively. The AUC and sensitivity of CEUS LI-RADS were much lower than those of the CEUS and US signatures (P < .001). CONCLUSION:LR-5 had high specificity but low sensitivity in diagnosing p-HCC. When the washout time and tumor boundary were included in the CEUS signature, the sensitivity and AUC were remarkably increased in the differentiation between p-HCC and ICC.
10.1002/jum.15812
The diagnostic value of contrast-enhanced ultrasound LI-RADS for hepatocellular carcinoma in patients with cirrhosis and chronic hepatitis B.
Yang Dan,Hu Hong,Li Rui,Tang Chun-Lin,Ma Kuan-Sheng,Guo De-Yu
Abdominal radiology (New York)
PURPOSE:To explore the diagnostic value of American College of Radiology Contrast-Enhanced Ultrasound-Liver Imaging Reporting and Data System (ACR-CEUS-LI-RADS) for hepatocellular carcinoma (HCC) in patients with cirrhosis and chronic hepatitis B. METHODS:A total of 205 patients at high risk of HCC with solitary hepatic nodule were enrolled and retrospectively analyzed. All patients were over 18 years old and had a single lesion with a diameter < 50 mm. Lesions were categorized according to size and contrast enhancement patterns in the arterial, portal venous and late phases. Diagnostic efficacy of CEUS LI-RADS for HCC, and the rate of non-HCC malignancies in the LR-M class were compared between patients with cirrhosis and chronic hepatitis B. RESULTS:Of all 205 nodules (median nodule size was 34 mm), 142 (69.3%) were HCC. Of the 127 (61.9%) LR-5 category nodules, 95.8% (92/96) nodules were corresponded to HCC in cirrhosis, while 61.3% (19/31) nodules were corresponded to HCC in chronic hepatitis B (P = 0.000). Positive predictive value (PPV) of LR-5 category for HCC was 95.8% in cirrhosis and 61.3% in chronic hepatitis B (P = 0.000). More category of LR-4 nodules were proved to be HCC in patients with cirrhosis than chronic hepatitis B (80.0% vs 8.3%, P = 0.000). Of 41 LR-M category nodules, more non-HCC malignancies were found in chronic hepatitis B (76.0%) than that in cirrhosis (25.0%, P = 0.001). CONCLUSIONS:The LR-5 category is highly specific for the diagnosis of HCC in patients with cirrhosis. However, LR-5 category nodules require further CT or MRI examination or histological confirmation in patients with chronic hepatitis B for its unsatisfactory PPV for HCC.
10.1007/s00261-021-03345-9
Diagnostic accuracy of contrast-enhanced ultrasound for the differential diagnosis of hepatocellular carcinoma: ESCULAP versus CEUS-LI-RADS.
Schellhaas Barbara,Görtz Ruediger S,Pfeifer Lukas,Kielisch Christian,Neurath Markus F,Strobel Deike
European journal of gastroenterology & hepatology
OBJECTIVE:A comparison is made of two contrast-enhanced ultrasound (CEUS) algorithms for the diagnosis of hepatocellular carcinoma (HCC) in high-risk patients: Erlanger Synopsis of Contrast-enhanced Ultrasound for Liver lesion Assessment in Patients at Risk (ESCULAP) and American College of Radiology Contrast-Enhanced Ultrasound-Liver Imaging Reporting and Data System (ACR-CEUS-LI-RADSv.2016). PATIENTS AND METHODS:Focal liver lesions in 100 high-risk patients were assessed using both CEUS algorithms (ESCULAP and CEUS-LI-RADSv.2016) for a direct comparison. Lesions were categorized according to size and contrast enhancement in the arterial, portal venous and late phases.For the definite diagnosis of HCC, categories ESCULAP-4, ESCULAP-Tr and ESCULAP-V and CEUS-LI-RADS-LR-5, LR-Tr and LR-5-V were compared. In addition, CEUS-LI-RADS-category LR-M (definitely/probably malignant, but not specific for HCC) and ESCULAP-category C [intrahepatic cholangiocellular carcinoma (ICC)] were compared.Histology, CE-computed tomography and CE-MRI served as reference standards. RESULTS:The reference standard among 100 lesions included 87 HCCs, six ICCs and seven non-HCC-non-ICC-lesions. For the diagnosis of HCC, the diagnostic accuracy of CEUS was significantly higher with ESCULAP versus CEUS-LI-RADS (94.3%/72.4%; p<0.01). Sensitivity, specificity and positive predictive value (PPV) and negative predictive value for ESCULAP/CEUS-LI-RADS were 94.3%/72.4%; 61.5%/69.2%; 94.3%/94%; and 61.5%/27.3%, respectively.The diagnostic accuracy for ICC (LR-M/ESCULAP-C) was identical with both algorithms (50%), with higher PPV for ESCULAP-C versus LR-M (75 vs. 50%). CONCLUSION:CEUS-based algorithms contribute toward standardized assessment and reporting of HCC-suspect lesions in high-risk patients. ESCULAP shows significantly higher diagnostic accuracy, sensitivity and negative predictive value with no loss of specificity compared with CEUS-LI-RADS. Both algorithms have an excellent PPV. Arterial hyperenhancement is the key feature for the diagnosis of HCC with CEUS. Washout should not be a necessary prerequisite for the diagnosis of definite HCC. CEUS-LI-RADS in its current version is inferior to ESCULAP for the noninvasive diagnosis of HCC. There are two ways to improve CEUS-LI-RADS: firstly, combination of the categories LR-4 and LR-5 for the diagnosis of definite HCC, and secondly, use of subtotal infiltration of a liver lobe as an additional feature.
10.1097/MEG.0000000000000916
Radiomics: Images Are More than Pictures, They Are Data.
Radiology
In the past decade, the field of medical image analysis has grown exponentially, with an increased number of pattern recognition tools and an increase in data set sizes. These advances have facilitated the development of processes for high-throughput extraction of quantitative features that result in the conversion of images into mineable data and the subsequent analysis of these data for decision support; this practice is termed radiomics. This is in contrast to the traditional practice of treating medical images as pictures intended solely for visual interpretation. Radiomic data contain first-, second-, and higher-order statistics. These data are combined with other patient data and are mined with sophisticated bioinformatics tools to develop models that may potentially improve diagnostic, prognostic, and predictive accuracy. Because radiomics analyses are intended to be conducted with standard of care images, it is conceivable that conversion of digital images to mineable data will eventually become routine practice. This report describes the process of radiomics, its challenges, and its potential power to facilitate better clinical decision making, particularly in the care of patients with cancer.
10.1148/radiol.2015151169
Radiomics-based classification of hepatocellular carcinoma and hepatic haemangioma on precontrast magnetic resonance images.
Wu Jingjun,Liu Ailian,Cui Jingjing,Chen Anliang,Song Qingwei,Xie Lizhi
BMC medical imaging
BACKGROUND:To evaluate the feasibility of using radiomics with precontrast magnetic resonance imaging for classifying hepatocellular carcinoma (HCC) and hepatic haemangioma (HH). METHODS:This study enrolled 369 consecutive patients with 446 lesions (a total of 222 HCCs and 224 HHs). A training set was constituted by randomly selecting 80% of the samples and the remaining samples were used to test. On magnetic resonance (MR) images of HCC and HH obtained with in-phase, out-phase, T2-weighted imaging (T2WI), and diffusion-weighted imaging (DWI) sequences, we outlined the target lesions and extracted 1029 radiomics features, which were classified as first-, second-, higher-order statistics and shape features. Then, the variance threshold, select k best, and least absolute shrinkage and selection operator algorithms were explored for dimensionality reduction of the features. We used four classifiers (decision tree, random forest, K nearest neighbours, and logistic regression) to identify HCC and HH on the basis of radiomics features. Two abdominal radiologists also performed the conventional qualitative analysis for classification of HCC and HH. Diagnostic performances of radiomics and radiologists were evaluated by receiver operating characteristic (ROC) analysis. RESULTS:Valuable radiomics features for building a radiomics signature were extracted from in-phase (n = 22), out-phase (n = 24), T2WI (n = 34) and DWI (n = 24) sequences. In comparison, the logistic regression classifier showed better predictive ability by combining four sequences. In the training set, the area under the ROC curve (AUC) was 0.86 (sensitivity: 0.76; specificity: 0.78), and in the testing set, the AUC was 0.89 (sensitivity: 0.822; specificity: 0.714). The diagnostic performance for the optimal radiomics-based combined model was significantly higher than that for the less experienced radiologist (2-years experience) (AUC = 0.702, p < 0.05), and had no statistic difference with the experienced radiologist (10-years experience) (AUC = 0.908, p>0.05). CONCLUSIONS:We developed and validated a radiomics signature as an adjunct tool to distinguish HCC and HH by combining in-phase, out-phase, T2W, and DW MR images, which outperformed the less experienced radiologist (2-years experience), and was nearly equal to the experienced radiologist (10-years experience).
10.1186/s12880-019-0321-9
Radiomics machine-learning signature for diagnosis of hepatocellular carcinoma in cirrhotic patients with indeterminate liver nodules.
Mokrane Fatima-Zohra,Lu Lin,Vavasseur Adrien,Otal Philippe,Peron Jean-Marie,Luk Lyndon,Yang Hao,Ammari Samy,Saenger Yvonne,Rousseau Herve,Zhao Binsheng,Schwartz Lawrence H,Dercle Laurent
European radiology
PURPOSE:To enhance clinician's decision-making by diagnosing hepatocellular carcinoma (HCC) in cirrhotic patients with indeterminate liver nodules using quantitative imaging features extracted from triphasic CT scans. MATERIAL AND METHODS:We retrospectively analyzed 178 cirrhotic patients from 27 institutions, with biopsy-proven liver nodules classified as indeterminate using the European Association for the Study of the Liver (EASL) guidelines. Patients were randomly assigned to a discovery cohort (142 patients (pts.)) and a validation cohort (36 pts.). Each liver nodule was segmented on each phase of triphasic CT scans, and 13,920 quantitative imaging features (12 sets of 1160 features each reflecting the phenotype at one single phase or its change between two phases) were extracted. Using machine-learning techniques, the signature was trained and calibrated (discovery cohort), and validated (validation cohort) to classify liver nodules as HCC vs. non-HCC. Effects of segmentation and contrast enhancement quality were also evaluated. RESULTS:Patients were predominantly male (88%) and CHILD A (65%). Biopsy was positive for HCC in 77% of patients. LI-RADS scores were not different between HCC and non-HCC patients. The signature included a single radiomics feature quantifying changes between arterial and portal venous phases: V-ADWT1_LL_Variance-2D and reached area under the receiver operating characteristic curve (AUC) of 0.70 (95%CI 0.61-0.80) and 0.66 (95%CI 0.64-0.84) in discovery and validation cohorts, respectively. The signature was influenced neither by segmentation nor by contrast enhancement. CONCLUSION:A signature using a single feature was validated in a multicenter retrospective cohort to diagnose HCC in cirrhotic patients with indeterminate liver nodules. Artificial intelligence could enhance clinicians' decision by identifying a subgroup of patients with high HCC risk. KEY POINTS:• In cirrhotic patients with visually indeterminate liver nodules, expert visual assessment using current guidelines cannot accurately differentiate HCC from differential diagnoses. Current clinical protocols do not entail biopsy due to procedural risks. Radiomics can be used to non-invasively diagnose HCC in cirrhotic patients with indeterminate liver nodules, which could be leveraged to optimize patient management. • Radiomics features contributing the most to a better characterization of visually indeterminate liver nodules include changes in nodule phenotype between arterial and portal venous phases: the "washout" pattern appraised visually using EASL and EASL guidelines. • A clinical decision algorithm using radiomics could be applied to reduce the rate of cirrhotic patients requiring liver biopsy (EASL guidelines) or wait-and-see strategy (AASLD guidelines) and therefore improve their management and outcome.
10.1007/s00330-019-06347-w
Deep Neural Architectures for Contrast Enhanced Ultrasound (CEUS) Focal Liver Lesions Automated Diagnosis.
Căleanu Cătălin Daniel,Sîrbu Cristina Laura,Simion Georgiana
Sensors (Basel, Switzerland)
Computer vision, biomedical image processing and deep learning are related fields with a tremendous impact on the interpretation of medical images today. Among biomedical image sensing modalities, ultrasound (US) is one of the most widely used in practice, since it is noninvasive, accessible, and cheap. Its main drawback, compared to other imaging modalities, like computed tomography (CT) or magnetic resonance imaging (MRI), consists of the increased dependence on the human operator. One important step toward reducing this dependence is the implementation of a computer-aided diagnosis (CAD) system for US imaging. The aim of the paper is to examine the application of contrast enhanced ultrasound imaging (CEUS) to the problem of automated focal liver lesion (FLL) diagnosis using deep neural networks (DNN). Custom DNN designs are compared with state-of-the-art architectures, either pre-trained or trained from scratch. Our work improves on and broadens previous work in the field in several aspects, e.g., a novel leave-one-patient-out evaluation procedure, which further enabled us to formulate a hard-voting classification scheme. We show the effectiveness of our models, i.e., 88% accuracy reported against a higher number of liver lesion types: hepatocellular carcinomas (HCC), hypervascular metastases (HYPERM), hypovascular metastases (HYPOM), hemangiomas (HEM), and focal nodular hyperplasia (FNH).
10.3390/s21124126
Clinical management of hepatocellular carcinoma. Conclusions of the Barcelona-2000 EASL conference. European Association for the Study of the Liver.
Bruix J,Sherman M,Llovet J M,Beaugrand M,Lencioni R,Burroughs A K,Christensen E,Pagliaro L,Colombo M,Rodés J,
Journal of hepatology
Contrast-Enhanced Ultrasound Liver Imaging Reporting and Data System in Hepatocellular Carcinoma ≤5 cm: Biological Characteristics and Patient Outcomes.
Liver cancer
Introduction:The present study aimed to evaluate the influence of biological characteristics of hepatocellular carcinoma (HCC) on the Liver Imaging Reporting and Data System (LI-RADS) v2017 category of contrast-enhanced ultrasound (CEUS) in patients with high risk and compare the outcomes among different categories after radical resection. Methods:Between June 2017 and December 2020, standardized CEUS data of liver nodules were prospectively collected from multiple centers across China. We conducted a retrospective analysis of the prospectively collected data on HCCs measuring no more than 5 cm, as diagnosed by pathology. LI-RADS categories were assigned after thorough evaluation of CEUS features. Then, CEUS LI-RADS categories and major features were compared in different differentiation, Ki-67, and microvascular invasion (MVI) statuses. Differences in recurrence-free survival (RFS) among different LI-RADS categories were further analyzed. Results:A total of 293 HCC nodules in 293 patients were included. This study revealed significant differences in the CEUS LI-RADS category of HCCs among differentiation ( < 0.001) and levels of Ki-67 ( = 0.01) and that poor differentiation (32.7% in LR-M, 12% in LR-5, and 6.2% in LR-4) ( < 0.001) and high level of Ki-67 (median value 30%) were more frequently classified into the LR-M category, whereas well differentiation (37.5% in LR-4, 15.1% in LR-5, and 11.5% in LR-M) and low levels of Ki-67 (median value 11%) were more frequently classified into the LR-4 category. No significant differences were found between MVI and CEUS LI-RADS categories ( > 0.05). With a median follow-up of 23 months, HCCs assigned to different CEUS LI-RADS classes showed no significant differences in RFS after resection. Conclusions:Biological characteristics of HCC, including differentiation and level of Ki-67 expression, could influence major features of CEUS and impact the CEUS LI-RADS category. HCCs in different CEUS LI-RADS categories showed no significant differences in RFS after resection.
10.1159/000527498
Contrast-enhanced ultrasound Liver Imaging Reporting and Data System category M: a systematic review and meta-analysis.
Shin Jaeseung,Lee Sunyoung,Kim Yeun-Yoon,Chung Yong Eun,Choi Jin-Young,Park Mi-Suk
Ultrasonography (Seoul, Korea)
PURPOSE:A meta-analysis was conducted to determine the proportion of contrast-enhanced ultrasound (CEUS) Liver Imaging Reporting and Data System category M (LR-M) in hepatocellular carcinomas (HCCs) and non-HCC malignancies and to investigate the frequency of individual CEUS LR-M imaging features. METHODS:The MEDLINE and Embase databases were searched from January 1, 2016 to July 23, 2020 for studies reporting the proportion of CEUS LR-M in HCC and non-HCC malignancies. The meta-analytic pooled proportions of HCC and non-HCC malignancies in the CEUS LR-M category were calculated. The meta-analytic frequencies of CEUS LR-M imaging features in nonHCC malignancies were also determined. Risk of bias and applicability were evaluated using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. RESULTS:Twelve studies reporting the diagnostic performance of the CEUS LR-M category were identified, as well as seven studies reporting the frequencies of individual CEUS LR-M imaging features. The pooled proportions of HCC and non-HCC malignancies in the CEUS LR-M category were 54% (95% confidence interval [CI], 44% to 65%) and 40% (95% CI, 28% to 53%), respectively. The pooled frequencies of individual CEUS LR-M imaging features in non-HCC malignancies were 30% (95% CI, 17% to 45%) for rim arterial phase hyperenhancement, 79% (95% CI, 66% to 90%) for early (<60 s) washout, and 42% (95% CI, 21% to 64%) for marked washout. CONCLUSION:In total, 94% of CEUS LR-M lesions were malignancies, with HCCs representing 54% and non-HCC malignancies representing 40%. The frequencies of individual CEUS LR-M imaging features varied; early washout showed the highest frequency for non-HCC malignancies.
10.14366/usg.21011