Management implications and outcomes of LI-RADS-2, -3, -4, and -M category observations

Clinical impact of MRI on indeterminate findings on contrast-enhanced CT suspicious of HCC

OBJECTIVES

In patients evaluated for hepatocellular carcinoma (HCC), magnetic resonance imaging (MRI) is often used secondarily when multiphase contrast-enhanced computed tomography (ceCT) is inconclusive. We investigated the clinical impact of adding MRI.

MATERIALS AND METHODS

This single-institution retrospective study included 48 MRI scans (44 patients) conducted from May 2016 to July 2023 due to suspicion of HCC on a multiphase ceCT scan. Data included medical history, preceding and subsequent imaging, histology when available, and decisions made at multidisciplinary team meetings.

RESULTS

In case of possible HCC recurrence, 63% of the MRI scans were diagnostic of HCC. For 80% of the negative MRI scans, the patients were diagnosed with HCC within a median of 165 days in the suspicious area of the liver. In case of possible de-novo HCC in patients with cirrhosis, 22% of the scans were diagnostic of HCC and 33% of the negative MRI scans were of patients diagnosed with HCC within a median of 109 days. None of the non-cirrhotic patients with possible de-novo HCC and negative MRI scans (64%) were later diagnosed with HCC, but 3/5 of the indeterminate scans were of patients diagnosed with HCC in a biopsy.

CONCLUSIONS

Secondary MRI to a multiphase ceCT scan suspicious of HCC is highly valuable in ruling out HCC in non-cirrhotic patients and in diagnosing HCC non-invasively in cirrhotic patients and patients with prior HCC. Patients with cirrhosis or prior HCC are still at high risk of having HCC if MRI results are inconclusive or negative.

LI-RADS v2018 category and imaging features: inter-modality agreement between contrast-enhanced CT, gadoxetate disodium-enhanced MRI, and extracellular contrast-enhanced MRI

PURPOSE

To perform an intra-individual comparison of LI-RADS category and imaging features in patients at high risk of hepatocellular carcinoma (HCC) on contrast-enhanced CT, gadoxetate disodium-enhanced MRI (EOB-MRI), and extracellular agent-enhanced MRI (ECA-MRI) and to analyze the diagnostic performance of each imaging modality.

METHOD

This retrospective study included cirrhotic patients with at least one LR-3, LR-4, LR-5, LR-M or LR-TIV observation imaged with at least two imaging modalities among CT, EOB-MRI, or ECA-MRI. Two radiologists evaluated the observations using the LI-RADS v2018 diagnostic algorithm. Reference standard included pathologic confirmation and imaging criteria according to LI-RADS v2018. Imaging features were compared between different exams using the McNemar test. Inter-modality agreement was calculated by using the weighted Cohen's kappa (k) test.

RESULTS

A total of 144 observations (mean size 34.0 ± 32.4 mm) in 96 patients were included. There were no significant differences in the detection of major and ancillary imaging features between the three imaging modalities. When considering all the observations, inter-modality agreement for category assignment was substantial between CT and EOB-MRI (k 0.60; 95%CI 0.44, 0.75), moderate between CT and ECA-MRI (k 0.46; 95%CI 0.22, 0.69) and substantial between EOB-MRI and ECA-MRI (k 0.72; 95%CI 0.59, 0.85). In observations smaller than 20 mm, inter-modality agreement was fair between CT and EOB-MRI (k 0.26; 95%CI 0.05, 0.47), moderate between CT and ECA-MRI (k 0.42; 95%CI -0.02, 0.88), and substantial between EOB-MRI and ECA-MRI (k 0.65; 95%CI 0.47, 0.82). ECA-MRI demonstrated the highest sensitivity (70%) and specificity (100%) when considering LR-5 as predictor of HCC.

CONCLUSIONS

Inter-modality agreement between CT, ECA-MRI, and EOB-MRI decreases in observations smaller than 20 mm. ECA-MRI has the provided higher sensitivity for the diagnosis of HCC.

Clinical impact of CEUS on non-characterizable observations and observations with intermediate probability of malignancy on CT/MRI in patients at risk for HCC

BACKGROUND

Hepatocellular carcinoma (HCC) is a unique cancer allowing tumor diagnosis with identification of definitive patterns of enhancement on contrast-enhanced imaging, avoiding invasive biopsy. However, it is still unclear to what extent Contrast-Enhanced Ultrasound (CEUS) is a clinically useful additional step when Computed tomography (CT) or Magnetic resonance imaging (MRI) are inconclusive.

METHODS

A prospective international multicenter validation study for CEUS Liver Imaging Reporting and Data System (LI-RADS) was conducted between January 2018 and August 2021. 646 patients at risk for HCC with focal liver lesions were enrolled. CEUS was performed using an intravenous ultrasound contrast agent within 4 weeks of CT/MRI. Liver nodules were categorized based on LI-RADS (LR) criteria. Histology or one-year follow-up CT/MRI imaging results were used as the reference standard. The diagnostic performance of CEUS was evaluated for inconclusive CT/MRI scan in two scenarios for which the AASLD recommends repeat imaging or imaging follow-up: observations deemed non-characterizable (LR-NC) or with indeterminate probability of malignancy (LR-3).

RESULTS

75 observations on CT or MRI were categorized as LR-3 (n = 54) or LR-NC (n = 21) CEUS recategorization of such observations into a different LR category (namely, into one among LR-1, LR-2, LR-5, LR-M, or LR-TIV) resulted in management recommendation changes in 33.3% (25/75) and in all but one (96.0%, 24/25) observation, the new management recommendations were correct.

CONCLUSION

CEUS LI-RADS resulted in management recommendations change in substantial number of liver observations with initial indeterminate CT/MRI characterization, identifying both non-malignant lesions and HCC, potentially accelerating the diagnostic process and alleviating the need for biopsy or follow-up imaging.

CLINICALTRIALS

gov number, NCT03318380.

Long-term evolution of LR-2, LR-3 and LR-4 observations in HBV-related cirrhosis based on LI-RADS v2018 using gadoxetic acid-enhanced MRI

PURPOSE

To investigate the long-term evolution of LR-2, LR-3 and LR-4 observations in patients with hepatitis B virus (HBV)-related cirrhosis based on LI-RADS v2018 and identify predictors of progression to a malignant category on serial gadoxetic acid-enhanced magnetic resonance imaging (Gd-EOB-MRI).

METHODS

This retrospective study included 179 cirrhosis patients with untreated indeterminate observations who underwent Gd-EOB-MRI exams at baseline and during the follow-up period between June 2016 and December 2021. Two radiologists independently assessed the major features, ancillary features, and LI-RADS category of each observation at baseline and follow-up. In cases of disagreement, a third radiologist was consulted for consensus. Cumulative incidences for progression to a malignant category (LR-5 or LR-M) and to LR-4 or higher were analyzed for each index category using Kaplan‒Meier methods and compared using log-rank tests. The risk factors for malignant progression were evaluated using a Cox proportional hazard model.

RESULTS

A total of 213 observations, including 74 (34.7%) LR-2, 95 (44.6%) LR-3, and 44 (20.7%) LR-4, were evaluated. The overall cumulative incidence of progression to a malignant category was significantly higher for LR-4 observations than for LR-3 or LR-2 observations (each P < 0.001), and significantly higher for LR-3 observations than for LR-2 observations (P < 0.001); at 3-, 6-, and 12-month follow-ups, the cumulative incidence of progression to a malignant category was 11.4%, 29.5%, and 39.3% for LR-4 observations, 0.0%, 8.5%, and 19.6% for LR-3 observations, and 0.0%, 0.0%, and 0.0% for LR-2 observations, respectively. The cumulative incidence of progression to LR-4 or higher was higher for LR-3 observations than for LR-2 observations (P < 0.001); at 3-, 6-, and 12-month follow-ups, the cumulative incidence of progression to LR-4 or higher was 0.0%, 8.5%, and 24.6% for LR-3 observations, and 0.0%, 0.0%, and 0.0% for LR-2 observations, respectively. In multivariable analysis, nonrim arterial phase hyperenhancement (APHE) [hazard ratio (HR) = 2.13, 95% CI 1.04-4.36; P = 0.038], threshold growth (HR = 6.50, 95% CI 2.88-14.65; P <0.001), and HBP hypointensity (HR = 16.83, 95% CI 3.97-71.34; P <0.001) were significant independent predictors of malignant progression.

CONCLUSION

The higher LI-RADS v2018 categories had an increasing risk of progression to a malignant category during long-term evolution. Nonrim APHE, threshold growth, and HBP hypointensity were the imaging features that were significantly predictive of malignant progression.

An interpretable deep learning framework for predicting liver metastases in postoperative colorectal cancer patients using natural language processing and clinical data integration

BACKGROUND

The significance of liver metastasis (LM) in increasing the risk of death for postoperative colorectal cancer (CRC) patients necessitates innovative approaches to predict LM.

AIM

Our study presents a novel and significant contribution by developing an interpretable fusion model that effectively integrates both free-text medical record data and structured laboratory data to predict LM in postoperative CRC patients.

METHODS

We used a robust dataset of 1463 patients and leveraged state-of-the-art natural language processing (NLP) and machine learning techniques to construct a two-layer fusion framework that demonstrates superior predictive performance compared to single modal models. Our innovative two-tier algorithm fuses the results from different data modalities, achieving balanced prediction results on test data and significantly enhancing the predictive ability of the model. To increase interpretability, we employed Shapley additive explanations to elucidate the contributions of free-text clinical data and structured clinical data to the final model. Furthermore, we translated our findings into practical clinical applications by creating a novel NLP score-based nomogram using the top 13 valid predictors identified in our study.

RESULTS

The proposed fusion models demonstrated superior predictive performance with an accuracy of 80.8%, precision of 80.3%, recall of 80.5%, and an F1 score of 80.8% in predicting LMs.

CONCLUSION

This fusion model represents a notable advancement in predicting LMs for postoperative CRC patients, offering the potential to enhance patient outcomes and support clinical decision-making.

Second-line Sonazoid-enhanced ultrasonography for Liver Imaging Reporting and Data System category 3 and 4 on gadoxetate-enhanced magnetic resonance imaging

Purpose

This study investigated the utility of second-line contrast-enhanced ultrasonography (CEUS) using Sonazoid in Liver Imaging Reporting and Data System category 3 (LR-3) and 4 (LR-4) observations on gadoxetate-enhanced magnetic resonance imaging (MRI).

Methods

This retrospective study included LR-3 or LR-4 observations on gadoxetate-enhanced MRI subsequently evaluated with CEUS from 2013 to 2017. The presence of MRI features, CEUS-arterial phase hyperenhancement (CEUS-APHE), and Kupffer phase defect (KPD) was evaluated. Multivariable logistic regression analysis was performed to identify significant imaging features associated with the diagnosis of hepatocellular carcinoma (HCC). The optimal diagnostic criteria were investigated using the McNemar test.

Results

In total, 104 patients with 104 observations (63 HCCs) were included. The presence of both CEUS-APHE and KPD on CEUS enabled the additional detection of 42.3% (11/26) of LR-3 HCCs and 78.4% (29/37) of LR-4 HCCs. Transitional phase (TP) hypointensity (adjusted odds ratio [OR], 10.59; P<0.001), restricted diffusion (adjusted OR, 7.55; P=0.004), and KPD (adjusted OR, 7.16; P=0.003) were significant imaging features for HCC diagnosis. The presence of at least two significant imaging features was optimal for HCC diagnosis (sensitivity, specificity, and accuracy: 88.9%, 78.1%, and 84.6%, respectively), with significantly higher sensitivity than the presence of both CEUS-APHE and KPD (sensitivity, specificity, and accuracy: 63.5% [P=0.001], 92.7% [P=0.077], and 75.0% [P=0.089], respectively).

Conclusion

The combined interpretation of gadoxetate-enhanced MRI and second-line CEUS using Sonazoid, focusing on TP hypointensity, restricted diffusion, and KPD, may be optimal for further characterizing LR-3 and LR-4 observations.

Up-to-Date Role of CT/MRI LI-RADS in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a leading cause of mortality worldwide and a major healthcare burden in most societies. Computed tomography (CT) and magnetic resonance imaging (MRI) play a pivotal role in the medical care of patients with or at risk for hepatocellular carcinoma (HCC). When stringent imaging criteria are fulfilled, CT and MRI allow for diagnosis, staging, and assessment of response to treatment, without the need for invasive workup, and can inform clinical decision making. Owing to the central role of these imaging modalities in HCC management, standardization is essential to facilitate proper imaging technique, accurate interpretation, and clear communication among all stakeholders in both the clinical practice and research settings. The Liver Imaging Reporting and Data System (LI-RADS) is a comprehensive system that provides standardization across the continuum of HCC imaging, including ordinal probabilistic approach for reporting that directs individualized management. This review discusses the up-to-date role of CT and MRI in HCC imaging from the LI-RADS perspective. It also provides a glimpse into the future by discussing how advances in knowledge and technology are likely to enrich the LI-RADS approach.

MRI Ancillary Features for LI-RADS Category 3 and 4 Observations: Improved Categorization to Indicate the Risk of Hepatic Malignancy

OBJECTIVE. The purpose of this study was to investigate whether ancillary features can help stratify malignancy risk in Liver Imaging Reporting and Data System (LI-RADS) category 3 (LR-3) and 4 (LR-4) observations. MATERIALS AND METHODS. This retrospective longitudinal study included 106 LR-3 or LR-4 observations on gadolinium-enhanced MRI obtained from January 2014 to December 2015 in 80 patients who were treatment naïve and at risk (mean age, 58.0 ± 10.7 [SD] years; 60 men). The presence of major and ancillary features, the category determined using only major features, and the final category adjusted by the application of ancillary features were retrospectively analyzed. MRI features were compared using generalized estimating equations, and cumulative incidence curves for malignancy were compared using log-rank tests with a resampling extension. RESULTS. At 6-month follow-up, the cumulative incidence of observations initially categorized as LR-4, observations upgraded to LR-4, observations initially categorized as LR-3, and observations downgraded to LR-3 were 62.5%, 29.7%, 6.2%, and 0%, respectively. The cumulative incidence of malignancy did not differ between observations categorized by major feature as LR-3 and LR-4 (p = 0.12), but was higher in final observations categorized as LR-4 than in those categorized as LR-3 (p < 0.001). Among observations categorized by major feature as LR-3, the cumulative incidence of malignancy was higher in observations upgraded to LR-4 than in observations that were initially graded as LR-3 (p = 0.03), which showed differences in the frequency of restricted diffusion and mild-to-moderate T2-weighted hyperintensity (p < 0.001 for both). CONCLUSION. Final categories determined with ancillary features, instead of categories determined by major features only, can help indicate malignancy risk in LR-3 and LR-4 observations on MRI.

LI-RADS v2018: utilizing ancillary features on gadoxetate-enhanced MRI to modify final LI-RADS category

PURPOSE

To quantify how often the LI-RADS v2018 category changed when utilizing major features only, when utilizing major and ancillary features, and when utilizing major and ancillary features excluding gadoxetate-specific ancillary features.

METHODS

Retrospective analysis of 100 patients age 18 and older at high risk for hepatocellular carcinoma who had an MRI abdomen performed with intravenous contrast gadoxetate between 1/1/2017 and 3/23/2018. Each examination was reviewed by a body fellowship-trained radiologist. LI-RADS category was assigned to the liver observation after review of major features only. Ancillary features were then reviewed and LI-RADS category assigned both including and excluding ancillary features specific to gadoxetate.

RESULTS

Utilizing all MRI ancillary features, including those specific to gadoxetate, changed the final LI-RADS category in 56.4% of liver observations, the majority an increase or decrease from LR-3. When not including the ancillary features specific to gadoxetate, the final LI-RADS category changed in 30.9% of observations, the majority increasing from LR-3 to LR-4.

CONCLUSION

Utilizing LI-RADS v2018 ancillary features can significantly alter the final LI-RADS category, especially when using gadoxetate-specific ancillary features. Understanding the correct application of ancillary features for the final LI-RADS category helps implement a more consistent category assessment amongst users.

LI-RADS ancillary feature prediction of longitudinal category changes in LR-3 observations: an exploratory study

PURPOSE

To determine whether LI-RADS ancillary features predict longitudinal LR-3 observation category changes.

MATERIALS AND METHODS

This exploratory, retrospective, single-center study with an independent reading center included patients who underwent two or more multiphase CT or MRI examinations for hepatocellular carcinoma assessment between 2011 and 2015. Three readers independently evaluated each observation using CT/MRI LI-RADS v2017, and observations categorized LR-3 using major features only were included in the analysis. Prevalence of major and ancillary features was calculated. After excluding low-frequency (< 5%) features, inter-reader agreement was assessed using intraclass correlation coefficient (ICC). Major and ancillary feature prediction of observation upgrade (to LR-4 or higher) or downgrade (to LR-1 or LR-2) on follow-up imaging was assessed using logistic regression.

RESULTS

141 LR-3 observations in 79 patients were included. Arterial phase hyperenhancement, washout, restricted diffusion, mild-moderate T2 hyperintensity, and hepatobiliary phase hypointensity were frequent enough for further analysis (consensus prevalence 5.0-66.0%). ICCs for inter-reader agreement ranged from 0.18 for restricted diffusion to 0.48 for hepatobiliary phase hypointensity. On follow-up, 40% (57/141) of baseline LR-3 observations remained LR-3. 8% (11/141) were downgraded to LR-2, and 42% (59/141) were downgraded to LR-1. A small number were ultimately upgraded to LR-4 (2%, 3/141) or LR-5 (8%, 11/141). None of the assessed major or ancillary features was significantly associated with observation category change. Longer follow-up time was significantly associated with both observation upgrade and downgrade.

CONCLUSION

While numerous ancillary features are described in LI-RADS, most are rarely present and are not useful predictors of LR-3 observation category changes.

Contrast-enhanced ultrasound LI-RADS 2017: comparison with CT/MRI LI-RADS

OBJECTIVE

To compare the classification based on contrast-enhanced ultrasound (CEUS) Liver Imaging Reporting and Data System (LI-RADS) with that of contrast-enhanced CT and MRI (CECT/MRI) LI-RADS for liver nodules in patients at high risk of hepatocellular carcinoma.

METHODS

Two hundred thirty-nine patients with 273 nodules were enrolled in this retrospective study. Each nodule was categorized according to the CEUS LI-RADS version 2017 and CECT/MRI LI-RADS version 2017. The diagnostic performance of CEUS and CECT/MRI was compared. The reference standard was histopathology diagnosis. Inter-modality agreement was assessed with Cohen's kappa.

RESULTS

The inter-modality agreement for CEUS LI-RADS and CECT/MRI LI-RADS was fair with a kappa value of 0.319 (p < 0.001). The positive predictive values (PPVs) of hepatocellular carcinoma (HCC) in LR-5, LR-4, and LR-3 were 98.3%, 60.0%, and 25.0% in CEUS, and 95.9%, 65.7%, and 48.1% in CECT/MRI, respectively. The sensitivities and specificities of LR-5 for diagnosing HCC were 75.6% and 93.8% in CEUS, and 83.6% and 83.3% in CECT/MRI, respectively. The positive predictive values of non-HCC malignancy in CEUS LR-M and CECT/MRI LR-M were 33.9% and 93.3%, respectively. The sensitivity, specificity, and accuracy for diagnosing non-HCC malignancy were 90.9%, 84.5%, and 85.0% in CEUS LR-M and 63.6%, 99.6%, and 96.7% in CECT/MRI LR-M, respectively.

CONCLUSIONS

The inter-modality agreement of the LI-RADS category between CEUS and CECT/MRI is fair. The positive predictive values of HCCs in LR-5 of the CEUS and CECT/MRI LI-RADS are comparable. CECT/MRI LR-M has better diagnostic performance for non-HCC malignancy than CEUS LR-M.

KEY POINTS

• The inter-modality agreement for the final LI-RADS category between CEUS and CECT/MRI is fair. • The LR-5 of CEUS and CECT/MRI LI-RADS corresponds to comparable positive predictive values (PPVs) of HCC. For LR-3 and LR-4 nodules categorized by CECT/MRI, CEUS examination should be performed, at least if they can be detected on plain ultrasound. • CECT/MRI LR-M has better diagnostic performance for non-HCC malignancy than CEUS LR-M. For LR-M nodules categorized by CEUS, re-evaluation by CECT/MRI is necessary.

Liver Imaging Reporting and Data System Version 2018: Impact on Categorization and Hepatocellular Carcinoma Staging

The purpose of this study was to assess the concordance in categorization and radiologic T staging using Liver Imaging Reporting and Data System (LI-RADS, LR) version 2017 (v2017), version 2018 (v2018), and the Organ Procurement and Transplantation Network (OPTN) criteria. All magnetic resonance imaging and computed tomography reports using a standardized LI-RADS macro between April 2015 and March 2018 were identified retrospectively. The major features (size, arterial phase hyperenhancement, washout, enhancing capsule, or threshold growth) were extracted from the report for each LR-3, LR-4, and LR-5 observation. Each observation was assigned a new category based on LI-RADS v2017, v2018, and OPTN criteria. Radiologic T stage was calculated based on the size and number of LR-5 or OPTN class 5 observations. Categories and T stages assigned by each system were compared descriptively. There were 398 patients (66.6% male; mean age, 63.4 years) with 641 observations (median size, 14 mm) who were included. A total of 73/182 (40.1%) observations categorized LR-4 by LI-RADS v2017 were up-categorized to LR-5 by LI-RADS v2018 due to changes in the LR-5 criteria, and 4/196 (2.0%) observations categorized as LR-5 by LI-RADS v2017 were down-categorized to LR-4 by LI-RADS v2018 due to changes in the threshold growth definition. The T stage was higher by LI-RADS v2018 than LI-RADS v2017 in 49/398 (12.3%) patients. Compared with the OPTN stage, 12/398 (3.0%) patients were upstaged by LI-RADS v2017 and 60/398 (15.1%) by LI-RADS v2018. Of 101 patients, 5 (5.0%) patients with T2 stage based on LI-RADS v2017 and 10/102 (9.8%) patients with T2 stage based on LI-RADS v2018 did not meet the T2 criteria based on the OPTN criteria. Of the 98 patients with a T2 stage based on OPTN criteria, 2 (2.0%) had a T stage ≥3 based on LI-RADS v2017 and 6 (6.1%) had a T stage ≥3 based on LI-RADS v2018.

Deep learning for liver tumor diagnosis part II: convolutional neural network interpretation using radiologic imaging features

OBJECTIVES

To develop a proof-of-concept "interpretable" deep learning prototype that justifies aspects of its predictions from a pre-trained hepatic lesion classifier.

METHODS

A convolutional neural network (CNN) was engineered and trained to classify six hepatic tumor entities using 494 lesions on multi-phasic MRI, described in Part 1. A subset of each lesion class was labeled with up to four key imaging features per lesion. A post hoc algorithm inferred the presence of these features in a test set of 60 lesions by analyzing activation patterns of the pre-trained CNN model. Feature maps were generated that highlight regions in the original image that correspond to particular features. Additionally, relevance scores were assigned to each identified feature, denoting the relative contribution of a feature to the predicted lesion classification.

RESULTS

The interpretable deep learning system achieved 76.5% positive predictive value and 82.9% sensitivity in identifying the correct radiological features present in each test lesion. The model misclassified 12% of lesions. Incorrect features were found more often in misclassified lesions than correctly identified lesions (60.4% vs. 85.6%). Feature maps were consistent with original image voxels contributing to each imaging feature. Feature relevance scores tended to reflect the most prominent imaging criteria for each class.

CONCLUSIONS

This interpretable deep learning system demonstrates proof of principle for illuminating portions of a pre-trained deep neural network's decision-making, by analyzing inner layers and automatically describing features contributing to predictions.

KEY POINTS

• An interpretable deep learning system prototype can explain aspects of its decision-making by identifying relevant imaging features and showing where these features are found on an image, facilitating clinical translation. • By providing feedback on the importance of various radiological features in performing differential diagnosis, interpretable deep learning systems have the potential to interface with standardized reporting systems such as LI-RADS, validating ancillary features and improving clinical practicality. • An interpretable deep learning system could potentially add quantitative data to radiologic reports and serve radiologists with evidence-based decision support.

Predictors and Cumulative Frequency of Hepatocellular Carcinoma in High and Intermediate LI-RADS Lesions: A Cohort Study from a Canadian Academic Institution

BACKGROUND

The frequency and predictors of hepatocellular carcinoma (HCC) within each liver imaging reporting and data system (LI-RADS) category remains unclear. We sought to estimate the cumulative frequency of HCC in LI-RADS observations of high/intermediate category and identify clinical/radiographic features associated with HCC.

METHODS

Our diagnostic imaging database was searched for computed tomography/magnetic resonance imaging reports of patients with evidence of cirrhosis and liver observations. LI-RADS categories were determined by imaging review, while demographic and clinical outcomes were assigned by chart review. A composite outcome of clinical/radiographic confirmation of HCC was used. We used multivariable analysis to identify features associated with HCC, and competing risks regression to estimate the cumulative frequency of HCC in each category.

RESULTS

Our search returned 95 patients with 137 observations (LR2 = 4, LR3 = 53, LR4 = 37, and LR5 = 43). On multivariable analysis, increasing age (hazard ratio [HR] 1.76 per 10 years, p = 0.049), washout (HR 5.34, p < 0.002), and increasing size (size < 10 mm reference, 10-20 mm, HR 3.93, p = 0.014; size > 20 mm, HR 21.69, p < 0.001) were associated with HCC. Median time to diagnosis was 6.13 months (interquartile range [IQR] 4.6-13.1), 4.7 months (IQR 2.5-14.5), and 3.6 months (IQR 1.9-6.6) for LR3, 4, and 5 category observations, respectively. The cumulative frequency of HCC was 59.8% in LR3, 84.62% in LR4, and 99.84% in LR5, at last follow-up.

CONCLUSION

The frequency of HCC within each LI-RADS category reflects the intended purpose, intermediate probability for LR3, probable HCC for LR4, and definite HCC for LR5.

Longitudinal evolution of CT and MRI LI-RADS v2014 category 1, 2, 3, and 4 observations

OBJECTIVES

This study assesses the risk of progression of Liver Imaging Reporting and Data System (LI-RADS) categories, and the effects of inter-exam changes in modality or radiologist on LI-RADS categorization.

METHODS

Clinical LI-RADS v2014 CT and MRI exams at our institution between January 2014 and September 2017 were retrospectively identified. Untreated LR-1, LR-2, LR-3, and LR-4 observations with at least one follow-up exam were included. Three hundred and seventy-two observations in 214 patients (149 male, 65 female, mean age 61 ± 10 years) were included during the study period (715 exams total). Cumulative incidence curves for progression to malignant LI-RADS categories (LR-5 or LR-M) and to LR-4 or higher were generated for each index category and compared using log-rank tests with a resampling extension. Relationships between inter-exam changes in LI-RADS category and modality or radiologist, adjusted for inter-exam time intervals, were modeled using mixed effect logistic regressions.

RESULTS

Median inter-exam follow-up interval and total follow-up duration were 123 and 227 days, respectively. Index LR-1, LR-2, LR-3, and LR-4 differed significantly in their cumulative incidences of progression to malignant categories (p < 0.0001), which were 0%, 2%, 7%, and 32% at 6 months, respectively. Index LR-1, LR-2, and LR-3 differed significantly in cumulative incidences of progression to LR-4 or higher (p = 0.003). MRI-MRI exam pairs had more stable LI-RADS categorization compared to CT-CT (OR = 0.460, p = 0.0018).

CONCLUSIONS

LI-RADS observations demonstrate increasing risk of progression to malignancy with increasing category ranging from 0% for LR-1 to 32% for LR-4 at 6 months. Inter-exam modality changes are associated with LI-RADS category changes.

KEY POINTS

• While the majority of LR-2 observations remain stable over long-term follow-up, LR-3 and especially LR-4 observations have a higher risk for category progression. • Category transitions between sequential exams using different modalities (CT vs. MRI) may reflect modality differences rather than biological change. MRI, especially with the same type of contrast agent, may provide the most reproducible categorization, although this needs additional validation. • In a clinical practice setting, in which radiologists refer to prior imaging and reports, there was no significant association between changes in radiologist and changes in LI-RADS categorization.

Joint Consensus Statement of the Indian National Association for Study of the Liver and Indian Radiological and Imaging Association for the Diagnosis and Imaging of Hepatocellular Carcinoma Incorporating Liver Imaging Reporting and Data System

Hepatocellular carcinoma (HCC) is the 6th most common cancer and the second most common cause of cancer-related mortality worldwide. There are currently no universally accepted practice guidelines for the diagnosis of HCC on imaging owing to the regional differences in epidemiology, target population, diagnostic imaging modalities, and staging and transplant eligibility. Currently available regional and national guidelines include those from the American Association for the Study of Liver Disease (AASLD), the European Association for the Study of the Liver (EASL), the Asian Pacific Association for the Study of the Liver, the Japan Society of Hepatology, the Korean Liver Cancer Study Group, Hong Kong, and the National Comprehensive Cancer Network in the United States. India with its large population and a diverse health infrastructure faces challenges unique to its population in diagnosing HCC. Recently, American Association have introduced a Liver Imaging Reporting and Data System (LIRADS, version 2017, 2018) as an attempt to standardize the acquisition, interpretation, and reporting of liver lesions on imaging and hence improve the coherence between radiologists and clinicians and provide guidance for the management of HCC. The aim of the present consensus was to find a common ground in reporting and interpreting liver lesions pertaining to HCC on imaging keeping LIRADSv2018 in mind.

Liver Imaging Reporting and Data System (LI-RADS) Version 2018: Imaging of Hepatocellular Carcinoma in At-Risk Patients

The Liver Imaging Reporting and Data System (LI-RADS) is composed of four individual algorithms intended to standardize the lexicon, as well as reporting and care, in patients with or at risk for hepatocellular carcinoma in the context of surveillance with US; diagnosis with CT, MRI, or contrast material-enhanced US; and assessment of treatment response with CT or MRI. This report provides a broad overview of LI-RADS, including its historic development, relationship to other imaging guidelines, composition, aims, and future directions. In addition, readers will understand the motivation for and key components of the 2018 update.

Contributions of Magnetic Resonance Imaging to Gastroenterological Practice: MRIs for GIs

MRI has transformed from the theoretical, investigative realm to mainstream clinical medicine over the past four decades and has become a core component of the diagnostic toolbox in the practice of gastroenterology (GI). Its success is attributable to exquisite contrast and the ability to isolate specific proton species through the use of different pulse sequences (i.e., T1-weighted, T2-weighted, diffusion-weighted) and exploiting extracellular and hepatobiliary contrast agents. Consequently, MRI has gained preeminence in various GI clinical applications: liver and pancreatic lesion evaluation and detection, liver transplantation evaluation, pancreatitis evaluation, Crohn's disease evaluation (using MR enterography) rectal cancer staging and perianal fistula evaluation. MR elastography, in concert with technical innovations allowing for fat and iron quantification, provides a noninvasive approach, or "MRI virtual liver biopsy" for diagnosis and management of chronic liver diseases. In the future, the arrival of ultra-high-field MR systems (7 T) and the ability to perform magnetic resonance spectroscopy in the abdomen promise even greater diagnostic insight into chronic liver disease.