Post-treatment CT LI-RADS categories: predictors of overall survival in hepatocellular carcinoma post bland transarterial embolization

Optimal Liver Transplantation Criteria for Hepatocellular Carcinoma

Liver transplantation continues to be the optimal treatment for hepatocellular carcinoma (HCC). Given the limited organ supply, patient selection for liver transplant must carefully balance tumor progression with risk of recurrence posttransplant. There are several pretransplant selection criteria that incorporate biomarkers as well as imaging modality to risk-stratify patients as we continue to look for the optimal transplant cutoff for patients with HCC, which should be transplant-center specific, and account for organ availability and dynamic response to locoregional therapy.

Early post-treatment MRI predicts long-term hepatocellular carcinoma response to radiation segmentectomy

OBJECTIVES

Radiation segmentectomy using yttrium-90 plays an emerging role in the management of early-stage HCC. However, the value of early post-treatment MRI for response assessment is uncertain. We assessed the value of response criteria obtained early after radiation segmentectomy in predicting long-term response in patients with HCC.

MATERIALS AND METHODS

Patients with HCC who underwent contrast-enhanced MRI before, early, and 12 months after radiation segmentectomy were included in this retrospective single-center study. Three independent radiologists reviewed images at baseline and 1st follow-up after radiation segmentectomy and assessed lesion-based response according to mRECIST, LI-RADS treatment response algorithm (TRA), and image subtraction. The endpoint was response at 12 months based on consensus readout of two separate radiologists. Diagnostic accuracy for predicting complete response (CR) at 12 months based on the 1st post-treatment MRI was calculated.

RESULTS

Eighty patients (M/F 60/20, mean age 67.7 years) with 80 HCCs were assessed (median size baseline, 1.8 cm [IQR, 1.4-2.9 cm]). At 12 months, 74 patients were classified as CR (92.5%), 5 as partial response (6.3%), and 1 as progressive disease (1.2%). Diagnostic accuracy for predicting CR was fair to good for all readers with excellent positive predictive value (PPV): mRECIST (range between 3 readers, accuracy: 0.763-0.825, PPV: 0.966-1), LI-RADS TRA (accuracy: 0.700-0.825, PPV: 0.983-1), and subtraction (accuracy: 0.775-0.825, PPV: 0.967-1), with no difference in accuracy between criteria (p range 0.053 to > 0.9).

CONCLUSION

mRECIST, LI-RADS TRA, and subtraction obtained on early post-treatment MRI show similar performance for predicting long-term response in patients with HCC treated with radiation segmentectomy.

CLINICAL RELEVANCE STATEMENT

Response assessment extracted from early post-treatment MRI after radiation segmentectomy predicts complete response in patients with HCC with high PPV (≥ 0.96).

KEY POINTS

• Early post-treatment response assessment on MRI predicts response in patients with HCC treated with radiation segmentectomy with fair to good accuracy and excellent positive predictive value. • There was no difference in diagnostic accuracy between mRECIST, LI-RADS, and subtraction for predicting HCC response to radiation segmentectomy.

Updates on LI-RADS Treatment Response Criteria for Hepatocellular Carcinoma: Focusing on MRI

As the incidence of hepatocellular carcinoma (HCC) and subsequent treatments with liver-directed therapies rise, the complexity of assessing lesion response has also increased. The Liver Imaging Reporting and Data Systems (LI-RADS) treatment response algorithm (LI-RADS TRA) was created to standardize the assessment of response after locoregional therapy (LRT) on contrast-enhanced CT or MRI. Originally created based on expert opinion, these guidelines are currently undergoing revision based on emerging evidence. While many studies support the use of LR-TRA for evaluation of HCC response after thermal ablation and intra-arterial embolic therapy, data suggest a need for refinements to improve assessment after radiation therapy. In this manuscript, we review expected MR imaging findings after different forms of LRT, clarify how to apply the current LI-RADS TRA by type of LRT, explore emerging literature on LI-RADS TRA, and highlight future updates to the algorithm. EVIDENCE LEVEL: 3. TECHNICAL EFFICACY: Stage 2.

Quantitative dual-energy computed tomography texture analysis predicts the response of primary small hepatocellular carcinoma to radiofrequency ablation

BACKGROUND

Radiofrequency ablation (RFA) is one of the effective therapeutic modalities in patients with hepatocellular carcinoma (HCC). However, there is no proper method to evaluate the HCC response to RFA. This study aimed to establish and validate a clinical prediction model based on dual-energy computed tomography (DECT) quantitative-imaging parameters, clinical variables, and CT texture parameters.

METHODS

We enrolled 63 patients with small HCC. Two to four weeks after RFA, we performed DECT scanning to obtain DECT-quantitative parameters and to record the patients' clinical baseline variables. DECT images were manually segmented, and 56 CT texture features were extracted. We used LASSO algorithm for feature selection and data dimensionality reduction; logistic regression analysis was used to build a clinical model with clinical variables and DECT-quantitative parameters; we then added texture features to build a clinical-texture model based on clinical model.

RESULTS

A total of six optimal CT texture analysis (CTTA) features were selected, which were statistically different between patients with or without tumor progression (P < 0.05). When clinical variables and DECT-quantitative parameters were included, the clinical models showed that albumin-bilirubin grade (ALBI) [odds ratio (OR) = 2.77, 95% confidence interval (CI): 1.35-6.65, P = 0.010], λ_{AP (40-100 keV)} (OR = 3.21, 95% CI: 3.16-5.65, P = 0.045) and IC_AP (OR = 1.25, 95% CI: 1.01-1.62, P = 0.028) were associated with tumor progression, while the clinical-texture models showed that ALBI (OR = 2.40, 95% CI: 1.19-5.68, P = 0.024), λ_{AP (40-100 keV)} (OR = 1.43, 95% CI: 1.10-2.07, P = 0.019), and CTTA-score (OR = 2.98, 95% CI: 1.68-6.66, P = 0.001) were independent risk factors for tumor progression. The clinical model, clinical-texture model, and CTTA-score all performed well in predicting tumor progression within 12 months after RFA (AUC = 0.917, 0.962, and 0.906, respectively), and the C-indexes of the clinical and clinical-texture models were 0.917 and 0.957, respectively.

CONCLUSIONS

DECT-quantitative parameters, CTTA, and clinical variables were helpful in predicting HCC progression after RFA. The constructed clinical prediction model can provide early warning of potential tumor progression risk for patients after RFA.

LI-RADS Treatment Response Algorithm: Performance and Diagnostic Accuracy With Radiologic-Pathologic Explant Correlation in Patients With SBRT-Treated Hepatocellular Carcinoma

PURPOSE

Our purpose was to evaluate the accuracy of LI-RADS Treatment Response Algorithm (LR-TRA) for assessing the viability of hepatocellular carcinoma (HCC) treated with stereotactic body radiation therapy (SBRT), using explant pathology as the gold standard.

METHODS AND MATERIALS

This retrospective study included patients who underwent SBRT for locoregional treatment of HCC between 2008 and 2019 with subsequent liver transplantation. Five radiologists independently assessed all treated lesions by using the LR-TRA. Imaging and posttransplant histopathology were compared. Lesions were categorized as either completely (100%) or incompletely (<100%) necrotic, and performance characteristics and predictive values for the LR-TR viable and nonviable categories were calculated for each reader. Interreader reliability was calculated using the Fleiss kappa test.

RESULTS

A total of 40 treated lesions in 26 patients (median age, 63 years [interquartile range, 59.4-65.5]; 23 men) were included. For lesions treated with SBRT, sensitivity for incomplete tumor necrosis across readers ranged between 71% and 86%, specificity between 85% and 96%, and positive predictive value between 86% and 92%, when the LR-TR equivocal category was treated as nonviable, accounting for subject clustering. When the LR-TR equivocal category was treated as viable, sensitivity of complete tumor necrosis for lesions treated with SBRT ranged from 88% to 96%, specificity from 71% to 93%, and negative predictive value from 85% to 96%. Interreader reliability was fair (k = 0.22; 95% confidence interval, 0.13-0.33). Although a loss of arterial phase hyperenhancement (APHE) was highly correlated with pathologically nonviable tumor on explant, almost half of the patients with APHE had pathologically nonviable tumor on explant.

CONCLUSIONS

LR-TRA v2018 performs well for predicting complete and incomplete necrosis in HCC treated with SBRT. In contrast to other locoregional therapies, the presence of APHE after SBRT does not always indicate viable tumor and suggests that observation may be an appropriate strategy for these patients.

Inter-observer agreement using the LI-RADS version 2018 CT treatment response algorithm in patients with hepatocellular carcinoma treated with conventional transarterial chemoembolization

Aim

To determine inter-reader agreement in categorization of imaging features using the Liver Imaging Reporting and Data System (LI-RADS) treatment response (LR-TR) algorithm in patients with hepatocellular carcinoma (HCC) treated with conventional transarterial chemoembolization (cTACE).

Methods

Two radiologists used the LR-TR algorithm to assess 112 computed tomography (CT) examinations of 102 patients treated with cTACE. The inter-observer agreement in categorization of LR-TR features was assessed using kappa (κ) statistics.

Results

There was substantial inter-observer agreement between the two reviewers using the LR-TR algorithm (κ = 0.70; 95% CI 0.58–0.81). The two reviewers categorized tumors as non-viable in 37 (33.0%) and 39 (34.8%) of 112 examinations, viable in 58 (51.8%) and 62 (55.4%) examinations, and equivocal in 18 (16.1%) and 11 (9.8%) examinations, respectively. There was almost perfect inter-observer agreement for the LR-TR non-viable category (κ = 0.80; 95% CI 0.68–0.92), substantial agreement for the viable category (κ = 0.78 95% CI 0.67–0.90), and fair agreement for the equivocal category (κ = 0.25; 95% CI 0.02–0.49).

Conclusion

The LR-TR algorithm conveys high degrees of inter-observer agreement for the assessment of CT imaging features in the viable and non-viable categories. Further refinement of indeterminate features may be necessary to improve the correct categorization of equivocal lesions.

Graphic abstract

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.