Per-Feature Accuracy of Liver Imaging Reporting and Data System Locoregional Treatment Response Algorithm: A Systematic Review and Meta-Analysis

Assessment of viable tumours by [68Ga]Ga-FAPI-04 PET/CT after local regional treatment in patients with hepatocellular carcinoma

PURPOSE

To investigate the efficacy of [68Ga]Ga-FAPI-04 PET/CT for assessing viable tumours (VTs) after local regional treatment (LRT) in hepatocellular carcinoma (HCC) patients. The related imaging features of HCC after LRT are preliminarily discussed.

METHODS

A cohort of 37 LRT patients with HCC (encompassing 51 lesions) was retrospectively included from a prospective parent study (ChiCTR2000039099), and sequential PET/CT using [18F]FDG and [68Ga]Ga-FAPI-04 was performed. The diagnostic accuracies of [68Ga]Ga-FAPI-04 and [18F]FDG PET/CT and multiphasic CT/MRI for detecting VTs after LRT were calculated and analysed. Pathological examination was considered the gold standard for VT diagnosis, and clinical follow-up was used as the reference standard. The SUVmax and tumour-to-background ratio (TBR) derived from [18F]FDG and [68Ga]Ga-FAPI-04 PET/CT were calculated and compared. Moreover, the SUVmax, target-to-normal liver ratio (TNR) of VT, tumour necrosis (TN), benign rim (BR), and normal liver (NL) from different imaging modalities after LRT for HCC were compared.

RESULTS

Both the sensitivity (96.0% [24/25] vs. 36.0% [9/25], p < 0.001) and accuracy (94.1%, [48/51] vs. 68.6% [35/51], p = 0.004) of [68Ga]Ga-FAPI-04 PET/CT for detecting VTs after LRT were greater than those of [18F]FDG PET/CT, whereas their specificities were comparable (92.3% [24/26] vs. 100% [26/26]). Notably, [68Ga]Ga-FAPI-04 PET/CT had a greater SUVmax (9.80 vs. 3.60) and TBR (9.93 vs. 1.57) than [18F]FDG PET/CT in VT patients (all p < 0.001). Furthermore, VT had a greater SUVmax and TNR than did TN, BR, and NL on [68Ga]Ga-FAPI-04 PET/CT (all p < 0.001) and exhibited morphologic nodular, mass-like, or irregular tracer uptake. Although no significant differences were observed for VT detection (all p > 0.05), [68Ga]Ga-FAPI-04 PET/CT and multiphasic CT/MRI complemented each other in some cases.

CONCLUSION

[68Ga]Ga-FAPI-04 PET/CT not only presented higher sensitivity and accuracy than [18F]FDG PET/CT for diagnosing VTs after LRT for HCC but also showed comparable diagnostic accuracy and complementary roles with multiphasic CT/MRI. Overall, [68Ga]Ga-FAPI-04 PET/CT may play an essential role in surveillance after LRT for HCC.

TRIAL REGISTRATION

Chinese Clinical Trial Registry ChiCTR2000039099. Registered 17 October 2020.

LI-RADS Nonradiation Treatment Response Algorithm Version 2024: Diagnostic Performance and Impact of Ancillary Features

Background: LI-RADS Treatment Response Algorithm (TRA) version 2024 (v2024) introduced separate algorithms for detecting hepatocellular carcinoma (HCC) viability after radiation and nonradiation locoregional therapies (LRT). The nonradiation algorithm incorporated MRI-based ancillary features to optionally upgrade lesions from LR-TR Equivocal to LR-TR Viable. Objective: To compare the diagnostic performance of LI-RADS Nonradiation TRA v2024 with LI-RADS TRA version 2017 (v2017) and modified RECIST (mRECIST) for evaluating HCC response to LRT on MRI, with attention to the impact of ancillary features. Methods: This retrospective study included 231 patients (198 male, 33 female; median age, 56 years) who underwent LRT for HCC followed by liver resection or transplant between January 2017 and December 2022. Two radiologists (R1, R2) independently evaluated treated lesions (n=306) using LI-RADS Nonradiation TRA v2024, LI-RADS TRA v2017, and mRECIST. Lesions were classified as showing pathologic viability (n=249) or complete pathologic necrosis (n=57) based on curative surgery pathology. Diagnostic performance for pathologic viability was compared using Bonferroni-adjusted McNemar tests, classifying LR-TR Equivocal assessments as test negative. Results: Sensitivity, specificity, and accuracy for LI-RADS Nonradiation TRA v2024 with ancillary features were 85.5%, 75.4%, and 83.7% (R1), and 87.2%, 63.2%, and 82.7% (R2); for LI-RADS Nonradiation TRA v2024 without ancillary features were 81.1%, 78.9%, and 80.7% (R1), and 80.3%, 78.9%, and 80.1% (R2); for LI-RADS TRA v2017 were 79.9%, 82.5%, and 80.4% (R1), and 79.1%, 79.0%, and 79.1% (R2); and for mRECIST, were 83.9%, 54.4%, and 78.4% (R1), and 78.2%, 40.4%, and 78.4% (R2). LI-RADS Nonradiation TRA v2024 with ancillary features showed higher sensitivity and accuracy than LI-RADS Nonradiation v2024 without ancillary features (both readers); higher sensitivity than LI-RADS TRA v2017 (both readers); higher specificity than mRECIST (both readers); and higher accuracy than LI-RADS TRA v2017 (R2) (p<.008); remaining comparisons between LI-RADS Nonradiation TRA v2024 with ancillary features and other systems were not significant (p>.008). Conclusion: LI-RADS Nonradiation TRA v2024 showed good diagnostic performance in detecting pathologic viability. Ancillary features yielded improved sensitivity and accuracy without significant change in specificity. Clinical Impact: Use of LI-RADS Nonradiation TRA v2024 with ancillary features is recommended for guiding prognostic assessments and treatment decisions after LRT.

CT/MRI LI-RADS 2024 Update: Treatment Response Assessment

With the rising incidence of hepatocellular carcinoma, there has been increasing use of local-regional therapy (LRT) to downstage or bridge to transplant, for definitive treatment, and for palliation. The CT/MRI Liver Imaging Reporting and Data System (LI-RADS) Treatment Response Assessment (TRA) algorithm provides guidance for step-by-step tumor assessment after LRT and standardized reporting. Current evidence suggests that the algorithm performs well in the assessment of tumor response to arterial embolic and loco-ablative therapies and fair when assessing response to radiation-based therapies, with limited data to validate the latter. Both evidence-based and expert-based refinements of the algorithm are needed to improve its diagnostic accuracy after varying types of LRT. This review provides an overview of the challenges and limitations of the LI-RADS TRA algorithm version 2017 and discusses the refinements introduced in the updated 2024 LI-RADS algorithm for CT/MRI.

LI-RADS version 2018 treatment response algorithm on extracellular contrast-enhanced MRI in patients treated with transarterial chemoembolization for hepatocellular carcinoma: diagnostic performance and the added value of ancillary features

BACKGROUND

The Liver Imaging Reporting and Data System (LI-RADS) Treatment Response Algorithm (TRA) (LI-RADS TRA) is used for assessing response of HCC to locoregional therapy (LRT), however, the value of ancillary features (AFs) for TACE-treated HCCs has not been extensively investigated on extracellular agent MRI (ECA-MRI).

PURPOSE

To evaluate the diagnostic performance of LI-RADS v2018 TRA on ECA-MRI for HCC treated with transarterial chemoembolization (TACE) and the value of ancillary features.

METHODS

This retrospective study included patients who underwent TACE for HCC and then followed by hepatic surgery between January 2019 and June 2023 with both pre- and post-TACE contrast-enhanced MRI available. Two radiologists independently evaluated the post-treated lesions on MRI using LI-RADS treatment response (TR) (LR-TR) algorithm and modified LR-TR (mLR-TR) algorithm in which ancillary features (restricted diffusion and intermediate T2-weighted hyperintensity) were added, respectively. Lesions were categorized as complete pathologic necrosis (100%, CPN) and non-complete pathologic necrosis (< 100%, non-CPN) on the basis of surgical pathology. The diagnostic performance in predicting viable and non-viable tumors based on LR-TR and mLR-TR algorithms was compared using the McNemar test. Interreader agreement was calculated by using Cohen's weighted and unweighted κ.

RESULTS

A total of 61 patients [mean age 59 years ± 10 (standard deviation); 47 men] with 79 lesions (57 pathologically viable) were included. For non-CPN prediction, the sensitivity, specificity of LR-TR viable and mLR-TR viable category were 75% (43 of 57), 82% (18 of 22) and 88% (50 of 57), 77% (17 of 22), respectively, the sensitivity of mLR-TR was significantly higher than that of LR-TR (P = 0.016) without difference in specificity (P = 1.000). Interreader agreement for LR-TR and mLR-TR category was moderate (k = 0.50, 95% confidence interval 0.33, 0.67, k = 0.42, 95% confidence interval 0.20, 0.63). The sensitivity of both LR-TR and mLR-TR algorithms in predicting viable tumors between conventional TACE (cTACE) and drug-eluting beads TACE (DEB-TACE) did not have significant difference (cTACE: 76%, 89% vs. DEB-TACE: 73%, 82%).

CONCLUSIONS

On ECA-MRI, applying ancillary features to LI-RADS v2018 TRA can improve the sensitivity in predicting pathologic tumor viability in patients treated with TACE for hepatocellular carcinoma with no significant difference in specificity.

Liver Cancer: Improving Standard Diagnosis and Therapy

In 2020, liver cancer ranked sixth for incidence (841,000 cases) and fourth for deaths globally (782,000 cases) [...].

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.

LI-RADS Treatment Response versus Modified RECIST for Diagnosing Viable Hepatocellular Carcinoma after Locoregional Therapy: A Systematic Review and Meta-Analysis of Comparative Studies

Purpose

To systematically compare the performance of liver imaging reporting and data system treatment response (LR-TR) with the modified Response Evaluation Criteria in Solid Tumors (mRECIST) for diagnosing viable hepatocellular carcinoma (HCC) treated with locoregional therapy (LRT).

Materials and Methods

Original studies of intra-individual comparisons between the diagnostic performance of LR-TR and mRECIST using dynamic contrast-enhanced CT or MRI were searched in MEDLINE and EMBASE, up to August 25, 2021. The reference standard for tumor viability was surgical pathology. The meta-analytic pooled sensitivity and specificity of the viable category using each criterion were calculated using a bivariate random-effects model and compared using bivariate meta-regression.

Results

For five eligible studies (430 patients with 631 treated observations), the pooled per-lesion sensitivities and specificities were 58% (95% confidence interval [CI], 45%–70%) and 93% (95% CI, 88%–96%) for the LR-TR viable category and 56% (95% CI, 42%–69%) and 86% (95% CI, 72%–94%) for the mRECIST viable category, respectively. The LR-TR viable category provided significantly higher pooled specificity (p < 0.01) than the mRECIST but comparable pooled sensitivity (p = 0.53).

Conclusion

The LR-TR algorithm demonstrated better specificity than mRECIST, without a significant difference in sensitivity for the diagnosis of pathologically viable HCC after LRT.

Enhancement and denoising method for low-quality MRI, CT images via the sequence decomposition Retinex model, and haze removal algorithm

The visibility and analyzability of MRI and CT images have a great impact on the diagnosis of medical diseases. Therefore, for low-quality MRI and CT images, it is necessary to effectively improve the contrast while suppressing the noise. In this paper, we propose an enhancement and denoising strategy for low-quality medical images based on the sequence decomposition Retinex model and the inverse haze removal approach. To be specific, we first estimate the smoothed illumination and de-noised reflectance in a successive sequence. Then, we apply a color inversion from 0-255 to the estimated illumination, and introduce a haze removal approach based on the dark channel prior to adjust the inverted illumination. Finally, the enhanced image is generated by combining the adjusted illumination and the de-noised reflectance. As a result, improved visibility is obtained from the processed images and inefficient or excessive enhancement is avoided. To verify the reliability of the proposed method, we perform qualitative and quantitative evaluation on five MRI datasets and one CT dataset. Experimental results demonstrate that the proposed method strikes a splendid balance between enhancement and denoising, providing performance superior to that of several state-of-the-art methods.