Purpose To determine whether combining the arterial phase onset time to washout onset time interval (AWTI) with washout onset time improves the accuracy of contrast-enhanced US (CEUS) Liver Imaging Reporting and Data System (LI-RADS) and reduces the incidence of hepatocellular carcinoma (HCC) being misclassified as LR-M. Materials and Methods CEUS and clinical data from patients with focal liver lesions (FLLs), collected between January 2019 and October 2024, were retrospectively analyzed. The AWTI was calculated for all FLLs with washout < 60 seconds. A revised classification was proposed: (a) washout < 45 seconds + AWTI < 21 seconds for LR-M; and (b) washout ≥ 45 seconds + AWTI ≥ 21 seconds for LR-5. Diagnostic performance of the revised classification was compared with that of the standard LI-RADS with washout < 60 seconds (and ≥60 seconds). Results The study included 352 patients (median age, 56 years [IQR, 46-65]; 284 male), each with one FLL. Among HCCs, 75.9% exhibited washout ≥ 60 seconds. In contrast, 75.0% of intrahepatic cholangiocarcinomas, 52.0% of metastatic liver carcinomas, and 66.7% of other malignancies demonstrated washout < 45 seconds. Of benign FLLs, 12.8% showed washout ≥ 60 seconds. Among FLLs classified as LR-5, 92.6% were HCCs; among FLLs classified as LR-M, 41.8% were HCCs. The optimal AWTI cutoff to distinguish LR-M from LR-5 was 21 seconds. The revised LR-M (washout < 45 seconds + AWTI < 21 seconds) significantly increased the positive predictive value to 92.6% (P < .05). The revised LR-5 (washout ≥ 45 seconds + AWTI ≥ 21 seconds) significantly improved the sensitivity and negative predictive value to 89.0% and 87.0%, respectively (both P < .05). The diagnostic accuracy and area under the receiver operating characteristic curve were 88.4% and 0.88, respectively, despite slight decreases in the specificity and positive predictive value. Conclusion Combining washout onset time with AWTI as an alternative for standard washout onset time (<60 sec/≥60 sec) reduced misclassification of HCC and significantly improved the diagnostic performance of CEUS LI-RADS for LR-M and LR-5. Keywords: Ultrasound, Reconstruction Algorithms, Diagnosis, Classification, Ultrasound-Contrast, Abdomen/GI, Liver, Contrast Agents-Intravenous, Efficacy Studies, Hepatocellular Carcinoma, Contrast-enhanced Ultrasound, Liver Imaging Reporting and Data System, Washout Time Supplemental material is available for this article. © RSNA, 2025 See also commentary by Hui and Chiang in this issue.

To investigate the value of dynamic contrast-enhanced ultrasound (DCE-US) analysis using the Liver Imaging Reporting and Data System (LI-RADS) to improve the diagnosis of hepatocellular carcinoma (HCC). This multicenter study retrospectively enrolled consecutive high-risk patients for HCC who underwent contrast-enhanced ultrasound (CEUS) between December 2022 and June 2023. Quantitative CEUS analysis was performed using VueBox® to obtain diagnostic parameters for HCC. These parameters were used as auxiliary indicators to reassign the LI-RADS categories. The reference standard was pathologic confirmation or composite criteria. The diagnostic performance of LI-RADS with and without quantitative DCE-US parameters was assessed. 269 patients (median age, 61years [interquartile range, 52-69]; 206 men, 63 women) with 269 focal liver lesions (FLLs) (median size, 40mm [interquartile range, 25-62mm]) were included. Among the 269 FLLs, 227 were HCC, 31 non-HCC malignancies, and 11 benign lesions. DCE-USanalysis showed HCChad higher rise time (RT) and fall time (FT) at the lesion marginthan non-HCC malignancies (both P < 0.05) but lower RT and FT than benign lesions (both P < 0.05).RT at the lesion margin (range 17.48s-21.16s) serves as an auxiliary indicator for HCC diagnosis. Compared to CEUS LI-RADS, the revised LR-5 improved sensitivity (61.7 vs. 52.8%, P < 0.001)without a significant decrease in specificity (76.2 vs. 83.3%,P = 0.25) for diagnosing HCC. DCE-USquantitativeanalysis improved the sensitivity for HCC diagnosis without affecting specificity, thereby optimizing the diagnostic performance of CEUS LI-RADS.

Liver Imaging Reporting and Data System (LI-RADS) category 3 (LR-3) observations remain indeterminate and often result in repeated follow-up or biopsy. Prostate-specific membrane antigen (PSMA) is overexpressed in hepatocellular carcinoma (HCC) neovasculature and may serve as a useful imaging biomarker. This study aimed to evaluate whether [68Ga]Ga-PSMA-11 PET/MRI improved characterization of LR-3 observations in patients with cirrhosis compared with MRI alone. Methods: In this prospective study, conducted between March 2022 and June 2024, 19 patients with cirrhosis and 54 LR-3 observations identified on prior MRI underwent [68Ga]Ga-PSMA-11 PET/MRI. An observation was classified as HCC if it demonstrated focal 68Ga-PSMA uptake greater than background liver combined with at least 1 LI-RADS major or ancillary feature. The reference standard was histopathology or a follow-up MRI within 12 mo. Diagnostic metrics were calculated. Univariable logistic regression and decision tree analysis were performed to identify imaging predictors of malignancy. Results: Of the 54 LR-3 observations, 13 (24%) were confirmed as HCC and 41 (76%) as benign. [68Ga]Ga-PSMA-11 PET/MRI correctly identified 12 of 13 HCCs (sensitivity, 92%; 95% CI, 66.7-99.6) and 39 of 41 benign observations (specificity, 95%; 95% CI, 81.9-99.3). Overall diagnostic accuracy was 94%, with a positive predictive value of 86% and negative predictive value of 97%. Diagnostic performance was significantly better than MRI alone (McNemar test, P < 0.001). [68Ga]Ga-PSMA-11 uptake was the only significant imaging predictor of malignancy on univariable analysis (odds ratio, 5.7; P = 0.017). Decision tree analysis identified [68Ga]Ga-PSMA-11 uptake, observation size, and hepatobiliary phase hypointensity as principal discriminators. Conclusion: [68Ga]Ga-PSMA-11 PET/MRI demonstrates high diagnostic accuracy in differentiating malignant from benign LR-3 liver observations in patients with cirrhosis. This technique may reduce unnecessary follow-up imaging and biopsy. These results support further validation of [68Ga]Ga-PSMA-11 PET/MRI as a promising imaging approach for indeterminate liver observations.

Background Current CT ancillary features (AFs) have limitations, as several AFs are more evident at MRI. Precontrast low attenuation may serve as a potential AF in the Liver Imaging Reporting and Data System (LI-RADS). Purpose To evaluate the diagnostic value of precontrast low attenuation at CT as an additional AF for diagnosing hepatocellular carcinoma (HCC) and to assess its impact on LI-RADS diagnostic performance. Materials and Methods This retrospective study included adults at risk of HCC who underwent multiphase dynamic liver CT before hepatic resection or liver transplant at a tertiary referral facility between January and December 2022. Two radiologists assessed the presence of major features and AFs for each hepatic observation on the basis of LI-RADs categories, as follows: LR-3, intermediate probability of malignancy; LR-4, probably HCC; and LR-5, definitely HCC. Each lesion was assigned a LI-RADS category twice: first, by using AFs only (LI-RADS category with AFs only) and next, by using AFs with precontrast low attenuation (LI-RADS category with AFs and precontrast low attenuation). Precontrast low attenuation was defined as an attenuation of the target hepatic observation lower than that of the liver parenchyma based on visual assessment. Histopathologic analysis and clinical assessment were used as reference standards. The diagnostic performance of the two LI-RADS strategies was compared using generalized estimating equations. Results A total of 194 patients (mean age, 59 years ± 10 [SD]; 159 men) with 328 hepatic observations were included: 187 (57.0%) HCCs, 26 (7.9%) non-HCC malignancies, and 115 (35.1%) benign lesions. Precontrast low attenuation was associated with HCCs, yielding a diagnostic odds ratio of 9.1 (95% CI: 4.9, 16.6; P < .001). Adding precontrast low attenuation upgraded 20 observations (17 HCCs and three dysplastic nodules) from LR-3 to LR-4, increasing the proportion of HCCs in LR-4 from 64.7% (22 of 34) to 72.2% (39 of 54). Compared with LR with AFs only, LR with AFs and precontrast low attenuation had a higher sensitivity (88.6% [163 of 187] vs 79.9% [146 of 187]; P < .001), with no evidence of a difference in specificity (82.7% [121 of 141] vs 85.0% [124 of 141]; P = .06). Conclusion Applying precontrast low attenuation at CT as an additional AF increased the proportion of HCCs in LR-4 and improved the sensitivity of LI-RADS for diagnosing HCC. © RSNA, 2025 Supplemental material is available for this article.

Liver Observations in Chronic Liver Disease on Noncontrast Abbreviated Magnetic Resonance Imaging MRI (AMRI): Proposal of Modified Liver Imaging Reporting and Data System (AMRI-LI-RADS) Categorization.

LI-RADS CT and MRI Major Feature Association with Vessels Encapsulating Tumor Clusters Pattern and Recurrence in Hepatocellular Carcinoma: A Preliminary Study.

Hepatocellular carcinoma (HCC) is the most common variant of primary liver cancer, characterized by high mortality and unfavorable prognosis. Global incidence and mortality from HCC continue to rise despite progress in the treatment of viral hepatitis due to the increasing prevalence of metabolic dysfunction-associa­ted steatotic liver disease and obesity. Based on the analysis of lite­rature sources from the Pubmed, MedLine, The Cochrane Library, Embase databases, the review summarizes current data on the epidemiology, key risk factors, pathogenesis and molecular classification of HCC. Special attention is paid to modern approaches to diagnosis, in particular imaging methods, the Liver Imaging Reporting and Data System, the role of non-invasive biomarkers and morphological verification. The importance of screening programs in high-risk groups and timely interdisciplinary interaction is emphasized, which allows optimizing the strategy of patient management in accordance with international clinical guidelines.

ObjectivesTo explore the role of apparent diffusion coefficient (ADC) changes in predicting pathological response to preoperative radiotherapy (RT) in hepatocellular carcinoma (HCC) compared to existing evaluation criteria, using histopathology as the reference standard.Materials and methodsBuilding on the prospective clinical trial, we included 35 patients with 38 HCCs who underwent preoperative RT followed by hepatectomy between December 2014 and January 2019. Pre- and post-RT ADC parameters (ADCroi and ADCslice measured from representative areas and histogram parameters derived from whole-tumor volume) were compared, and the percentage change of parameters (Δ-parameters%) was calculated to correlate with major pathological response (MPR). The modified Response Evaluation Criteria in Solid Tumors (mRECIST) and Liver Imaging Reporting and Data System Treatment Response (LR-TR) categories were evaluated. ROC analysis was performed to assess discrimination performance.ResultsADC values, interquartile range, range, variance, mean absolute deviation, robust mean absolute deviation, and root mean squared increased; energy and total energy decreased; and skewness developed into negative skewness after RT. Higher Δ-ADCroi%, Δ-ADCslice%, Δ-ADCmean%, and Δ-ADCmedian% and lower Δ-energy% and Δ-total energy% were associated with MPR. LR-ADCslice showed the best performance, with significantly higher AUC than mRECIST/LR-TR, Δ-ADCmean%, and Δ-ADCmedian% (0.917 vs 0.708, 0.732, and 0.705, respectively; p = 0.005, 0.029, and 0.023). Responders had significantly better RFS than non-responders according to Δ-ADCroi% (p = 0.024).ConclusionsADC changes have the potential to predict the pathological response of HCC to preoperative RT, thereby enhancing current evaluation criteria. Integration of Δ-ADCslice% and LR-TR yielded the best results.Critical relevance statementΔ-ADCslice%, with high performance in predicting pathological response, excellent inter-observer agreement, and the potential to supplement existing evaluation criteria, is a promising method for determining therapeutic response to preoperative radiotherapy and may facilitate the early indication for further surgery.Key PointsPrecise assessment of hepatocellular carcinoma response is required for patients undergoing preoperative radiotherapy.Radiotherapy induced an increase in ADC values and heightened intratumoral heterogeneity.As the delineated region of interest expanded, AUC decreased and inter-observer agreement increased.Δ-ADCslice% exhibited excellent performance in predicting pathological response and the potential to supplement existing evaluation criteria.Graphical

BACKGROUNDContrast-enhanced ultrasound (CEUS) offers valuable reference data for the early diagnosis of hepatocellular carcinoma (HCC) through dynamic enhancement patterns and quantitative analysis.AIMTo evaluate the clinical value, diagnostic accuracy, and imaging characteristics of CEUS in the early diagnosis of HCC and its correlation with HCC pathological findings.METHODSThis single-center retrospective study included 125 patients suspected of having primary liver cancer who underwent CEUS at the Department of Hepatobiliary Surgery and Imaging of our hospital from January 2022 to March 2024. All patients were diagnosed with HCC via postoperative pathology or puncture histology. All patients underwent conventional ultrasound examination and CEUS, while some underwent computed tomography or magnetic resonance imaging examination. Clinical data, liver function, serological indicators, and imaging results were collected. Key CEUS indicators, including arterial phase enhancement time (APT) and peak enhancement intensity (PEI), were analyzed.RESULTSOf the 125 patients, 66.40% were male, with a mean age of 56.74 ± 11.25 years. Conventional type HCC accounted for 71.20%, with histological grades I (14.40%), II (51.20%), and III-IV (34.40%). CEUS enhancement patterns included “fast-in and fast-out” (36%), “fast-in and slow-out” (40%), and “continuous enhancement” (24%). APT < 15 seconds was observed in 40% of patients, and PEI ≥ 1.5 in 56%. Correlation analysis revealed significant negative correlations between tumor differentiation grade and APT, washout completion time, and longest diameter (P < 0.01). Logistic regression identified PEI [odds ratio (OR) = 3.374], WIT (OR = 0.541), lesion boundary characteristics, and APT (OR = 0.471) as significant predictors. Receiver operating characteristic analysis demonstrated high diagnostic performance: PEI (area under the curve = 0.893), WIT (0.851), lesion boundary characteristics (0.876), and APT (0.864), all with Youden’s index > 0.4. Subgroup analysis showed comparable overall diagnostic performance between CEUS and computed tomography/magnetic resonance imaging, but computed tomography/magnetic resonance imaging had higher sensitivity and specificity for Liver Imaging Reporting and Data System 5 lesions (P = 0.032).CONCLUSIONCEUS holds significant clinical value in the early diagnosis of HCC, as it effectively identifies the typical imaging characteristics of early-stage HCC through dynamic contrast enhancement and quantitative analysis, particularly during the arterial and portal phases. As a non-invasive, cost-effective, and efficient imaging modality, CEUS has a broad clinical application potential.

The 2024 version of the Liver Imaging Reporting and Data System (LI-RADS) Treatment Response Assessment (TRA) introduces two distinct algorithms: nonradiation TRA and radiation TRA. We aimed to assess the diagnostic performance of LI-RADS v2024 nonradiation TRA in evaluating hepatocellular carcinoma (HCC) treated with transarterial chemoembolization (TACE). This retrospective single-center study enrolled 167 patients (mean age, 59 years ± 10 (standard deviation), 136 men) with HCC who underwent TACE followed by surgery. Post-treatment contrast-enhanced MRI was independently evaluated by two radiologists using the LI-RADS 2024 nonradiation TRA algorithm. Histopathologic results (complete necrosis (44/191) vs. incomplete necrosis [147/191]) served as the reference standard. Sensitivity, specificity, and accuracy of LI-RADS Treatment Response (LR-TR) categories were calculated. The impact of ancillary features (AFs) on diagnostic performance was analyzed using the McNemar test. For predicting incomplete necrosis, the LR-TR Viable category showed a sensitivity of 70.1% (95% CI: 0.63-0.77) and specificity of 97.7% (95% CI: 0.93-0.98). For predicting complete necrosis, the LR-TR Nonviable category demonstrated a sensitivity of 77.3% (95% CI: 0.65-0.90) and specificity of 81.0% (95% CI: 0.75-0.87). Incorporating AFs significantly improved sensitivity for detecting incomplete necrosis (77.6% vs. 70.1%, p = 0.003) without compromising specificity (88.6% vs. 97.7%, p = 0.134). LI-RADS v2024 nonradiation TRA demonstrated good diagnostic performance in assessing HCC response after TACE. The use of AFs enhanced sensitivity for detecting residual viable tumors, supporting their clinical utility in equivocal cases. Question The diagnostic performance of the 2024 updated Liver Imaging Reporting and Data Systems (LI-RADS) Treatment Response Assessment (TRA), particularly the role of ancillary features (AFs), requires evaluation. Findings LI-RADS v2024 nonradiation TRA performed well in predicting histopathologic necrosis, with AFs significantly improving sensitivity for viability prediction. Clinical relevance The application of AFs is recommended to optimize post-TACE assessment and clinical decision-making for HCC management.

The diagnostic performance of the Liver Imaging Reporting and Data System (LI-RADS) version 2018 (v2018), its modifications modified LI-RADS (mLI-RADS) and revised LI-RADS (rLI-RADS), for diagnosing hepatocellular carcinoma (HCC) remains poorly understood and requires further validation. This multicentre study aimed to evaluate the diagnostic performance of three algorithms in diagnosing HCC. We included 1092 untreated patients at risk for developing HCC who underwent gadoxetic acid-enhanced MRI across three independent cohorts from January 2020 to December 2022. Two readers independently interpreted each hepatic lesion and recorded their imaging features. The readers' judgements regarding whether the lesion was HCC or not were also noted. Non-HCC cases were confirmed based on histologic and clinical follow-up data, while HCC cases were pathologically confirmed. Diagnostic performance metrics were compared using bootstrap resampling and generalised estimating equations. Additionally, the diagnostic odds ratio (DOR) was evaluated. Of 1313 lesions, 52.3% (687/1313) were diagnosed as HCC. For all hepatic lesions, mLI-RADS achieved higher sensitivity (82.9%) and accuracy (84.1%) than LI-RADS v2018 (sensitivity, 79.9%, p = 0.024; accuracy, 83.2%) and rLI-RADS (sensitivity, 81.3%; accuracy, 83.8%), while maintaining a similar positive predictive value (mLI-RADS, 86.2%; LI-RADS v2018, 86.9%; rLI-RADS, 86.9%). The DORs were 28.3 (95% CI: 21.1-38.0) for mLI-RADS, 27.8 (95% CI: 20.6-37.7) for LI-RADS v2018 and 26.0 (95% CI: 19.3-35.0) for rLI-RADS. The readers' judgement exhibited higher accuracy than that of three algorithms (87.7%: 83.2%-84.1%). For the diagnosis of HCC in an HBV-predominant cohort, mLI-RADS showed higher performance compared with LI-RADS v2018 and rLI-RADS. Reader judgement achieved higher accuracy than all algorithms, highlighting the role of clinical expertise. NCT06663904.

The Liver Imaging Reporting and Data System (LI-RADS) assessment is subject to inter-reader variability. The present study aimed to evaluate the impact of an artificial intelligence (AI) system on the accuracy and inter-reader agreement of LI-RADS classification based on contrast-enhanced magnetic resonance imaging among radiologists with varying experience levels. This single-center, multi-reader, multi-case retrospective study included 120 patients with 200 focal liver lesions who underwent abdominal contrast-enhanced magnetic resonance imaging examinations between June 2023 and May 2024. Five radiologists with different experience levels independently assessed LI-RADS classification and imaging features with and without AI assistance. The reference standard was established by consensus between two expert radiologists. Accuracy was used to measure the performance of AI systems and radiologists. Kappa or intraclass correlation coefficient was utilized to estimate inter-reader agreement. The LI-RADS categories were as follows: 33.5% of LR-3 (67/200), 29.0% of LR-4 (58/200), 33.5% of LR-5 (67/200), and 4.0% of LR-M (8/200) cases. The AI system significantly improved the overall accuracy of LI-RADS classification from 69.9 to 80.1% (p < 0.001), with the most notable improvement among junior radiologists from 65.7 to 79.7% (p < 0.001). Inter-reader agreement for LI-RADS classification was significantly higher with AI assistance compared to that without (weighted Cohen's kappa, 0.655 vs. 0.812, p < 0.001). The AI system also enhanced the accuracy and inter-reader agreement for imaging features, including non-rim arterial phase hyperenhancement, non-peripheral washout, and restricted diffusion. Additionally, inter-reader agreement for lesion size measurements improved, with intraclass correlation coefficient changing from 0.857 to 0.951 (p < 0.001). The AI system significantly increases accuracy and inter-reader agreement of LI-RADS 3/4/5/M classification, particularly benefiting junior radiologists.

Modified CEUS LI-RADS With Perfluorobutane in Patients at High-Risk for Hepatocellular Carcinoma: A Systematic Review and Network Meta-Analysis.

Background/Objectives: Hepatocellular carcinoma (HCC) is the most common primary malignant tumour of the liver. In a cirrhotic liver, each nodule larger than 10 mm demands further work-up using CT or MRI. The Liver Imaging Reporting and Data System (LI-RADS) is still based on visual assessment and measurements. The purpose of this study was to evaluate whether semi-automated quantification of visual LR-5 lesions is appropriate and can objectify HCC classification for personalized radiomic research. Methods: A total of 52 HCC patients (median age 67 years, 17% females, 83% males) from a retrospective data collection were evaluated visually and compared by the results using an oncology software with features of LI-RADS-based structured tumour evaluation and documentation, semi-automated tumour segmentation, and texture analysis. Results: Software-based evaluation of non-rim arterial-phase hyperenhancement (APHE) and non-peripheral washout, as well as the LI-RADS-score, showed no statistically significant differences compared with visual assessment (p = 0.2, 0.7, 0.17), with a consensus between a human reader and the software approach in 98% (APHE), 89% (washout), and 93% (threshold growth) of cases, respectively. The software provided automated LI-RADS classification, structured reporting, and quantitative features for HCC registries and radiomic research. Conclusions: The presented work may serve as an outlook for LI-RADS-based automated qualitative and quantitative evaluation. Future research may show if texture analysis can be used to foster personalized medical approaches in HCC.

The Liver Imaging Reporting and Data System (LI-RADS) has become an essential tool for standardizing the detection, diagnosis, and treatment response assessment of hepatocellular carcinoma (HCC) using contrast-enhanced CT and MRI. With the rising incidence of HCC, the use of local-regional therapies (LRT) has also expanded. To address the increasing complexity of post-treatment imaging interpretation, the LI-RADS Treatment Response Assessment (TRA) algorithm was developed to provide a structured and reproducible approach for evaluating tumor response following various forms of LRT. However, imaging features following radiation-based therapies-such as transarterial radioembolization and stereotactic body radiation therapy-often differ from those seen after thermal ablation and transarterial chemoembolization. Recognizing these differences, the 2024 LI-RADS TRA update introduces refinements to improve diagnostic accuracy in this context. This review outlines the evolving role of LI-RADS in post-treatment assessment, highlights imaging findings associated with different LRTs, and explores ongoing refinements aimed at optimizing its clinical utility. Finally, we discuss the need for prospective validation of the new algorithm to confirm its diagnostic performance and clinical impact.

Hepatocellular carcinoma (HCC) encompasses a wide array of histopathologic and genetic features that can be broadly categorized as proliferative or non-proliferative HCC to reflect tumor aggressiveness. However, accurately characterizing tumor behavior remains challenging due to the biologic heterogeneity of HCC and limited access to tissue samples. Currently, imaging is used for the diagnosis of HCC using the Liver Imaging Reporting and Data System (LI-RADS) without histologic confirmation in most cases. Emerging data suggest that imaging can provide clinical insight beyond diagnosis and predict patient outcomes by identifying key prognostic features, including those not yet integrated in LI-RADS. Certain CT and MRI features correlate with proliferative and non-proliferative HCC, and may yield prognostic information. Imaging findings such as tumor size, multifocality, and low apparent diffusion coefficient (ADC) have also been associated with microvascular invasion-an independent marker of poor prognosis. Growing data support the role of imaging in predicting treatment responsiveness before therapy initiation, which may influence the selection of a therapeutic agent. The radiologist can offer key clinical information by understanding and describing the prognostic and predictive features in HCC imaging. CRITICAL RELEVANCE STATEMENT: This study provides radiologists with a comprehensive summary of imaging findings associated with HCC prognosis, treatment responsiveness, and microvascular invasion. KEY POINTS: Hepatocellular carcinoma (HCC) is a heterogeneous cancer leading to challenges in diagnosis and management. Tumors can exhibit imaging features associated with proliferative or non-proliferative HCC. Key imaging features can help predict tumor aggressiveness and treatment responsiveness before the therapy is applied. Further research leveraging molecular data and applying machine learning models can improve our understanding of HCC prognostication.

LR-M is a category within the Liver Imaging Reporting and Data System (LI-RADS) that refers to liver observations that are probably or definitely malignant but are not specific to hepatocellular carcinoma (HCC). It includes etiologies such as atypical HCC, intrahepatic cholangiocarcinoma, combined hepatocellular cholangiocarcinoma and metastases. The primary aim of LR-M is to ensure a high sensitivity for detecting all hepatic malignancies while preserving a high specificity for HCC in LR-5. The imaging criteria for LR-M encompass a variety of targetoid and non-targetoid features. LR-M is often less well understood by end-users compared to more prominent categories such as LR-4 and LR-5, which have garnered greater attention and familiarity. In this review written from an end-user perspective, we examine the critical role that LR-M plays within LI-RADS for CT/MRI, the prevalence of HCC and non-HCC malignancies in LR-M, and demonstrate how LR-M can impact prognosis and treatment outcomes. We discuss the current imaging criteria for LR-M and the challenges faced by end-users in LI-RADS v2018 for CT/MRI. Finally, we explore future directions for improving the application of LR-M in clinical practice.

Photon-counting detector CT (PCD-CT) offers technical advantages over energy-integrating detector CT (EID-CT) for liver imaging. However, it is unclear whether these translate into clinical improvements regarding the classification of suspicious liver lesions using the Liver Imaging Reporting and Data System (LI-RADS). This study compared the intra- and intermodal agreement of EID-CT and PCD-CT with Magnetic resonance imaging (MRI) for liver lesion classification. This retrospective study included patients who underwent EID-CT or PCD-CT and MRI within 30days between 02/2023 and 01/2024. Three board-certified radiologists assessed LI-RADS classification and presence of LI-RADS major features. Fleiss' Kappa and intraclass correlation coefficients (ICC) were used to evaluate rater agreement. Sixty-eight lesions in 26 patients (mean age 65.0 ± 14.2years, 19 [73.1%] male) were analyzed. Intramodal inter-rater agreement for LI-RADS classification was 0.88 (0.62-0.88) for EID-CT, 0.90 (0.83-0.94) for PCD-CT, and 0.87 (0.81-0.91) for MRI. Agreement in PCD-CT was substantial for all LI-RADS major features, whereas in EID-CT only for washout. Intermodal agreement between CT and MRI ranged from 0.67 to 0.72. Final intermodal LI-RADS classification agreement was higher for PCD-CT (0.72-0.85) than EID-CT (0.52-0.64). PCD-CT demonstrated higher intermodal and intramodal agreement for LI-RADS classification and major features than EID-CT. Additionally, PCD-CT shows significantly higher intramodal and inter-rater agreement for LI-RADS classification and greater concordance with MRI compared to EID-CT, reaching substantial to almost perfect agreement. These results suggest a potential benefit of PCD-CT in the management and treatment decision-making of HCC.

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related deaths worldwide, necessitating accurate and early diagnosis to guide therapy, along with assessment of treatment response. Response assessment criteria have evolved from traditional morphologic approaches, such as WHO criteria and Response Evaluation Criteria in Solid Tumors (RECIST), to more recent methods focused on evaluating viable tumor burden, including European Association for Study of Liver (EASL) criteria, modified RECIST (mRECIST) and Liver Imaging Reporting and Data System (LI-RADS) Treatment Response (LI-TR) algorithm. This shift reflects the complex and evolving landscape of HCC treatment in the context of emerging systemic and locoregional therapies. Each of these criteria have their own nuanced strengths and limitations in capturing the detailed characteristics of HCC treatment and response assessment. The emergence of functional imaging techniques, including dual-energy CT, perfusion imaging, and rising use of radiomics, are enhancing the capabilities of response assessment. Growth in the realm of artificial intelligence and machine learning models provides an opportunity to refine the precision of response assessment by facilitating analysis of complex imaging data patterns. This review article provides a comprehensive overview of existing criteria, discusses functional and emerging imaging techniques, and outlines future directions for advancing HCC tumor response assessment.

Using Machine Learning to Improve the Contrast-Enhanced Ultrasound Liver Imaging Reporting and Data System Diagnosis of Hepatocellular Carcinoma in Indeterminate Liver Nodules.