ACG Clinical Guideline: Focal Liver Lesions.

Focal liver lesions (FLLs) have become an increasingly common finding on abdominal imaging, especially asymptomatic and incidental liver lesions. Gastroenterologists and hepatologists often see these patients in consultation and make recommendations for management of multiple types of liver lesions, including hepatocellular adenoma, focal nodular hyperplasia, hemangioma, and hepatic cystic lesions including polycystic liver disease. Malignancy is important to consider in the differential diagnosis of FLLs, and healthcare providers must be familiar with the diagnosis and management of FLLs. This American College of Gastroenterology practice guideline uses the best evidence available to make diagnosis and management recommendations for the most common FLLs.

Prediction power of radiomics in early recurrence of hepatocellular carcinoma: A systematic review and meta-analysis.

BACKGROUND: Raiomics is an emerging auxiliary diagnostic tool, but there are still differences in whether it can be applied to predict early recurrence of hepatocellular carcinoma (HCC). The purpose of this meta-analysis was to systematically evaluate the predictive power of radiomics in the early recurrence (ER) of HCC. METHODS: Comprehensive studies on the application of radiomics to predict ER in HCC patients after hepatectomy or curative ablation were systematically screened in Embase, PubMed, and Web of Science. RESULTS: Ten studies which is involving a total of 1929 patients were reviewed. The overall estimates of radiomic models for sensitivity and specificity in predicting the ER of HCC were 0.79 (95% confidence interval [CI]: 0.68-0.87) and 0.83 (95% CI: 0.73-0.90), respectively. The area under the summary receiver operating characteristic curve (SROC) was 0.88 (95% CI: 0.85-0.91). CONCLUSIONS: The imaging method is a reliable method for diagnosing HCC. Radiomics, which is based on medical imaging, has excellent power in predicting the ER of HCC. With the help of radiomics, we can predict the recurrence of HCC after surgery more effectively and provide a useful reference for clinical practice.

Feasibility, safety, and efficacy of intraoperative magnetic resonance imaging-guided hepatectomy for small hepatocellular carcinoma: A retrospective study.

Advancements in diagnostic modalities, such as enhanced magnetic resonance imaging, provide increased opportunities for identifying small hepatocellular carcinoma that is undetectable on preoperative ultrasonography. Whether it is acceptable to leave these lesions untreated is uncertain. This study aimed to evaluate the safety and efficacy of intraoperative magnetic resonance imaging-guided hepatectomy using new navigation systems. This study was conducted between July 2019 and January 2023. We retrospectively studied the clinicopathological features and prognoses of patients with small hepatocellular carcinoma who underwent curative intraoperative magnetic resonance imaging-guided hepatectomy. We evaluated 23 patients (median age, 75 years), among whom 20 (87.0%) were males. Seven (30.4%) and 15 (65.2%) patients had liver cirrhosis and a history of hepatectomy, respectively. The median size of the target lesions was 9 mm, with a median distance of 6 mm from the liver surface. Despite being undetectable preoperatively on contrast-enhanced ultrasonography, all lesions were identified using intraoperative magnetic resonance imaging. Based on pathological findings, 76.0% of the lesions were malignant. The complete resection rate was 100%, and tumor-free margins were confirmed in 96.0% of the patients. Intraoperative magnetic resonance imaging-guided hepatectomy is safe and effective in identifying and resecting small hepatocellular carcinoma lesions that are undetectable on preoperative ultrasonography.

A novel radiomics approach for predicting TACE outcomes in hepatocellular carcinoma patients using deep learning for multi-organ segmentation.

Transarterial chemoembolization (TACE) represent the standard of therapy for non-operative hepatocellular carcinoma (HCC), while prediction of long term treatment outcomes is a complex and multifactorial task. In this study, we present a novel machine learning approach utilizing radiomics features from multiple organ volumes of interest (VOIs) to predict TACE outcomes for 252 HCC patients. Unlike conventional radiomics models requiring laborious manual segmentation limited to tumoral regions, our approach captures information comprehensively across various VOIs using a fully automated, pretrained deep learning model applied to pre-TACE CT images. Evaluation of radiomics random survival forest models against clinical ones using Cox proportional hazard demonstrated comparable performance in predicting overall survival. However, radiomics outperformed clinical models in predicting progression-free survival. Explainable analysis highlighted the significance of non-tumoral VOI features, with their cumulative importance superior to features from the largest liver tumor. The proposed approach overcomes the limitations of manual VOI segmentation, requires no radiologist input and highlight the clinical relevance of features beyond tumor regions. Our findings suggest the potential of this radiomics models in predicting TACE outcomes, with possible implications for other clinical scenarios.

[Spontaneous regression of hepatocellular carcinoma after severe acute respiratory syndrome coronavirus 2 vaccination:a case report].

An 86-year-old male patient with sustained virological response of chronic hepatitis type C was diagnosed with hepatocellular carcinoma (HCC) in hepatic segment 3. He was treated with transcatheter arterial chemoembolization (TACE) and radiation therapy because the tumor was located at the edge of the liver and umbilical portion of the portal vein. The value of alpha-fetoprotein (AFP) which is a serological tumor marker decreased, and the tumor size did not increase;however, another tumor was recognized at S3 of the liver 15 months post-TACE. The patient underwent a second TACE, and computed tomography revealed HCC recurrence at S3, S8/4, and S1 of the liver 6 months later. The patient refused to undergo another treatment, but the AFP and Des-γ-carboxy prothrombin values and the tumor size decreased 3 months postrecurrence. Two months after multiple recurrences of HCC, he received the third dose of messenger RNA-based vaccine for severe acute respiratory syndrome coronavirus 2. Tumor regression may occur after an immune-inflammatory response induced by messenger RNA-based vaccine.

Y-90 Radioembolization and PD-1 Inhibitor as Neoadjuvant Treatment in Hepatocellular Carcinoma.

This study showcases a comprehensive treatment protocol for high-risk hepatocellular carcinoma (HCC) patients, focusing on the combined use of Y-90 transarterial radioembolization (TARE) and Programmed Cell Death-1 (PD-1) inhibitors as neoadjuvant therapy. Highlighted through a case report, it offers a step-by-step reference for similar therapeutic interventions. A retrospective analysis was conducted on a patient who underwent hepatectomy following Y-90 TARE and PD-1 inhibitor treatment. Key demographic and clinical details were recorded at admission to guide therapy selection. Y-90 TARE suitability and dosage calculation were based on Technetium-99m (Tc-99m) macroaggregated albumin (MAA) perfusion mapping tests. Lesion coverage by Y-90 microspheres was confirmed through single photon emission computed tomography/computed tomography (SPECT/CT) fusion imaging, and adverse reactions and follow-up outcomes were meticulously documented. The patient, with a 7.2 cm HCC in the right hepatic lobe (T1bN0M0, BCLC A, CNLC Ib) and an initial alpha-fetoprotein (AFP) level of 66,840 ng/mL, opted for Y-90 TARE due to high recurrence risk and initial surgery refusal. The therapy's parameters, including the lung shunting fraction (LSF) and non-tumor ratio (TNR), were within therapeutic limits. A total of 1.36 GBq Y-90 was administered. At 1 month post-therapy, the tumor shrank to 6 cm with partial necrosis, and AFP levels dropped to 21,155 ng/mL, remaining stable for 3 months. After 3 months, PD-1 inhibitor treatment led to further tumor reduction to 4 cm and AFP decrease to 1.84 ng/mL. The patient then underwent hepatectomy; histopathology confirmed complete tumor necrosis. At 12 months post-surgery, no tumor recurrence or metastasis was observed in follow-up sessions. This protocol demonstrates the effective combination of Y-90 TARE and PD-1 inhibitor as a bridging strategy to surgery for HCC patients at high recurrence risk, providing a practical guide for implementing this approach.

ResTransUnet: An effective network combined with Transformer and U-Net for liver segmentation in CT scans.

Liver segmentation is a fundamental prerequisite for the diagnosis and surgical planning of hepatocellular carcinoma. Traditionally, the liver contour is drawn manually by radiologists using a slice-by-slice method. However, this process is time-consuming and error-prone, depending on the radiologist's experience. In this paper, we propose a new end-to-end automatic liver segmentation framework, named ResTransUNet, which exploits the transformer's ability to capture global context for remote interactions and spatial relationships, as well as the excellent performance of the original U-Net architecture. The main contribution of this paper lies in proposing a novel fusion network that combines Unet and Transformer architectures. In the encoding structure, a dual-path approach is utilized, where features are extracted separately using both convolutional neural networks (CNNs) and Transformer networks. Additionally, an effective feature enhancement unit is designed to transfer the global features extracted by the Transformer network to the CNN for feature enhancement. This model aims to address the drawbacks of traditional Unet-based methods, such as feature loss during encoding and poor capture of global features. Moreover, it avoids the disadvantages of pure Transformer models, which suffer from large parameter sizes and high computational complexity. The experimental results on the LiTS2017 dataset demonstrate remarkable performance for our proposed model, with Dice coefficients, volumetric overlap error (VOE), and relative volume difference (RVD) values for liver segmentation reaching 0.9535, 0.0804, and -0.0007, respectively. Furthermore, to further validate the model's generalization capability, we conducted tests on the 3Dircadb, Chaos, and Sliver07 datasets. The experimental results demonstrate that the proposed method outperforms other closely related models with higher liver segmentation accuracy. In addition, significant improvements can be achieved by applying our method when handling liver segmentation with small and discontinuous liver regions, as well as blurred liver boundaries. The code is available at the website: https://github.com/Jouiry/ResTransUNet.

A microscopic model of the dose distribution in hepatocellular carcinoma after selective internal radiation therapy.

The dosimetry evaluation for the selective internal radiation therapy is currently performed assuming a uniform activity distribution, which is in contrast with literature findings. A 2D microscopic model of the perfused liver was developed to evaluate the effect of two different 90Y microspheres distributions: i) homogeneous partitioning with the microspheres equally distributed in the perfused liver, and ii) tumor-clustered partitioning where the microspheres distribution is inferred from the patient specific images. METHODS: Two subjects diagnosed with liver cancer were included in this study. For each subject, abdominal CT scans acquired prior to the SIRT and post-treatment 90Y positron emission tomography were considered. Two microspheres partitionings were simulated namely homogeneous and tumor-clustered partitioning. The homogeneous and tumor-clustered partitionings were derived starting from CT images. The microspheres radiation is simulated by means of Russell's law. RESULTS: In homogenous simulations, the dose delivery is uniform in the whole liver while in the tumor-clustered simulations a heterogeneous distribution of the delivered dose is visible with higher values in the tumor regions. In addition, in the tumor-clustered simulation, the delivered dose is higher in the viable tumor than in the necrotic tumor, for all patients. In the tumor-clustered case, the dose delivered in the non-tumoral tissue (NTT) was considerably lower than in the perfused liver. CONCLUSIONS: The model proposed here represents a proof-of-concept for personalized dosimetry assessment based on preoperative CT images.

Radiomics model based on contrast-enhanced computed tomography imaging for early recurrence monitoring after radical resection of AFP-negative hepatocellular carcinoma.

BACKGROUND: Although radical surgical resection is the most effective treatment for hepatocellular carcinoma (HCC), the high rate of postoperative recurrence remains a major challenge, especially in patients with alpha-fetoprotein (AFP)-negative HCC who lack effective biomarkers for postoperative recurrence surveillance. Emerging radiomics can reveal subtle structural changes in tumors by analyzing preoperative contrast-enhanced computer tomography (CECT) imaging data and may provide new ways to predict early recurrence (recurrence within 2 years) in AFP-negative HCC. In this study, we propose to develop a radiomics model based on preoperative CECT to predict the risk of early recurrence after surgery in AFP-negative HCC. PATIENTS AND METHODS: Patients with AFP-negative HCC who underwent radical resection were included in this study. A computerized tool was used to extract radiomic features from the tumor region of interest (ROI), select the best radiographic features associated with patient's postoperative recurrence, and use them to construct the radiomics score (RadScore), which was then combined with clinical and follow-up information to comprehensively evaluate the reliability of the model. RESULTS: A total of 148 patients with AFP-negative HCC were enrolled in this study, and 1,977 radiographic features were extracted from CECT, 2 of which were the features most associated with recurrence in AFP-negative HCC. They had good predictive ability in both the training and validation cohorts, with an area under the ROC curve (AUC) of 0.709 and 0.764, respectively. Tumor number, microvascular invasion (MVI), AGPR and radiomic features were independent risk factors for early postoperative recurrence in patients with AFP-negative HCC. The AUCs of the integrated model in the training and validation cohorts were 0.793 and 0.791, respectively. The integrated model possessed the clinical value of predicting early postoperative recurrence in patients with AFP-negative HCC according to decision curve analysis, which allowed the classification of patients into subgroups of high-risk and low-risk for early recurrence. CONCLUSION: The nomogram constructed by combining clinical and imaging features has favorable performance in predicting the probability of early postoperative recurrence in AFP-negative HCC patients, which can help optimize the therapeutic decision-making and prognostic assessment of AFP-negative HCC patients.

Comparison of the Ability of Gadobenate Dimeglumine and Gadolinium Ethoxybenzyl Dimeglumine to Display the major Features for Noninvasively Diagnosing Hepatocellular Carcinoma According to the LI-RADS 2018v.

OBJECTIVE: To compare the ability of gadolinium ethoxybenzyl dimeglumine (Gd-EOB-DTPA) and gadobenate dimeglumine (Gd-BOPTA) to display the 3 major features recommended by the Liver Imaging Reporting and Data System (LI-RADS 2018v) for diagnosing hepatocellular carcinoma (HCC). MATERIALS AND METHODS: In this retrospective study, we included 98 HCC lesions that were scanned with either Gd-EOB-DTPA-MR or Gd-BOPTA-M.For each lesion, we collected multiple variables, including size and enhancement pattern in the arterial phase (AP), portal venous phase (PVP), transitional phase (TP), delayed phase (DP), and hepatobiliary phase (HBP). The lesion-to-liver contrast (LLC) was measured and calculated for each phase and then compared between the 2 contrast agents. A P value < .05 was considered statistically significant. The display efficiency of the LLC between Gd-BOPTA and Gd-EOB-DTPA for HCC features was evaluated by receiver operating characteristic (ROC) curve analysis. RESULTS: Between Gd-BOPTA and Gd-EOB-DTPA, significant differences were observed regarding the display efficiency for capsule enhancement and the LLC in the AP/PVP/DP (P < .05), but there was no significant difference regarding the LLC in the TP/HBP. Both Gd-BOPTA and Gd-EOB-DTPA had good display efficiency in each phase (AUCmin > 0.750). When conducting a total evaluation of the combined data across the 5 phases, the display efficiency was excellent (AUC > 0.950). CONCLUSION: Gd-BOPTA and Gd-EOB-DTPA are liver-specific contrast agents widely used in clinical practice. They have their own characteristics in displaying the 3 main signs of HCC. For accurate noninvasive diagnosis, the choice of agent should be made according to the specific situation.

A clinical-radiomic-pathomic model for prognosis prediction in patients with hepatocellular carcinoma after radical resection.

PURPOSE: Radical surgery, the first-line treatment for patients with hepatocellular cancer (HCC), faces the dilemma of high early recurrence rates and the inability to predict effectively. We aim to develop and validate a multimodal model combining clinical, radiomics, and pathomics features to predict the risk of early recurrence. MATERIALS AND METHODS: We recruited HCC patients who underwent radical surgery and collected their preoperative clinical information, enhanced computed tomography (CT) images, and whole slide images (WSI) of hematoxylin and eosin (H & E) stained biopsy sections. After feature screening analysis, independent clinical, radiomics, and pathomics features closely associated with early recurrence were identified. Next, we built 16 models using four combination data composed of three type features, four machine learning algorithms, and 5-fold cross-validation to assess the performance and predictive power of the comparative models. RESULTS: Between January 2016 and December 2020, we recruited 107 HCC patients, of whom 45.8% (49/107) experienced early recurrence. After analysis, we identified two clinical features, two radiomics features, and three pathomics features associated with early recurrence. Multimodal machine learning models showed better predictive performance than bimodal models. Moreover, the SVM algorithm showed the best prediction results among the multimodal models. The average area under the curve (AUC), accuracy (ACC), sensitivity, and specificity were 0.863, 0.784, 0.731, and 0.826, respectively. Finally, we constructed a comprehensive nomogram using clinical features, a radiomics score and a pathomics score to provide a reference for predicting the risk of early recurrence. CONCLUSIONS: The multimodal models can be used as a primary tool for oncologists to predict the risk of early recurrence after radical HCC surgery, which will help optimize and personalize treatment strategies.

Generation of virtual monoenergetic images at 40 keV of the upper abdomen and image quality evaluation based on generative adversarial networks.

BACKGROUND: Abdominal CT scans are vital for diagnosing abdominal diseases but have limitations in tissue analysis and soft tissue detection. Dual-energy CT (DECT) can improve these issues by offering low keV virtual monoenergetic images (VMI), enhancing lesion detection and tissue characterization. However, its cost limits widespread use. PURPOSE: To develop a model that converts conventional images (CI) into generative virtual monoenergetic images at 40 keV (Gen-VMI40keV) of the upper abdomen CT scan. METHODS: Totally 444 patients who underwent upper abdominal spectral contrast-enhanced CT were enrolled and assigned to the training and validation datasets (7:3). Then, 40-keV portal-vein virtual monoenergetic (VMI40keV) and CI, generated from spectral CT scans, served as target and source images. These images were employed to build and train a CI-VMI40keV model. Indexes such as Mean Absolute Error (MAE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity (SSIM) were utilized to determine the best generator mode. An additional 198 cases were divided into three test groups, including Group 1 (58 cases with visible abnormalities), Group 2 (40 cases with hepatocellular carcinoma [HCC]) and Group 3 (100 cases from a publicly available HCC dataset). Both subjective and objective evaluations were performed. Comparisons, correlation analyses and Bland-Altman plot analyses were performed. RESULTS: The 192nd iteration produced the best generator mode (lower MAE and highest PSNR and SSIM). In the Test groups (1 and 2), both VMI40keV and Gen-VMI40keV significantly improved CT values, as well as SNR and CNR, for all organs compared to CI. Significant positive correlations for objective indexes were found between Gen-VMI40keV and VMI40keV in various organs and lesions. Bland-Altman analysis showed that the differences between both imaging types mostly fell within the 95% confidence interval. Pearson's and Spearman's correlation coefficients for objective scores between Gen-VMI40keV and VMI40keV in Groups 1 and 2 ranged from 0.645 to 0.980. In Group 3, Gen-VMI40keV yielded significantly higher CT values for HCC (220.5HU vs. 109.1HU) and liver (220.0HU vs. 112.8HU) compared to CI (p < 0.01). The CNR for HCC/liver was also significantly higher in Gen-VMI40keV (2.0 vs. 1.2) than in CI (p < 0.01). Additionally, Gen-VMI40keV was subjectively evaluated to have a higher image quality compared to CI. CONCLUSION: CI-VMI40keV model can generate Gen-VMI40keV from conventional CT scan, closely resembling VMI40keV.

[Multiparametric MRI in hepatocellular carcinoma, part 2 : Diffusion-weighted imaging in the primary diagnostics and treatment monitoring]. / Multiparametrische MRT-Bildgebung beim hepatozellulären Karzinom, Teil 2 : Diffusionsbildgebung in der Primärdiagnostik und Therapieverlaufskontrolle.

In addition to morphology and tissue perfusion, diffusion-weighted imaging (DWI) is the third pillar of multiparametric diagnostics in oncology. Due to the strong correlation between the apparent diffusion coefficient (ADC) and cell count in hepatocellular carcinoma (HCC), it can be used as a surrogate marker for tumor cell quantity. Therefore, ADC effectively reflects the effects of cytoreductive treatment, such as transarterial chemoembolization (TACE) and systemic chemotherapy and becomes an important clinical marker for treatment response. The DWI should remain an integral part of a magnetic resonance imaging (MRI) protocol in primary HCC diagnostics and treatment monitoring but is of secondary clinical importance compared to contrast-enhanced MRI perfusion sequences and the use of liver-specific contrast agents. For the future, standardization of DWI sequences for better comparability of various study protocols would be desirable.

The Combination of Gd-EOB-DTPA Enhanced T1 Mapping with Apparent Diffusion Coefficient could Improve the Diagnostic Efficacy of Hepatocellular Carcinoma Grading.

BACKGROUND: Accurately predicting the hepatocellular carcinoma (HCC) grade may facilitate the rational selection of treatment strategies. The diagnostic efficacy of the combination of Gadolinium ethoxybenzy diethylenetriamine pentaacetic (Gd-EOB-DTPA) enhancement T1 mapping and apparent diffusion coefficient (ADC) values in predicting HCC grade needs further validation. OBJECTIVES: This study aimed to assess the capacity of Gd-EOB-DTPA-enhanced T1 mapping and ADC values, both individually and in combination, to discriminate between different grades of HCC. MATERIALS AND METHODS: From July 2017 to February 2020, 96 patients (male, 83; mean age, 53.67 years; age range, 29-71 years) clinically diagnosed with HCC were included in the present study. All patients underwent Gd-EOB-DTPA-enhanced magnetic resonance imaging (MRI, including T1 mapping sequence) before surgery or biopsy. All the patients were categorized into 3 groups according to the pathological results (including 24 cases of well-differentiated HCCs, 59 cases of moderately differentiated HCCs, 13 cases of and poorly differentiated HCCs). The mean Gd-EOB-DTPA enhanced T1 values (ΔT1=[(T1pre-T1post)/T1pre]×100%) and ADC values between different grading groups of HCC were calculated and compared. The area under the characteristics curve (AUC), the diagnostic threshold, sensitivity, and specificity of ΔT1 and ADC for differential diagnosis were analyzed. RESULTS: Mean ΔT1 was 58% for well-differentiated HCCs, 50% for moderately-differentiated HCCs, and 43% for poorly-differentiated HCCs. ΔT1 showed statistical differences between the groups (P<0.001). The mean ADC values of the 3 groups were 1.11×10-3 mm2/s, 0.91×10-3 mm2/s, and 0.80×10-3mm2/s, respectively. ADC showed statistical differences between the groups (P<0.001). In discriminating well- differentiated group from the moderately differentiated group, the AUC of ΔT1 was 0.751 (95% CI: 0.642, 0.859), the AUC of ADC was 0.782 (95% CI: 0.671, 0.894), the AUC of combined model was 0.811 (95% CI: 0.709, 0.914). In discriminating the poorly differentiated group from the moderately differentiated group, the AUC of ΔT1 was 0.768 (95% CI: 0.634, 0.902), the AUC of ADC was 0.754 (95% CI: 0.603, 0.904), and the AUC of the combined model was 0.841 (95% CI: 0.729, 0.953). CONCLUSION: Gd-EOB-DTPA enhanced T1 mapping, and ADC values have complementary effects on the sensitivity and specificity for identifying different HCC grades. A combined model of Gd-EOB-DTPA-enhanced MRI T1 mapping and ADC values could improve diagnostic performance for predicting HCC grades.

Effectiveness and safety of computed tomography-guided high-dose-rate brachytherapy in treating recurrent hepatocellular carcinoma not amenable to repeated resection or radiofrequency ablation.

PURPOSE: To assess the efficacy and safety of computed tomography (CT)-guided high-dose-rate HDR) brachytherapy in treating recurrent hepatocellular carcinoma (HCC) not amenable to repeated resection or radiofrequency ablation. MATERIALS AND METHODS: From January 2010 to January 2022, 38 patients (mean age, 70.1 years; SD ± 9.0 years) with 79 nodular and four diffuse intrahepatic HCC recurrences not amenable to repeated resection or radiofrequency ablation underwent CT-guided HDR brachytheapy in our department. Tumor response was evaluated by cross-sectional imaging 6 weeks after CT-guided HDR brachytherapy and every 3 months thereafter. Local tumor control (LTC), progression-free survival (PFS) and overall survival (OS) were assessed using Kaplan-Meier curves (KPCs). Severity of procedure-related complications (PRCs) was classified as recommended by the Society of Interventional Radiology (SIR). RESULTS: Patients were available for MRI evaluation for a mean follow-up of 33.1 months (SD, ±21.6 mm, range 4-86 months; median 29 months). Patients had a mean of 2.3 (SD, ±1.4) intrahepatic tumors. Mean tumor diameter was 43.2 mm (SD, ±19.6 mm). 13 of 38 (34.2%) patients showed local tumor progression after CT-guided HDR brachytherapy. Mean LTC was 29.3 months (SD, ±22.1). Distant tumor progression was seen in 12 patients (31.6%). The mean PFS was 20.8 months (SD, ±22.1). Estimated 1-, 3-, and 5-year PFS rates were 65.1%, 35.1% and 22.5%, respectively. 13 patients died during the follow-up period. Mean OS was 35.4 months (SD, ±21.7). Estimated 1-, 3-, and 5-year OS rates were 91.5%, 77.4% and 58.0%, respectively. SIR grade 1 complications were recorded in 8.6% (5/38) and SIR grade 2 complications in 3.4% (2/58) of interventions. CONCLUSION: CT-guided HDR brachytherapy is a safe and efficient therapeutic option for managing large or critically located HCC recurrences in the remaining liver after prior hepatic resection.

Liver Imaging Reporting and Data System Contrast-Enhanced US Nonradiation Treatment Response Assessment Version 2024.

The American College of Radiology Liver Imaging Reporting and Data System (LI-RADS) standardizes the imaging technique, reporting lexicon, disease categorization, and management for patients with or at risk for hepatocellular carcinoma (HCC). LI-RADS encompasses HCC surveillance with US; HCC diagnosis with CT, MRI, or contrast-enhanced US (CEUS); and treatment response assessment (TRA) with CT or MRI. LI-RADS was recently expanded to include CEUS TRA after nonradiation locoregional therapy or surgical resection. This report provides an overview of LI-RADS CEUS Nonradiation TRA v2024, including a lexicon of imaging findings, techniques, and imaging criteria for posttreatment tumor viability assessment. LI-RADS CEUS Nonradiation TRA v2024 takes into consideration differences in the CEUS appearance of viable tumor and posttreatment changes within and in close proximity to a treated lesion. Due to the high sensitivity of CEUS to vascular flow, posttreatment reactive changes commonly manifest as areas of abnormal perilesional enhancement without washout, especially in the first 3 months after treatment. To improve the accuracy of CEUS for nonradiation TRA, different diagnostic criteria are used to evaluate tumor viability within and outside of the treated lesion margin. Broader criteria for intralesional enhancement increase sensitivity for tumor viability detection. Stricter criteria for perilesional enhancement limit miscategorization of posttreatment reactive changes as viable tumor. Finally, the TRA algorithm reconciles intralesional and perilesional tumor viability assessment and assigns a single LI-RADS treatment response (LR-TR) category: LR-TR nonviable, LR-TR equivocal, or LR-TR viable.

Prognostication of Hepatocellular Carcinoma Using Artificial Intelligence.

Hepatocellular carcinoma (HCC) is a biologically heterogeneous tumor characterized by varying degrees of aggressiveness. The current treatment strategy for HCC is predominantly determined by the overall tumor burden, and does not address the diverse prognoses of patients with HCC owing to its heterogeneity. Therefore, the prognostication of HCC using imaging data is crucial for optimizing patient management. Although some radiologic features have been demonstrated to be indicative of the biologic behavior of HCC, traditional radiologic methods for HCC prognostication are based on visually-assessed prognostic findings, and are limited by subjectivity and inter-observer variability. Consequently, artificial intelligence has emerged as a promising method for image-based prognostication of HCC. Unlike traditional radiologic image analysis, artificial intelligence based on radiomics or deep learning utilizes numerous image-derived quantitative features, potentially offering an objective, detailed, and comprehensive analysis of the tumor phenotypes. Artificial intelligence, particularly radiomics has displayed potential in a variety of applications, including the prediction of microvascular invasion, recurrence risk after locoregional treatment, and response to systemic therapy. This review highlights the potential value of artificial intelligence in the prognostication of HCC as well as its limitations and future prospects.