Hepatocellular carcinoma with portal vein tumor thrombosis presents a significant therapeutic challenge due to its poor prognosis and limited treatment options. This review thoroughly examines diagnostic methods, including imaging techniques and classification systems such as the Japanese Vp and Cheng’s classifications, to aid in clinical decision-making. Treatment strategies encompass liver resection and liver transplantation, particularly living donor liver transplantation after successful downstaging, which have shown potential benefits in selected cases. Locoregional therapies, including hepatic arterial infusion chemotherapy, transarterial chemoembolization, transarterial radioembolization, and external beam radiation therapy, remain vital components of treatment. Recent advancements in systemic therapies, such as sorafenib, lenvatinib, and immune checkpoint inhibitors (e.g., atezolizumab plus bevacizumab) have demonstrated improvements in overall survival and progression-free survival. These developments underscore the importance of a multidisciplinary and personalized approach to improve outcomes for patients with hepatocellular carcinoma and portal vein tumor thrombosis.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Hepatocellular carcinoma with portal vein tumor thrombosis presents a significant therapeutic challenge due to its poor prognosis and limited treatment options. This review thoroughly examines diagnostic methods, including imaging techniques and classification systems such as the Japanese Vp and Cheng’s classifications, to aid in clinical decision-making. Treatment strategies encompass liver resection and liver transplantation, particularly living donor liver transplantation after successful downstaging, which have shown potential benefits in selected cases. Locoregional therapies, including hepatic arterial infusion chemotherapy, transarterial chemoembolization, transarterial radioembolization, and external beam radiation therapy, remain vital components of treatment. Recent advancements in systemic therapies, such as sorafenib, lenvatinib, and immune checkpoint inhibitors (e.g., atezolizumab plus bevacizumab) have demonstrated improvements in overall survival and progression-free survival. These developments underscore the importance of a multidisciplinary and personalized approach to improve outcomes for patients with hepatocellular carcinoma and portal vein tumor thrombosis.

Hepatocellular carcinoma with portal vein tumor thrombosis presents a significant therapeutic challenge due to its poor prognosis and limited treatment options. This review thoroughly examines diagnostic methods, including imaging techniques and classification systems such as the Japanese Vp and Cheng’s classifications, to aid in clinical decision-making. Treatment strategies encompass liver resection and liver transplantation, particularly living donor liver transplantation after successful downstaging, which have shown potential benefits in selected cases. Locoregional therapies, including hepatic arterial infusion chemotherapy, transarterial chemoembolization, transarterial radioembolization, and external beam radiation therapy, remain vital components of treatment. Recent advancements in systemic therapies, such as sorafenib, lenvatinib, and immune checkpoint inhibitors (e.g., atezolizumab plus bevacizumab) have demonstrated improvements in overall survival and progression-free survival. These developments underscore the importance of a multidisciplinary and personalized approach to improve outcomes for patients with hepatocellular carcinoma and portal vein tumor thrombosis.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.