Yuri Cho, Jin Woo Choi, Hoon Kwon, Kun Yung Kim, Byung Chan Lee, Hee Ho Chu, Dong Hyeon Lee, Han Ah Lee, Gyoung Min Kim, Jung Suk Oh, Dongho Hyun, In Joon Lee, Hyunchul Rhim, Research Committee of the Korean Liver Cancer Association
Clin Mol Hepatol 2023;29(3):521-541. Published online July 1, 2023
Transarterial chemoembolization (TACE) was introduced in 1977 with the administration of chemotherapeutic agent to gelatin sponge particles through the hepatic artery in patients with hepatocellular carcinoma (HCC) and was established as conventional TACE using Lipiodol in the 1980s. In the 2000s, drug-eluting beads were developed and applied clinically. Currently, TACE is a commonly used non-surgical treatment modality for patients with HCC who are unsuitable for curative treatment. Considering the vital role of TACE in the management of HCC, it is crucial to organize current knowledge and expert opinions regarding patient preparation, procedural techniques, and post-treatment care in TACE, which can enhance therapeutic efficacy and safety. A group of 12 experts in the fields of interventional radiology and hepatology, convened by the Research Committee of the Korean Liver Cancer Association (KLCA), has developed expert consensus-based practical recommendations in TACE. These recommendations have been endorsed by the Korean Society of Interventional Radiology and provide useful information and direction in performing TACE procedure as well as pre- and post- procedural patient care.
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Background/Aims Transarterial radioembolization (TARE) has shown promising results in treating advanced hepatocellular carcinoma (HCC) with portal vein tumor thrombosis (PVTT). However, whether TARE can provide superior or comparable outcomes to tyrosine kinase inhibitor (TKI) in patients with HCC and PVTT remains unclear. We compared the outcomes of TARE and TKI therapy in treatment-naïve patients with locally advanced HCC and segmental or lobar PVTT.
Methods This multicenter study included 216 patients initially treated with TARE (n=124) or TKI (sorafenib or lenvatinib; n=92) between 2011 and 2021. Baseline characteristics were balanced using propensity score matching (PSM) or inverse probability of treatment weighting (IPTW). The primary outcome was overall survival (OS). The secondary outcomes included progression-free survival (PFS) and objective response rate (ORR).
Results In the unmatched cohort, the median OS of the TARE and TKI groups were 28.2 and 7.2 months, respectively (p<0.001), and the TARE group experienced significantly and independently longer OS compared to the TKI group (adjusted hazard ratio=0.41, 95% confidence interval=0.28–0.60, p<0.001). Similar results were observed in the study cohorts balanced with IPTW (p=0.003) or PSM (p=0.004). Although PFS was comparable between the two groups, the TARE group showed a trend of prolonged PFS in a subpopulation of patients with Vp1 or Vp2 PVTT (p=0.052). In the matched cohorts, the ORR of the TARE group was 53.0–56.7%, whereas that of the TKI group was 12.3–15.0%.
Conclusions For patients with advanced HCC with segmental or lobar PVTT and well-preserved liver function, TARE may provide superior OS compared to sorafenib or lenvatinib.
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Background/Aims To develop an early prediction model for gestational diabetes mellitus (GDM) using machine learning and to evaluate whether the inclusion of nonalcoholic fatty liver disease (NAFLD)-associated variables increases the performance of model.
Methods This prospective cohort study evaluated pregnant women for NAFLD using ultrasound at 10–14 weeks and screened them for GDM at 24–28 weeks of gestation. The clinical variables before 14 weeks were used to develop prediction models for GDM (setting 1, conventional risk factors; setting 2, addition of new risk factors in recent guidelines; setting 3, addition of routine clinical variables; setting 4, addition of NALFD-associated variables, including the presence of NAFLD and laboratory results; and setting 5, top 11 variables identified from a stepwise variable selection method). The predictive models were constructed using machine learning methods, including logistic regression, random forest, support vector machine, and deep neural networks.
Results Among 1,443 women, 86 (6.0%) were diagnosed with GDM. The highest performing prediction model among settings 1–4 was setting 4, which included both clinical and NAFLD-associated variables (area under the receiver operating characteristic curve [AUC] 0.563–0.697 in settings 1–3 vs. 0.740–0.781 in setting 4). Setting 5, with top 11 variables (which included NAFLD and hepatic steatosis index), showed similar predictive power to setting 4 (AUC 0.719–0.819 in setting 5, P=not significant between settings 4 and 5).
Conclusions We developed an early prediction model for GDM using machine learning. The inclusion of NAFLDassociated variables significantly improved the performance of GDM prediction. (ClinicalTrials.gov Identifier: NCT02276144)
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