Notes

Single Vs . Double-Sided Concepts and also Odds.
The intraoperative course and the complication and mortality rates were similar among the three groups. The overall survival and disease-free survival were higher in the simultaneous TACE + PVE group than in the other two groups.

Simultaneous TACE and PVE is a safe and effective approach to increase FLR volume for patients with large HCC before major hepatectomy.
Simultaneous TACE and PVE is a safe and effective approach to increase FLR volume for patients with large HCC before major hepatectomy.
Patients with hepatitis B virus-associated acute-on-chronic liver failure (HBV-ACLF) present a complex and poor prognosis. Systemic inflammation plays an important role in its pathogenesis, and interleukin-6 (IL-6) as a pro-inflammatory cytokine is related with severe liver impairment and also plays a role in promoting liver regeneration. Whether serum IL-6 influences HBV-ACLF prognosis has not been studied.

To determine the impact of serum IL-6 on outcome of patients with HBV-ACLF.

We performed a retrospective study of 412 HBV-ACLF patients. The findings were analyzed with regard to mortality and the serum IL-6 level at baseline, as well as dynamic changes of serum IL-6 within 4 wk.

The serum IL-6 level was associated with mortality. Within 4 wk, deceased patients had significantly higher levels of IL-6 at baseline than surviving patients [17.9 (7.3-57.6)
10.4 (4.7-22.3),
= 0.011]. Patients with high IL-6 levels (> 11.8 pg/mL) had a higher mortality within 4 wk than those with low IL-6 levelFurthermore, a sustained high level or dynamic elevated level of serum IL-6 indicates a higher mortality.
A high level of serum IL-6 is an independent risk factor for mortality in patients with HBV-ACLF. Furthermore, a sustained high level or dynamic elevated level of serum IL-6 indicates a higher mortality.
Lenvatinib has become an indispensable part of treatment regimens for patients with advanced hepatocellular carcinoma (aHCC). Several recent real-world studies appear to have confirmed this; however, there are etiological differences. This necessitates further real-world studies of lenvatinib across diverse populations, such as in China.

To investigate the efficacy and safety of lenvatinib in a Chinese HCC patient population under real-world conditions.

This is a retrospective and multiregional study involving patients with aHCC receiving lenvatinib monotherapy. Efficacy was assessed using the Response Evaluation Criteria in Solid Tumors version 1.1. selleck chemical Baseline characteristics and adverse events (AEs) were recorded throughout the entire study.

In total, 54 HCC patients treated with lenvatinib monotherapy were included for final analysis. The objective response rate was 22% (
= 12) with a progression-free survival (PFS) of 168 d; however, AEs occurred in 92.8% of patients. Multivariate analysis showed ed.
Our findings confirm previous evidence from the phase III REFLECT study. The majority of patients in this Chinese sample were suffering from concomitant hepatitis B virus-related HCC. However, further analysis suggested that baseline characteristics, changes in serum biomarkers and gene sequencing may hold the key for predicting lenvatinib responses. Further large-scale prospective studies that incorporate more basic medical science measures should be conducted.
Despite advancements in operative technique and improvements in postoperative managements, postoperative pancreatic fistula (POPF) is a life-threatening complication following pancreatoduodenectomy (PD). There are some reports to predict POPF preoperatively or intraoperatively, but the accuracy of those is questionable. Artificial intelligence (AI) technology is being actively used in the medical field, but few studies have reported applying it to outcomes after PD.

To develop a risk prediction platform for POPF using an AI model.

Medical records were reviewed from 1769 patients at Samsung Medical Center who underwent PD from 2007 to 2016. A total of 38 variables were inserted into AI-driven algorithms. The algorithms tested to make the risk prediction platform were random forest (RF) and a neural network (NN) with or without recursive feature elimination (RFE). The median imputation method was used for missing values. The area under the curve (AUC) was calculated to examine the discriminative power of d be used to select patients who need especially intense therapy and to preoperatively establish an effective treatment strategy.
This study is the first to predict POPF with multiple risk factors using AI. This platform is reliable (AUC 0.74), so it could be used to select patients who need especially intense therapy and to preoperatively establish an effective treatment strategy.
Transarterial chemoembolization (TACE) is the first-line treatment for patients with unresectable liver cancer; however, TACE is associated with postembolization pain.

To analyze the risk factors for acute abdominal pain after TACE and establish a predictive model for postembolization pain.

From January 2018 to September 2018, all patients with liver cancer who underwent TACE at our hospital were included. General characteristics; clinical, imaging, and procedural data; and postembolization pain were analyzed. Postembolization pain was defined as acute moderate-to-severe abdominal pain within 24 h after TACE. Logistic regression and a classification and regression tree were used to develop a predictive model. Receiver operating characteristic curve analysis was used to examine the efficacy of the predictive model.

We analyzed 522 patients who underwent a total of 582 TACE procedures. Ninety-seven (16.70%) episodes of severe pain occurred. A predictive model built based on the dataset from classificatin pain.

Blood vessel invasion, TACE history, TACE with drug-eluting beads, and history of abdominal pain after TACE are predictors of acute moderate-to-severe pain. The predictive model may help medical staff to manage pain.
Blood vessel invasion, TACE history, TACE with drug-eluting beads, and history of abdominal pain after TACE are predictors of acute moderate-to-severe pain. The predictive model may help medical staff to manage pain.
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