Liver transplantation versus watchful waiting in hepatocellular carcinoma patients with complete response to bridging therapy - a retrospective observational study

Deep Learning-Adjusted Monitoring of In-Hospital Mortality after Liver Transplantation

Background: Surgeries represent a mainstay of medical care globally. Patterns of complications are frequently recognized late and place a considerable burden on health care systems. The aim was to develop and test the first deep learning-adjusted CUSUM program (DL-CUSUM) to predict and monitor in-hospital mortality in real time after liver transplantation. Methods: Data from 1066 individuals with 66,092 preoperatively available data point variables from 2004 to 2019 were included. DL-CUSUM is an application to predict in-hospital mortality. The area under the curve for risk adjustment with Model of End-stage Liver Disease (D-MELD), Balance of Risk (BAR) score, and deep learning (DL), as well as the ARL (average run length) and control limit (CL) for an in-control process over 5 years, were calculated. Results: D-MELD AUC was 0.618, BAR AUC was 0.648 and DL model AUC was 0.857. CL with BAR adjustment was 2.3 with an ARL of 326.31. D-MELD reached an ARL of 303.29 with a CL of 2.4. DL prediction resulted in a CL of 1.8 to reach an ARL of 332.67. Conclusions: This work introduces the first use of an automated DL-CUSUM system to monitor postoperative in-hospital mortality after liver transplantation. It allows for the real-time risk-adjusted monitoring of process quality.

[Liver transplantation for treatment of nonresectable primary and secondary liver malignancies : Hepatocellular and cholangiocellular carcinomas and colorectal liver metastases]

BACKGROUND

With the increasing efficacy of systemic therapy, liver transplantation plays an important role not only for hepatocellular carcinoma (HCC) but also for nonresectable intrahepatic cholangiocellular carcinoma (iCC), perihilar cholangiocellular carcinoma (phCC) and colorectal liver metastases (CRLM).

AIM

To review the current state of knowledge regarding the indications, patient selection and expected outcomes of liver transplantation for HCC, iCC, phCC and CRLM.

RESULTS

When combined with neoadjuvant locoregional therapy (LRT) and/or systemic therapy, patients with nonresectable HCC, iCC, pCC and CRLM confined to the liver can be successfully transplanted with 5‑year survival rates exceeding 65%. The key to success is strict patient selection, which includes oncogenetic (e.g., BRAFV600E mutation status) and clinical criteria indicative of individual tumor biology (tumor markers: alpha-fetoprotein, AFP/carbohydrate antigen 19‑9, CA19-9/carcinoembryonic antigen, CEA, stable response to neoadjuvant therapy) in addition to morphometric criteria.

CONCLUSION

Liver transplantation offers the possibility of curative treatment even for nonresectable hepatic malignancies. A major limitation of this treatment is the lack of donor organs. Crucial for success is patient selection based on individual tumor biology.

Changes of long-term survival of resection and liver transplantation in hepatocellular carcinoma throughout the years: A meta-analysis

BACKGROUND

Hepatocellular Carcinoma (HCC) still is one of the most detrimental malignant diseases in the world. As two curative surgical therapies exist, the discussion whether to opt for liver resection (LR) or transplantation (LT) is ongoing, especially as novel techniques to improve outcome have emerged for both. The aim of the study was to investigate how the utilization and outcome of the respective modalities changed through time.

METHODS

We searched Medline and PubMed for relevant publications comparing LT and LR in HCC patients during the time period from 1990 to 2022, prior to March 31, 2023. A total of 63 studies involving 19,804 patients - of whom 8178 patients received a liver graft and 11,626 underwent partial hepatectomy - were included in this meta-analysis.

RESULTS

LT is associated with significantly better 5-year overall survival (OS) (64.83%) and recurrence-free survival (RFS) (70.20%) than LR (OS: 50.83%, OR: 1.79, p < 0.001; RFS: 34.46%, OR: 5.32, p < 0.001). However, these differences are not as evident in short-term intervals. Older cohorts showed comparable disparities between the outcome of the respective modalities, as did newer cohorts after 2005. This might be due to the similar improvement in survival rates that were observed for both, LT (15-23%) and LR (12-20%) during the last 30 years.

CONCLUSION

LT still outperforms LR in the therapy of HCC in terms of long-term survival rates. Yet, LR outcome has remarkably improved which is of major importance in reference to the well-known limitations that occur in LT.

Frequent Follow-Up of Delisted Liver Transplant Candidates Is Necessary: An Observational Study about Characteristics and Outcomes of Delisted Liver Transplant Candidates

This observational study focuses on the characteristics and survival of patients taken off of the liver transplant waiting list. Assessment of post-delisting survival and a frequent follow-up of patients after delisting are important keys to improve the survival rate of patients with liver failure after being delisted. Within this study, delisted liver transplant candidates were divided into the following groups: (1) "too good" (54%) or (2) "too sick" (22%) for transplantation, (3) adherence issues (12%) or (4) therapy goal changed (11%). The 5-year survival after delisting within these groups was 84%, 9%, 50%, and 68%, respectively. Less than 3% of the delisted patients had to be relisted again. The clinical expert decision of the multidisciplinary transplant team was sufficiently accurate to differentiate between patients requiring liver transplantation and those who were delisted after a stable recovery of liver function. The assessment of post-delisting survival may serve as a complementary metric to assess differences in center practices and to estimate cumulative post-delisting mortality risk.

Combined DNA Methylation and Transcriptomic Assessments to Determine a Prognostic Model for PD-1-Negative Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) has the highest incidence and mortality of any malignancy in the world. Immunotherapy has been a major breakthrough for HCC treatment, but immune checkpoint inhibitors (ICIs) are effective in only a small percentage of HCC patients. In the present study, we screened programmed cell death protein 1 (PD-1) -negative HCC samples, which are frequently resistant to ICIs, and identified their methylation and transcription characteristics through the assessment of differential gene methylation and gene expression. We also screened for potential targeted therapeutic drugs using the DrugBank database. Finally, we used a LASSO (least absolute shrinkage and selection operator) regression analysis to construct a prognostic model based on three differentially methylated and expressed genes (DMEGs). The results showed that ESTIMATE (Estimation of Stromal and Immune Cells in Malignant Tumors using Expression Data) scores for the tumor samples were significantly lower compared to normal sample ESTIMATE scores. In addition, we identified 31 DMEGs that were able to distinguish PD-1-negative samples from normal samples. A functional enrichment analysis showed that these genes were involved in a variety of tumor-related pathways and immune-related pathways, and the DrugBank screening identified potential therapeutic drugs. Finally, the prognostic model based on three DMEGs (UBD, CD5L, and CD213A2) demonstrated good predictive power for HCC prognosis and was verified using an independent cohort. The present study demonstrated the methylation characteristics of PD-1-negative HCC samples, identified several potential therapeutic drugs, and proposed a prognostic model based on UBD, CD5L, and CD213A2 methylation expression. In conclusion, this work provides an in-depth understanding of methylation in HCC samples that are not sensitive to ICIs.