Role of accuracy measures in selecting hepatocellular carcinoma patients for liver transplantation A systematic review and meta-analysis
Frazao, L. P.; Pereira-Leal, J. B.; Duvoux, C.; Cardoso, J.
Show abstract
Structured AbstractO_ST_ABSImportanceC_ST_ABSMultiple criteria are used worldwide to select hepatocellular carcinoma (HCC) patients with a low risk of recurrence for liver transplantation (LT). However, it remains unclear which criteria are best for the LT-involved stakeholders, particularly in accurately identifying patients at high risk of recurrence. ObjectiveTo identify the most accurate criteria for selecting HCC patients for LT. Data SourcesIn June 2023, a systematic literature search was conducted in PubMed and CENTRAL to identify studies including LT selection criteria of HCC patients. Study SelectionThe selected studies had LT selection criteria based solely on pre-LT variables. They included a minority of down-staged patients, over 80% of deceased donors, presented recurrence-free survival curves (or equivalent) with the number of patients at risk at different times, and had a follow-up period of over 3 years. Data Extraction and SynthesisData was extracted from recurrence-free survival curves using a validated algorithm and subsequently used to calculate accuracy measures. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting guidelines were applied. Main Outcome(s) and Measures(s)Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy at 3- and 5-years post-LT. ResultsOf 815 records screened, only 17 met our study inclusion parameters, embodying 14 LT selection criteria. All LT criteria achieved an adjusted PPV (aPPV) over 80%, indicating the correct selection of at least 80% of low-risk HCC patients. However, the adjusted NPV (aNPV) was below 50% in most cases, indicating that these criteria cannot correctly identify patients with a true high risk of recurrence. This raises major ethical concerns regarding the models ability to exclude patients from LT. Since a perfect model is nonexistent, we created a ranking to account for the distinct concerns of all stakeholders in LT eligibility in the context of HCC. Conclusions and RelevanceThese results highlight the urgent need for new tools/models with improved NPV to select more patients amenable to LT who are currently excluded. Whether through refined existing criteria or newly developed criteria, emphasis should be placed on specificity and NPV as key performance indicators in the emerging era of transplant oncology. Key pointsO_LIQuestion: What are the best criteria to select patients with hepatocellular carcinoma for liver transplantation? C_LIO_LIFindings: An objective ranking of existing criteria using accuracy measures considers the concerns of different stakeholders such as patients, physicians, payers, and organ allocation organisms. None of the analyzed criteria are ideal in satisfying all stakeholders in the selection of patients with hepatocellular carcinoma for liver transplantation. Criteria are even worse in accurately identifying patients who will not benefit from transplantation. C_LIO_LIMeaning: There is a need to refine current criteria by focusing on specificity and negative predictive value as key performance indicators. C_LI
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Predicting Short-Term Mortality in Severe Cirrhosis: An Interpretable Machine Learning Model Integrating Routine Clinical Indicators 93%
- Trends in underlying causes of death in solid organ transplant recipients between 2010 and 2020: Using the CLASS method for determining specific causes of death 92%
- Survival benefits of cytoreductive nephrectomy in patients with metastatic renal cell carcinoma: evidence from a SEER-based retrospective cohort study 91%
Similar papers in this journal
- Predicting Post-Liver Transplant Outcomes in Patients with Acute-on-Chronic Liver Failure using Expert-Augmented Machine Learning 93%
- Machine learning-supported interpretation of kidney graft elementary lesions in combination with clinical data 92%
- Direct and indirect impact of the COVID-19 pandemic on the survival of kidney transplant recipients: a national observational study in France 90%
Similar papers in this journal
- Serum Autotaxin is a Prognostic Indicator of Liver-related Events in Patients with Non-alcoholic Fatty Liver Disease 91%
- Systematic Review of Large Language Models for Patient Care: Current Applications and Challenges 87%
- Computational Assessment of Memory Function in Kidney Transplant Recipients and Donors 85%
Similar papers in this journal
- FOXP3 mRNA profile prognostic of T cell mediated rejection and human kidney allograft survival 90%
- Perfusate metabolomics content and tubular transporters expression during kidney graft preservation by hypothermic machine perfusion 90%
- How hostile is prolonged brain death for donor organs in transplantation? A time course analysis using clinical samples 89%
Similar papers in this journal
- Opt-out policies capacity to increase organ donors is limited 92%
- Patterns of peritoneal dissemination and response to systemic chemotherapy in common and rare peritoneal tumors treated by cytoreductive surgery: Study protocol of a prospective, multi-center, observational study 90%
- Macrophage Therapy for Acute Liver Injury (MAIL): a Phase 1 Randomised, Open-Label, Dose-Escalation Study to Evaluate Safety, Tolerability, and Activity of Allogeneic Alternatively Activated Macrophages in Patients with Paracetamol-induced Acute Liver Injury. 89%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.