scholarly journals Editorial: hepatocellular carcinoma risk prediction models following DAA‐mediated SVR—more evidence needed

2021 ◽  
Vol 55 (1) ◽  
pp. 135-136
Author(s):  
Sarah Romero ◽  
Jacinta A. Holmes
Keyword(s):  
Hepatocellular Carcinoma ◽  
Risk Prediction ◽  
Prediction Models ◽  
Risk Prediction Models ◽  
Carcinoma Risk

Diagnostic accuracy of hepatocellular carcinoma risk prediction models during antiviral therapy in chronic hepatitis B patients

2021 ◽  
Vol 51 (11) ◽  
pp. 1170-1171
Author(s):  
Nobuharu Tamaki ◽  
Masayuki Kurosaki ◽  
Sakura Kirino ◽  
Namiki Izumi
Keyword(s):  
Hepatocellular Carcinoma ◽  
Chronic Hepatitis ◽  
Hepatitis B ◽  
Diagnostic Accuracy ◽  
Antiviral Therapy ◽  
Chronic Hepatitis B ◽  
Risk Prediction ◽  
Prediction Models ◽  
Risk Prediction Models ◽  
Carcinoma Risk

Validation of the Risk Prediction Models STATE-Score and START-Strategy to Guide TACE Treatment in Patients with Hepatocellular Carcinoma

2017 ◽  
Vol 40 (7) ◽  
pp. 1017-1025 ◽  
Author(s):  
Aline Mähringer-Kunz ◽  
Roman Kloeckner ◽  
Michael B. Pitton ◽  
Christoph Düber ◽  
Irene Schmidtmann ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Risk Prediction ◽  
Prediction Models ◽  
Risk Prediction Models ◽  
Tace Treatment ◽  
State Score

scholarly journals Systematic review: risk prediction models for recurrence of hepatocellular carcinoma after liver transplantation

2020 ◽  
Vol 33 (7) ◽  
pp. 697-712 ◽  
Author(s):  
Abdulahad Abdulrab Mohammed Al‐Ameri ◽  
Xuyong Wei ◽  
Xue Wen ◽  
Qiang Wei ◽  
Haijun Guo ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Systematic Review ◽  
Liver Transplantation ◽  
Risk Prediction ◽  
Prediction Models ◽  
Risk Prediction Models

scholarly journals Risk prediction models for hepatocellular carcinoma in different populations

2016 ◽  
Vol 28 (2) ◽  
pp. 150-160 ◽  
Author(s):  
Xiao Ma ◽  
◽  
Yang Yang ◽  
Hong Tu ◽  
Jing Gao ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Risk Prediction ◽  
Prediction Models ◽  
Risk Prediction Models ◽  
Different Populations

scholarly journals Reporting and Performance of Hepatocellular Carcinoma Risk Prediction Models: Based on TRIPOD Statement and Meta-Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Liuqing Yang ◽  
Qiang Wang ◽  
Tingting Cui ◽  
Jinxin Huang ◽  
Hui Jin
Keyword(s):  
Hepatocellular Carcinoma ◽  
Prediction Model ◽  
Risk Prediction ◽  
Missing Values ◽  
Prediction Models ◽  
Meta Analysis ◽  
External Validation ◽  
Discrimination Performance ◽  
Risk Prediction Models

Background. The performance of risk prediction models for hepatocellular carcinoma (HCC) in patients with chronic hepatitis B (CHB) was uncertain. The aim of the study was to critically evaluate the reports of transparent and external validation performances of these prediction models based on system review and meta-analysis. Methods. A systematic search of the Web of Science and PubMed was performed for studies published until October 17, 2020. The transparent reporting of a multivariable prediction model for the individual prognosis or diagnosis (TRIPOD) tool was used to critically evaluate the quality of external validation reports for six models (CU-HCC, GAG-HCC, PAGE-B, mPAGE-B, REACH-B, and mREACH-B). The area under the receiver operator characteristic curve (AUC) values was to estimate the pooled external validating performance based on meta-analysis. Subgroup analysis and metaregression were also performed to explore heterogeneity. Results. Our meta-analysis included 22 studies published between 2011 and 2020. The compliance of the included studies to TRIPOD ranged from 59% to 90% (median, 74%; interquartile range (IQR), 70%, 79%). The AUC values of the six models ranged from 0.715 to 0.778. In the antiviral therapy subgroups, the AUC values of mREACH-B, GAG-HCC, and mPAGE-B were 0.785, 0.760, and 0.778, respectively. In the cirrhosis subgroup, all models had poor discrimination performance (AUC < 0.7). Conclusions. A full report of calibration and handling of missing values would contribute to a greater improvement in the quality of external validation reports for CHB-related HCC risk prediction. It was necessary to develop a specific HCC risk prediction model for patients with cirrhosis.

scholarly journals Comparative performance of risk prediction models for hepatitis B-related hepatocellular carcinoma in the United States

2021 ◽  
Author(s):  
Hyun-seok Kim ◽  
Xian Yu ◽  
Jennifer Kramer ◽  
Aaron P. Thrift ◽  
Pete Richardson ◽  
...  
Keyword(s):  
United States ◽  
Hepatocellular Carcinoma ◽  
Hepatitis B ◽  
Risk Prediction ◽  
Prediction Models ◽  
The United States ◽  
Comparative Performance ◽  
Risk Prediction Models

scholarly journals Rare Germline Pathogenic Variants Identified by Multigene Panel Testing and the Risk of Aggressive Prostate Cancer

Cancers ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1495
Author(s):  
Tú Nguyen-Dumont ◽  
James G. Dowty ◽  
Robert J. MacInnis ◽  
Jason A. Steen ◽  
Moeen Riaz ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Risk Prediction ◽  
Prediction Models ◽  
Risk Prediction Models ◽  
Aggressive Prostate Cancer ◽  
Panel Testing ◽  
Pathogenic Variants ◽  
Increased Risk ◽  
Predisposition Genes ◽  
Rare Genetic Variants

While gene panel sequencing is becoming widely used for cancer risk prediction, its clinical utility with respect to predicting aggressive prostate cancer (PrCa) is limited by our current understanding of the genetic risk factors associated with predisposition to this potentially lethal disease phenotype. This study included 837 men diagnosed with aggressive PrCa and 7261 controls (unaffected men and men who did not meet criteria for aggressive PrCa). Rare germline pathogenic variants (including likely pathogenic variants) were identified by targeted sequencing of 26 known or putative cancer predisposition genes. We found that 85 (10%) men with aggressive PrCa and 265 (4%) controls carried a pathogenic variant (p < 0.0001). Aggressive PrCa odds ratios (ORs) were estimated using unconditional logistic regression. Increased risk of aggressive PrCa (OR (95% confidence interval)) was identified for pathogenic variants in BRCA2 (5.8 (2.7–12.4)), BRCA1 (5.5 (1.8–16.6)), and ATM (3.8 (1.6–9.1)). Our study provides further evidence that rare germline pathogenic variants in these genes are associated with increased risk of this aggressive, clinically relevant subset of PrCa. These rare genetic variants could be incorporated into risk prediction models to improve their precision to identify men at highest risk of aggressive prostate cancer and be used to identify men with newly diagnosed prostate cancer who require urgent treatment.

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