Dataset Information

Diagnostic accuracy of tumor markers for hepatocellular carcinoma: a systematic review.

ABSTRACT: Background and aims The role of alphafetoprotein (AFP) in the diagnosis and surveillance of hepatocellular carcinoma (HCC) is getting smaller owing to the advances in imaging modalities. The aims of this study were to assess the diagnostic accuracy of tumor markers in small HCC and to find the optimal cutoff value of each tumor marker for efficient surveillance. Methods Studies in all languages were identified by searching MEDLINE from 1982 to 2002. Studies were included when they showed sensitivity and specificity for HCCs 5 cm or smaller and recruited only patients with chronic hepatitis or liver cirrhosis as control. We assessed diagnostic odds ratios (DORs) for the evaluation of diagnostic accuracy of tumor markers and positive likelihood ratios (LRs+) to find the optimal cutoff value. DORs and LRs+ were combined according to the random effect model. The summary receiver operating characteristics (ROC) curve was also assessed. Results Seventeen articles on three tumor markers-AFP, des-gamma-carboxyprothrombin (DCP), and Lens culinaris agglutinin-reactive fraction of AFP (AFP-L3)-were enrolled after full-text evaluation. AFP was inferior to DCP and AFP-L3 in both DOR (4.50 vs. 8.16 and 10.50) and area under the ROC curve (0.647 vs. 0.688 and 0.695). Optimal cutoff values that provide the best LR+ were 200 ng/ml for AFP, 40 mAU/ml for DCP, and 15% for AFP-L3. Conclusions Diagnostic accuracy of AFP in small HCC was substantially limited. Surveillance including other tumor markers with optimal cutoff value should be conducted to confirm the efficacy of the policy.

SUBMITTER: Tateishi R

PROVIDER: S-EPMC2716871 | biostudies-other | 2008 Mar

REPOSITORIES: biostudies-other

ACCESS DATA

Similar Datasets

Project description:Purpose This study aimed to assess the diagnostic performance of circulating tumor DNA (ctDNA) in hepatocellular carcinoma (HCC). Materials and Methods We enrolled all relevant studies published up to 5 January 2022. Three primary subgroups were investigated: qualitative or quantitative ctDNA analyses, combined alpha-fetoprotein (AFP), and ctDNA assay. In addition to the three primary subgroups, we also evaluated the diagnostic value of methylated SEPTIN9 (mSEPT9), which has been studied extensively in the diagnosis of hepatocellular carcinoma. After a search based on four primary databases, we used a bivariate linear mixed model to analyze the pooled sensitivity (SEN), specificity (SPE), positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR). We also plotted hierarchical summary receiver operating characteristics (HSROC) and utilized lambda as well as the area under the curve (AUC) to create summary receiver operating characteristic (SROC) curves to estimate the diagnostic value of ctDNA. Results A total of 59 qualified articles with 9,766 subjects were incorporated into our meta-analysis. The integrated SEN, SPE, and DOR in the qualitative studies were 0.50 (95% CI [0.43–0.56]), 0.90 (95% CI [0.86–0.93]), and 8.72 (95% CI [6.18–12.32]), respectively, yielding an AUC of 0.78 and lambda of 1.93 (95% CI [1.56–2.33]). For quantitative studies, the corresponding values were 0.69 (95% CI [0.63–0.74]), 0.84 (95% CI [0.77–0.89]), 11.88 (95% CI [7.78–18.12]), 0.81, and 2.32 (95% CI [1.96–2.69]), respectively. Six studies were included to evaluate the SETP9 methylation, which yielded an AUC of 0.86, a SEN of 0.80 (95% CI [0.71–0.87]), and a SPE of 0.77 (95% CI [0.68–0.85]). Likewise, ctDNA concentration yielded an AUC of 0.73, with a SEN of 0.63 (95% CI [0.56–0.70]) and a SPE of 0.86 (95% CI [0.74–0.93]). AFP combined with ctDNA assay resulted in an AUC of 0.89, with a SEN of 0.82 (95% CI [0.77–0.86]) and a SPE of 0.84 (95% CI [0.76–0.90]). Conclusion This study shows that circulating tumor DNA, particularly mSEPT9, shows promising diagnostic potential in HCC; however, it is not enough to diagnose HCC independently, and ctDNA combined with conventional assays such as AFP can effectively improve diagnostic performance.

Project description:BackgroundThe presence of microvascular invasion (MVI) is considered an independent prognostic factor associated with early recurrence and poor survival in hepatocellular carcinoma (HCC) patients after resection. Artificial intelligence (AI), mainly consisting of non-deep learning algorithms (NDLAs) and deep learning algorithms (DLAs), has been widely used for MVI prediction in medical imaging.AimTo assess the diagnostic accuracy of AI algorithms for non-invasive, preoperative prediction of MVI based on imaging data.MethodsOriginal studies reporting AI algorithms for non-invasive, preoperative prediction of MVI based on quantitative imaging data were identified in the databases PubMed, Embase, and Web of Science. The quality of the included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) scale. The pooled sensitivity, specificity, positive likelihood ratio (PLR), and negative likelihood ratio (NLR) were calculated using a random-effects model with 95% CIs. A summary receiver operating characteristic curve and the area under the curve (AUC) were generated to assess the diagnostic accuracy of the deep learning and non-deep learning models. In the non-deep learning group, we further performed meta-regression and subgroup analyses to identify the source of heterogeneity.ResultsData from 16 included studies with 4,759 cases were available for meta-analysis. Four studies on deep learning models, 12 studies on non-deep learning models, and two studies compared the efficiency of the two types. For predictive performance of deep learning models, the pooled sensitivity, specificity, PLR, NLR, and AUC values were 0.84 [0.75-0.90], 0.84 [0.77-0.89], 5.14 [3.53-7.48], 0.2 [0.12-0.31], and 0.90 [0.87-0.93]; and for non-deep learning models, they were 0.77 [0.71-0.82], 0.77 [0.73-0.80], 3.30 [2.83-3.84], 0.30 [0.24-0.38], and 0.82 [0.79-0.85], respectively. Subgroup analyses showed a significant difference between the single tumor subgroup and the multiple tumor subgroup in the pooled sensitivity, NLR, and AUC.ConclusionThis meta-analysis demonstrates the high diagnostic accuracy of non-deep learning and deep learning methods for MVI status prediction and their promising potential for clinical decision-making. Deep learning models perform better than non-deep learning models in terms of the accuracy of MVI prediction, methodology, and cost-effectiveness.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/display_record.php? RecordID=260891, ID:CRD42021260891.

Dataset Information

Diagnostic accuracy of tumor markers for hepatocellular carcinoma: a systematic review.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets