ArticleScientific reports2018
Development and Evaluation of Novel Statistical Methods in Urine Biomarker-Based Hepatocellular Carcinoma Screening.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- Novel urine cell-free DNA methylation markers for hepatocellular carcinoma.Scientific reports · 2023Article
- Urinary biomarkers for hepatocellular carcinoma: current knowledge for clinicians.Cancer cell international · 2023Review
- Role of three-dimensional printing and artificial intelligence in the management of hepatocellular carcinoma: Challenges and opportunities.World journal of gastrointestinal oncology · 2022Review
- Screening of Hub Genes in Hepatocellular Carcinoma Based on Network Analysis and Machine Learning.Computational and mathematical methods in medicine · 2022Article
- Computer-aided diagnosis of hepatocellular carcinoma fusing imaging and structured health data.Health information science and systems · 2021Article
- Artificial intelligence method to predict overall survival of hepatocellular carcinoma.Hepatology forum · 2021Article
- Novel miRNA signature for predicting the stage of hepatocellular carcinoma.Scientific reports · 2020Article
- Incremental Value of CSF Biomarkers in Clinically Diagnosed AD and Non-AD Dementia.Frontiers in neurology · 2020Article
- Impact of initial coagulation and fibrinolytic markers on mortality in patients with severe blunt trauma: a multicentre retrospective observational study.Scandinavian journal of trauma, resuscitation and emergency medicine · 2019Observational
- Liquid biopsies for hepatocellular carcinoma.Translational research : the journal of laboratory and clinical medicine · 2018Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Hepatocellular carcinoma is one of the fastest growing cancers in the US and has a low survival rate, partly due to difficulties in early detection. Because of HCC's high heterogeneity, it has been suggested that multiple biomarkers would be needed to develop a sensitive HCC screening test. This study applied random forest (RF), a machine learning technique, and proposed two novel models, fixed sequential (FS) and two-step (TS), for comparison with two commonly used statistical techniques, logistic regression (LR) and classification and regression trees (CART), in combining multiple urine DNA biomarkers for HCC screening using biomarker values obtained from 137 HCC and 431 non-HCC (224 hepatitis and 207 cirrhosis) subjects. The sensitivity, specificity, area under the receiver operating curve, and variability were estimated through repeated 10-fold cross-validation to compare the models' performances in accuracy and robustness. We show that RF and TS have higher accuracy and stability; specifically, they reach 90% specificity and 86%/87% sensitivity respectively along with 15% higher sensitivity and 10% higher specificity than LR in cross-validation. The potential of RF and TS to develop a panel of multiple biomarkers and the possibility for self-training, cloud-based models for HCC screening are discussed.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.