ArticleAmerican journal of translational research2022
Incidence trends and predictive model of hepatic malignant tumors in children: a population-based study.
Article in American journal of translational research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed, 4 citations in OpenAlex.
- Overexpression of ONECUT1 suppresses hepatoblastoma progression via modulating tumor cell growth and tumor microenvironment.Cell & bioscience · 2026Article
- Global trends and regional differences of hepatoblastoma: a systematic analysis and predictions to 2035 based on the global burden of disease study 2021.BMC public health · 2025Article
- Congenital focal nodular hyperplasia-like lesion mimicking hepatoblastoma: anecdotal but plausible.World journal of pediatric surgery · 2025Article
- Evolving Global Etiology of Hepatocellular Carcinoma (HCC): Insights and Trends for 2024.Journal of clinical and experimental hepatologyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo analyze the incidence trend and establish a model to predict the prognosis of hepatic malignant tumors in children (CHMTs).
methodsWe analyzed the incidence data of CHMTs from 1975 to 2018 from the Surveillance, Epidemiology, and End Results (SEER) database, and evaluated the incidence trends based on different demographic and pathological features. We also analyzed clinicopathologic data from 2000 to 2018 from the SEER database. Univariate and multivariate Cox regression analyses were performed to explore prognostic factors related to overall survival (OS). Then, we established nomograms based on independent predictors and verified them using receiver operating characteristic curves, calibration plots, and decision curve analysis plots.
resultsThe incidence of CHMTs increased significantly, from 0.1 per 100,000 in 1975 to 0.4 per 100,000 in 2018. Incidences among different races and genders were increasing and converging. The incidence of hepatoblastoma (HB) increased, while that of hepatocellular carcinoma (HCC) was relatively stable. The 1-, 3-, 5-, and 10-year OS rates were 86.2%, 77.5%, 74.2%, and 70.2%, respectively. Being Spanish-Hispanic-Latino, HB, surgery, and systemic therapy were independent predictors of longer OS, whereas regional and distant stages were independent predictors of shorter OS. Nomograms with good predictive ability and clinical utility were established to evaluate the prognosis of children with HB or HCC.
conclusionThe incidence of CHMTs is increasing, especially for HB and in younger children. This study identified independent predictors and developed nomograms that could provide a personalized and accurate prognosis for CHMTs.
Indexed as
Identifiers
36398244PMC9641436W4309413372What 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.