ArticleToxicology reports2019
Quantifying association between liver tumor incidence and early-stage liver weight increase - An NTP data analysis.
Article in Toxicology reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 3 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
3 citing papers in PubMed, 6 citations in OpenAlex.
- Transferability and Reproducibility of the HepaRG CometChip Assay.Environmental and molecular mutagenesis · 2025Article
- The association between histopathologic effects and liver weight changes induced in mice and rats by chemical exposures: an analysis of the data from Toxicity Reference Database (ToxRefDB).Toxicological sciences : an official journal of the Society of Toxicology · 2024Article
- Toxicokinetics, dose-response, and risk assessment of nanomaterials: Methodology, challenges, and future perspectives.Wiley interdisciplinary reviews. Nanomedicine and nanobiotechnology · 2022Review
Corrections and comments
- Erratum issued
Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Two-year toxicology and carcinogenesis rodent studies conducted at the National Toxicology Program (NTP) are used to identify potential adverse health effects in humans due to chemical exposure, including cancer. Liver tumor, the most frequently diagnosed tumor type of chemically induced neoplastic effects documented in NTP's carcinogenicity studies, is usually difficult to be detected at early stage due to the inconspicuous symptoms. However, the abnormal growth of liver cells can lead to liver weight increase, so it is hypothesized that liver tumor incidence is associated with early stage liver weight increase. In this study, the association between liver weight increase and liver tumor incidence are quantified by (1) calculating the correlation coefficient of and (2) building quantitative linear relationship between benchmark dose estimates derived from these two types of data collected from NTP studies. Together with 151 chemical/species/sex combinations of liver tumor data showing positive evidence collected from 76 NTP long-term studies, short-term liver weight data reported in the same NTP report were extracted to be paired with the liver tumor data for the analyses. Results show that the estimated correlation coefficients (as high as 0.78) along with the adequately fitted linear models suggest that the association between relative liver weight increase and aggregated liver tumor incidence are relatively strong. Additional analyses focused on some more specific situations (e.g., specific tumor type or specific strain/sex combination) further confirmed the strong association. Given the design of this study, the interpretation of the findings is not that liver weight increase can be used to predict liver tumor incidence, instead, evident increase in liver weight might be used as a reason to prioritize the test article for a two-year toxicology and carcinogenesis study.
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.