ArticleOncotarget2017
Novel risk models for early detection and screening of ovarian cancer.
Article in Oncotarget, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Current and Emerging Methods for Ovarian Cancer Screening and Diagnostics: A Comprehensive Review.Cancers · 2022Review
- SWATH-MS identification of CXCL7, LBP, TGFβ1 and PDGFRβ as novel biomarkers in human systemic mastocytosis.Scientific reports · 2022Article
- A comprehensive analysis of somatic alterations in Chinese ovarian cancer patients.Scientific reports · 2021Article
- Statistical approaches using longitudinal biomarkers for disease early detection: A comparison of methodologies.Statistics in medicine · 2020Article
- Understanding tumor ecosystems by single-cell sequencing: promises and limitations.Genome biology · 2018Review
- Elevation of TP53 Autoantibody Before CA125 in Preclinical Invasive Epithelial Ovarian Cancer.Clinical cancer research : an official journal of the American Association for Cancer Research · 2017Article
- Unravelling biology and shifting paradigms in cancer with single-cell sequencing.Nature reviews. Cancer · 2017Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
Abstract
purposeOvarian cancer (OC) is the most lethal gynaecological cancer. Early detection is required to improve patient survival. Risk estimation models were constructed for Type I (Model I) and Type II (Model II) OC from analysis of Protein Z, Fibronectin, C-reactive protein and CA125 levels in prospectively collected samples from the United Kingdom Collaborative Trial of Ovarian Cancer Screening (UKCTOCS).
resultsModel I identifies cancers earlier than CA125 alone, with a potential lead time of 3-4 years. Model II detects a number of high grade serous cancers at an earlier stage (Stage I/II) than CA125 alone, with a potential lead time of 2-3 years and assigns high risk to patients that the ROCA Algorithm classified as normal. MATERIALS AND
methodsThis nested case control study included 418 individual serum samples serially collected from 49 OC cases and 31 controls up to six years pre-diagnosis. Discriminatory logit models were built combining the ELISA results for candidate proteins with CA125 levels.
conclusionsThese models have encouraging sensitivities for detecting pre-clinical ovarian cancer, demonstrating improved sensitivity compared to CA125 alone. In addition we demonstrate how the models improve on ROCA for some cases and outline their potential future use as clinical tools.
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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.