Evidence map›Paper›PMID 25288876›Full record

ReviewCancer informatics2014

A review of cancer risk prediction models with genetic variants.

Xuexia Wang, Michael J Oldani, Xingwang Zhao, Xiaohui Huang, Dajun Qian

Registry-linked trialAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Cancer informatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05423860 (This is a Human Analytics Longitudinal Observational), which is not on this map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

NCT05423860 recruitingnot on this mapstarted 2022, after this paper: background citation

This is a Human Analytics Longitudinal Observational (HALO) Study. A Phase I Study to Analyze All Available Biomarkers and Determinants of Health to Increase Diagnostic Accuracy While Reducing the Time to Diagnosis of Disease.

TypeobservationalSponsorHALO DiagnosticsRan2022 to 2037Enrolled2,000ConditionsCardiovascular Diseases, Cancer, Dementia, Traumatic Brain InjuryArmsno interventions will be performed (observational)
3 · Its place in the literature

Who cites it

12 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Exploring the role of health-related quality of life measures in predictive modelling for oncology: a systematic review.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2025
    Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. A Decade of GWAS Results in Lung Cancer.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2018
    Review
  9. Article
  10. Article
  11. A Novel Pathway-Based Approach Improves Lung Cancer Risk Prediction Using Germline Genetic Variations.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2016
    Article
  12. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Xuexia WangJoseph J. Zilber School of Public Health, University of Wisconsin-Milwaukee, Milwaukee, WI, USA.
Michael J OldaniCriminology and Anthropology Department, University of Wisconsin-Whitewater, Whitewater, WI, USA.
Xingwang ZhaoJoseph J. Zilber School of Public Health, University of Wisconsin-Milwaukee, Milwaukee, WI, USA.
Xiaohui HuangSanofi-Aventis, Bridgewater, NJ, USA.
Dajun QianCity of Hope, Durate, CA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cancer risk prediction models are important in identifying individuals at high risk of developing cancer, which could result in targeted screening and interventions to maximize the treatment benefit and minimize the burden of cancer. The cancer-associated genetic variants identified in genome-wide or candidate gene association studies have been shown to collectively enhance cancer risk prediction, improve our understanding of carcinogenesis, and possibly result in the development of targeted treatments for patients. In this article, we review the cancer risk prediction models that have been developed for popular cancers and assess their applicability, strengths, and weaknesses. We also discuss the factors to be considered for future development and improvement of models for cancer risk prediction.

Indexed as

cancercancer interventioncancer risk predictiongenetic variantsrisk prediction models

Identifiers

PMID25288876
PMCPMC4179686

What OpenQuestion holds

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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.