Evidence map›Paper›PMID 40778115›Full record

ArticlemedRxiv : the preprint server for health sciences2025

Projecting individualized probabilities of lifetime all-cancer risk.

Neel M Butala, Noor Al-Hammadi, Asiri Ediriwickrema, Jaime L Schneider, Aaron Fullerton, Jeya Balasubramanian, Parichoy Pal Choudhury, Nilanjan Chatterjee

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Neel M ButalaDepartment of Medicine, University of Colorado School of Medicine, Aurora, CO.
Noor Al-HammadiDepartment of Health & Clinical Outcomes Research, Saint Louis University School of Medicine, St. Louis, MO.
Asiri EdiriwickremaDepartment of Medicine, Division of Hematology, Stanford University School of Medicine, Stanford, CA.ORCID 0000-0002-6480-8290
Jaime L SchneiderMassachusetts General Hospital Cancer Center, Boston, MA.
Aaron FullertonCatch Bio, Chicago, IL.
Jeya BalasubramanianDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD.
Parichoy Pal ChoudhuryDepartments of Surveillance and Health Equity Science and Population Science, American Cancer Society, Atlanta, GA.
Nilanjan ChatterjeeDepartment of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.

Funding

Robust Methods for Polygenic Analysis to Inform Disease Etiology and Enhance Risk PredictionR01HG010480 · NHGRI · JOHNS HOPKINS UNIVERSITY · PI CHATTERJEE, NILANJAN · 2019 to 2023
$2.8M
Multifactoral breast cancer risk prediction accounting for ethnic and tumor diversityU01CA249866 · NCI · JOHNS HOPKINS UNIVERSITY · PI CHATTERJEE, NILANJAN · 2020 to 2023
$2.3M
Pharmacogenetic Refinement of the Warfarin Dose Using Machine LearningR01HL173734 · NHLBI · WASHINGTON UNIVERSITY · PI BRIAN F GAGE · 2024 to 2026
$1.2M
NCI NIH HHS U01 CA249866NHGRI NIH HHS R01 HG010480NHLBI NIH HHS R01 HL173734
6 · The paper itself

Abstract

Introduction: Technological advances and direct-to-consumer marketing have unearthed significant organic demand from patients for cancer screening and prevention. However, in the absence of strong data or guidelines, physicians have minimal support on how to approach patients in clinical practice. Methods: We projected individualized probabilities of 10-year and lifetime cancer risk across a population as well as potential improvement with healthy behaviors in the UK Biobank. Results: A total of 118 distinct variables were included across 38 cancer-specific models. The distribution of lifetime cancer risk had a rightward skew and wide variation for both men and women. The median lifetime cancer risk was 29.5% for men (interquartile range (IQR) 8.4%) and 21.0% for women (IQR 8.8%). If all modifiable risk factors were set to the ideal state, this decreased to 20.5% for men (IQR 3.9%) and 16.5% for women (IQR 4.9%). There was considerable overlap between age groups, with men aged 50-59 at the 90 Conclusions: Lifetime cancer risk varies widely across the UK Biobank cohort, but this risk decreases substantially with healthy behaviors. There was considerable overlap in 10-year cancer risk between age groups, suggesting that future multicancer screening guidelines should account for more than age and sex as more evidence becomes available in the future.

Indexed as

Cancerlifetimepopulationpreventionriskrisk factors

Identifiers

PMID40778115
PMCPMC12330417

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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.