Evidence mapPaperPMID 40720523Full record

ArticlePLOS global public health2025

Optimizing breast cancer screening strategies for women with different BMI levels in Ghana: A simulation-based study on BMI-dependent tumor growth model.

Asamoah Larbi, Eric Nyarko, Samuel Iddi

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Article in PLOS global public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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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

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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

3 authors.

Asamoah LarbiDepartment of Statistics and Actuarial Science, School of Physical and Mathematical Sciences, University of Ghana, Legon, Accra, Ghana.ORCID https://orcid.org/0000-0003-4456-621X
Eric NyarkoDepartment of Statistics and Actuarial Science, School of Physical and Mathematical Sciences, University of Ghana, Legon, Accra, Ghana.ORCID https://orcid.org/0000-0002-1666-7489
Samuel IddiDepartment of Statistics and Actuarial Science, School of Physical and Mathematical Sciences, University of Ghana, Legon, Accra, Ghana.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Breast cancer is a disease in which abnormal cells in the breast tissue grow out of control to form tumors and can spread to other parts of the body. While it can affect both men and women, it poses a greater risk to women, and it is a leading cause of cancer-related deaths worldwide. This study aimed to examine different mammography screening interval strategies using a body mass index (BMI)-dependent tumor growth model and a simulation approach. The goal was to identify the optimal screening strategy for various BMI levels by investigating the association between BMI and tumor growth rate, and further examine the relationship between BMI and screening outcomes, using a continuous growth model and Cox regression, respectively. Our results indicated that a biennial screening interval yielded the best outcomes for all BMI levels compared to annual and triennial strategies. Obese individuals may require higher screening sensitivity and are likely to benefit from shorter screening intervals than those with other body weights within the screening age range of 30 to 65 years. Additionally, obese individuals have a slightly higher risk of being diagnosed with interval-detected cancers rather than screen-detected cancers. In contrast, women with a normal body weight have a greater chance of being detected through screening rather than at intervals. These findings suggest that breast cancers may become symptomatic more quickly in obese individuals than in those with lower body weights. Consequently, the standard two-year screening interval may not be optimal for this group, indicating that more frequent screenings (14-18 months) could be necessary. This underscores the potential impact of improved screening practices to enhance the treatment and management of breast cancer.

Identifiers

PMID40720523
PMCPMC12303353

What Socratic holds

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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 Socratic graph.