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Record W4409625038 · doi:10.1158/1538-7445.am2025-3591

Abstract 3591: Global burden and trends in cancer incidence and mortality among young adults

2025· article· en· W4409625038 on OpenAlexaboutno aff
Yejun Son, Nikita Sandeep Wagle, Dong Keon Yon, Ahmedin Jemal, Hyuna Sung

Bibliographic record

VenueCancer Research · 2025
Typearticle
Languageen
FieldMedicine
TopicCancer Risks and Factors
Canadian institutionsnot available
Fundersnot available
KeywordsCancerMedicineCancer incidenceIncidence (geometry)DemographyGerontologyInternal medicine

Abstract

fetched live from OpenAlex

Abstract Background: Increasing cancer incidence among young adults has been reported for several types of cancers, primarily in certain high-income countries. However, there is a lack of comprehensive examination of the global burden and trends in cancer incidence and mortality among young adults. Methods: We presented estimated numbers of cancer cases, cancer deaths, age-standardized incidence, and mortality rates per 100, 000 people among young adults (YAs, ages 25-49 years) for all cancers combined and 35 cancer sites in 185 countries and 20 UN regions using GLOBOCAN 2022. We further quantified trends in age-standardized incidence (ASIR; 52 countries) and age-standardized mortality (ASMR; 62 countries) for the top five incident cancers (female breast, cervix uteri, thyroid, colorectum, and trachea, bronchus and lung cancers) among YAs over the most recent decade by estimating average annual percent change (AAPC), using the Cancer Incidence in Five Continents (CI5; 2008-2017) and WHO mortality data (2011-2020; thyroid mortality data was not available), respectively. Results: In 2022, an estimated 2.9 million cancer cases and 962, 000 cancer-related deaths occurred in YAs worldwide, corresponding to 108.1 cases per 100, 000 people per year and 35.9 deaths per 100, 000 people per year. Globally, ASIR and ASMR were higher in women than men (143.7 vs. 73.5 for incidence; 40.0 vs. 32.0 for mortality). By UN region, ASIR per 100, 000 people was highest in Australia-New Zealand (185.7) and lowest in South Central Asia (72.5), while ASMR per 100, 000 was highest in Eastern Africa (55.9) and lowest in Northern Europe (21.9). For the most recent 10 years, increases in ASIRs were observed for thyroid cancer (AAPC, 2.5-19.6%) in 27 countries, for colorectal cancer (AAPC, 1.2-11.5%) in 20 countries, for female breast cancer (AAPC, 0.7-5.9%) in 17 countries, and for cervix uteri cancer (AAPC, 1.8-15.7%) in 4 countries. Increases in ASMR were observed for colorectum cancer (AAPC, 0.6-8.0%) in 13 countries, for female breast cancer (AAPC, 0.9-4.8%) in 11 countries, and for cervix uteri cancer (AAPC, 2.1-4.7%) in 6 countries. Incidence and mortality both increased in YAs for female breast cancer in Czechia, ovarian cancer in the Republic of Korea, cervix uterine cancer in the Netherlands, and colorectum cancer in Australia, Canada, Chile, and the USA. Trachea, bronchus and lung cancers ASIR and ASMR decreased in 24 (AAPC, -1.5 to -11.3%) and 47 (AAPC, -2.0 to -11.4%) countries among YAs, respectively. Conclusions and Relevance: The burden and trends in cancer among young adults vary significantly by sex and region. Rising incidence and mortality rates for multiple cancer types across the world underscore the need for renewed attention to cancer prevention efforts and improvements to cancer surveillance systems targeting this often overlooked age group. Citation Format: Kieran Patrick Kelly, Yejun Son, Nikita Sandeep Wagle, Dong Keon Yon, Ahmedin Jemal, Hyuna Sung. Global burden and trends in cancer incidence and mortality among young adults [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 3591.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.200
Threshold uncertainty score0.857

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.067
GPT teacher head0.473
Teacher spread0.406 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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