Evidence map›Paper›PMID 42165065›Full record

ArticleFrontiers in psychiatry2026

Patterns and predictors of cognitive function and post-traumatic growth in cancer patients: a latent profile analysis.

Libing Zhang, Riyu Huang, Meijuan Cao, Juan Fang, Dongyan Lu

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Article in Frontiers in psychiatry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Libing Zhang *School of Medicine, Huzhou University, Huzhou, Zhejiang, China.
Riyu Huang *School of Medicine, Huzhou University, Huzhou, Zhejiang, China.
Meijuan CaoSchool of Medicine, Huzhou University, Huzhou, Zhejiang, China.
Juan FangSchool of Medicine, Huzhou University, Huzhou, Zhejiang, China.
Dongyan LuSchool of Medicine, Huzhou University, Huzhou, Zhejiang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Cancer patients often face concurrent challenges of cancer-related cognitive impairment (CRCI) and opportunities for post-traumatic growth (PTG), but existing research primarily adopts a variable-centered approach, failing to capture the heterogeneous co-occurrence patterns of CRCI and PTG or identify key predictors of these patterns. This study aimed to explore latent profiles of psychological adaptation and their predictive factors among cancer patients, thereby responding to the practical need for refined understanding of heterogeneous adaptation patterns and providing a reference for stratified clinical interventions. Methods: A cross-sectional study was conducted between August and December 2024, recruiting 249 cancer patients from two tertiary hospitals in Zhejiang, China. Data were collected using sociodemographic and disease information, the Functional Assessment of Cancer Therapy-Cognition (FACT-Cog), the Post-Traumatic Growth Inventory (PTGI), and the Chinese Big Five Personality Inventory Brief Version (CBF-PI-B). Latent profile analysis was employed to classify patients based on standardized FACT-Cog and PTGI scores, with binary logistic regression used to identify predictors of profile membership. Results: Based on model fit indices from latent profile analysis, a two-profile solution was identified as optimal. This included the "cognitively stable but growth-constricted" profile (73.1%), characterized by better self-reported cognitive function but limited post-traumatic growth, and the "post-traumatic growth but cognitively vulnerable" profile (26.9%), featuring significant psychological growth alongside prominent perceived cognitive impairment. Binary logistic regression analysis revealed that personality traits (neuroticism, extraversion, conscientiousness, agreeableness, openness), treatment-related factors (radiotherapy, endocrine therapy), and educational level served as significant factors influencing profile membership. Conclusion: Heterogeneity exists in psychological adaptation patterns integrating CRCI and PTG among cancer patients, manifesting as two distinct profiles. Patients with lower neuroticism, higher levels of extraversion, conscientiousness, agreeableness, and openness, or those undergoing radiotherapy are more likely to belong to the "post-traumatic growth but cognitively vulnerable" profile; high educational level and endocrine therapy also show marginal positive associations with this profile. These findings support the development of tailored interventions that address these specific psychological adaptation patterns to optimize psychological care and enhance patients' quality of life and overall well-being.

Indexed as

cancercancer-related cognitive impairmentlatent profile analysispersonality traitspost-traumatic growth

Identifiers

PMID42165065
PMCPMC13183558

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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.