Recommendations are made that LDCT screening high-risk populations to reduce lung cancer specific mortality by 16% to 24% and screening protocols should prioritize high-risk individuals‚ regardless of their age or smoking status.
BACKGROUND
Cancer-related cognitive impairment (CRCI) is a major clinical challenge faced by lung cancer patients during or after treatment. Early identification of at-risk populations by healthcare professionals is inadequate, and little is known about measures that can be taken to enhance their prevention. Existing systematic reviews and meta-analyses have summarized common risk factors for CRCI in lung cancer patients, but integrated predictive models based on holistic theoretical frameworks remain scarce.
AIM
To construct a visual assessment tool for the identification of CRCI in lung cancer survivors based on the theory of unpleasant symptoms (TOUS), complementing existing predictive models with a multidimensional theoretical perspective.
DESIGN
A prospective, observational, single-centre study.
METHODS
The present study was conducted in a major hospital in Urumqi, China, between October 2023 and July 2024. A total of 350 lung cancer survivors participated in this survey, which was divided into a training and validation group in a 7:3 ratio. Lasso regression and logistic regression analyses were employed to identify the risk factors for CRCI, construct a nomogram prediction model and test the prediction effect in the validation set. Model performance was evaluated using the area under the curve (AUC) and goodness-of-fit statistics, and the model was internally validated.
RESULTS
A total of 350 lung cancer patients, comprising 245 in the training and 105 in validation groups, were included. Of these, 117 (33.4%) experienced CRCI. The predictive model identified significant predictors, including age, pathological stage, chemotherapy, post-traumatic stress disorder (PTSD), depression and social support scores. At the 32.3% optimal cut-off, the model had AUC values of 0.863 and 0.818 in the training and validation groups. Calibration plots demonstrated a strong correlation between predicted and observed rates, and decision curve analysis revealed optimal net benefit at threshold probabilities ranging from 10% to 80%.
CONCLUSIONS
The risk prediction model constructed in this study, based on TOUS, demonstrates satisfactory predictive ability superior to some existing models. It integrates physiological, psychological and environmental factors, serving as a valuable complementary tool for healthcare professionals in identifying high-risk groups, particularly in clinical settings emphasizing holistic symptom management.
Mengyuan Qiao, Li Luo, Hui Zhang· International Journal of Nur...· 0 citations
Lung cancer is the leading cause of cancer-related mortality worldwide, yet the majority of cases are diagnosed at advanced stages when a cure is rarely achievable. Low-dose computed tomography (LDCT) screening in high-risk populations reduces lung cancer mortality by 20–24% in randomised trials, but fewer than 20% of eligible adults in the United States undergo annual screening. We conducted a structured narrative review of PubMed, Embase, and the Cochrane Library for studies published between January 2002 and March 2026, supplemented by review of current clinical guidelines from the USPSTF, NCCN, ACS, CHEST, and ERS. Evidence from the National Lung Screening Trial (NLST) and the NELSON trial establishes the mortality benefit of LDCT screening, though both trials have important methodological limitations that affect generalisability. The NLST predominantly detected non-small cell lung cancer (NSCLC), particularly adenocarcinoma and squamous cell carcinoma, while small cell lung cancer (SCLC) was infrequently screen detected and showed no survival benefit from early detection. Guideline eligibility criteria have progressively broadened, and multivariable risk model-based selection using the PLCOm2012 now demonstrates superiority over categorical smoking thresholds in prospective validation cohorts, with the added benefit of reducing racial and ethnic eligibility disparities. Overdiagnosis estimates have declined substantially with extended follow-up, reaching 7% when observation exceeds five years. Implementation remains critically deficient: patient stigma, provider knowledge gaps, structural barriers, and inadequate electronic health record infrastructure collectively account for screening uptake below 20%. Integrating smoking cessation into screening encounters is evidence-based and cost-effective. Artificial intelligence tools show promising performance in nodule detection and risk prediction, but lack the prospective external validation required for routine clinical deployment. The field has established efficacy; the urgent challenge is now effectiveness at scale. Transitioning to risk model-based eligibility, expanding access to underserved populations, and mandating cessation integration represent the three highest-priority actions. A research agenda addressing never-smoker screening, personalised intervals, and robust AI validation must proceed in parallel.
Arihant Surana, Riya Bhattacharya· Romanian Journal of Preventi...· 0 citations
This study identified several personal characteristics that were more strongly linked to breast cancer occurrence, highlighting the value of integrating clinical and self-reported information to better understand individual risk and support more targeted prevention and screening efforts.
M. Franchini, F. Denoth, Stefania Pieroni et al.· Cancers· 0 citations
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D. Campa, C. Rizzato, A. Kataki et al.· Revista Espanola de Enfermed...· 0 citations
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V. Dvorovy, L. Kováčová, M. Selvek et al.· European Heart Journal, Supp...· 0 citations
This research demonstrates the feasibility of using big data to conduct case-control analyses to identify associations between possible risk factors and cancers and demonstrates the limitations of the traditional epidemiological approach by requiring less effort, time, and cost.
J. Jaddoo· 0 citations
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