Incidence and risk factors of suicide in patients with lung cancer: a scoping review

Exploring the relationship between illness perception, self-transcendence, and demoralization in patients with lung cancer: A latent profile and mediation analysis

Objective

This study examined the heterogeneity of illness perceptions in patients with lung cancer and evaluated the mediating role of self-transcendence in the relation between illness perception and demoralization.

Methods

A convenience sample of 477 patients with lung cancer was selected from three tertiary hospitals in Wuhan, China, between January and June 2024. Participants completed the Brief Illness Perception Questionnaire, Self-Transcendence Scale, and Demoralization Scale. Data were analyzed using Mplus 8.3 and SPSS 25.0.

Results

Three latent illness perception profiles were identified among patients with lung cancer: low (27.25%), moderate (40.04%), and high (32.71%). Mediation analyses revealed a partial mediation effect in the relation between illness perception and demoralization in the low versus moderate (SE ​= ​1.56, 95% CI ​= ​14.71, 20.86) and high versus low illness perception groups (SE ​= ​1.71, 95% CI ​= ​35.44, 42.71).

Conclusions

Patients with lung cancer exhibited heterogeneous illness perceptions, and self-transcendence partially mediated the relation between illness perception and demoralization. Promoting self-transcendence may help mitigate the negative impact of illness perceptions on demoralization. Clinical interventions aimed at reducing negative illness perceptions and enhancing self-transcendence should be prioritized in the care of patients with lung cancer.

Dynamic prediction of lung cancer suicide risk based on meteorological factors and clinical characteristics：A landmarking analysis approach

Suicide is a severe public health issue globally. Accurately identifying high-risk lung cancer patients for suicidal behavior and taking timely intervention measures has become a focus of current research. This study intended to construct dynamic prediction models for identifying suicide risk among lung cancer patients. Patients were sourced from the Surveillance, Epidemiology, and End Results database, while meteorological data was acquired from the Centers for Disease Control and Prevention. This cohort comprised 455, 708 eligible lung cancer patients from January 1979 to December 2011. A Cox proportional hazard regression model based on landmarking approach was employed to explore the impact of meteorological factors and clinical characteristics on suicide among lung cancer patients, and to build dynamic prediction models for the suicide risk of these patients. Additionally, subgroup analyses were conducted by age and sex. The model's performance was evaluated using the C-index, Brier score, area under curve (AUC) and calibration plot. During the study period, there were 666 deaths by suicide among lung cancer patients. Multivariable Cox results from the dynamic prediction model indicated that age, marital status, race, sex, primary site, stage, monthly average daily sunlight, and monthly average temperature were significant predictors of suicide. The dynamic prediction model demonstrated well consistency and discrimination capabilities. Subgroup analyses revealed that the association of monthly average daily sunlight and monthly average temperature with suicide remained significant among female and younger lung cancer patients. The dynamic prediction model can effectively incorporate covariates with time-varying to predict lung cancer patients' suicide death. The results of this study have significant implications for assessing lung cancer individuals' suicide risk.

Indirect effects of health-related quality of life on suicidal ideation through psychological distress among cancer patients

The interrelationships of suicidal ideation, psychological distress, and impaired health-related quality of life (HRQoL) in cancer patients are complex and multifaceted. Limited empirical evidence exists on the indirect effects of impaired HRQoL on suicidal ideation through psychological distress among cancer patients. To fill this research gap, 250 cancer patients were recruited through a cross-sectional hospital-based research design. Structural equation model (SEM) results indicated that impaired HRQoL is a predictor of psychological distress (β = 0.153; p < 0.05), and psychological distress positively predicts suicidal ideation (β = 0.647; p < 0.000). The study found no direct effects of impaired HRQoL on suicidal ideation (β = -0.05; p = 0.223). Indirect effects of HRQoL on suicidal ideation was confirmed, showing a full-mediation effect β = 0.099 (SE = 0.048, CI = [0.030, 0.189], p < 0.05) (i.e. the pathway impaired HRQoL predict suicidal ideation is through psychological distress). Cognitive-behavioral therapy and other emotional support programs should be considered for cancer patients to mitigate psychological vulnerabilities linking impaired HRQoL to suicidal ideation.

Development of a prediction model for suicidal ideation in patients with advanced cancer: A multicenter, real-world, pan-cancer study in China

BACKGROUND

Patients diagnosed with advanced stage cancer face an elevated risk of suicide. We aimed to develop a suicidal ideation (SI) risk prediction model in patients with advanced cancer for early warning of their SI and facilitate suicide prevention in this population.

PATIENTS AND METHODS

We consecutively enrolled patients with multiple types of advanced cancers from 10 cancer institutes in China from August 2019 to December 2020. Demographic characteristics, clinicopathological data, and clinical treatment history were extracted from medical records. Symptom burden, psychological status, and SI were assessed using the MD Anderson Symptom Inventory (MDASI), Hospital Anxiety and Depression Scale (HADS), and Patient Health Questionnaire-9 (PHQ-9), respectively. A multivariable logistic regression model was employed to establish the model structure.

RESULTS

In total, 2814 participants were included in the final analysis. Nine predictors including age, sex, number of household members, history of previous chemotherapy, history of previous surgery, MDASI score, HADS-A score, HADS-D score, and life satisfaction were retained in the final SI prediction model. The model achieved an area under the curve (AUC) of 0.85 (95% confidential interval: 0.82-0.87), with AUCs ranging from 0.75 to 0.95 across 10 hospitals and higher than 0.83 for all cancer types.

CONCLUSION

This study built an easy-to-use, good-performance predictive model for SI. Implementation of this model could facilitate the incorporation of psychosocial support for suicide prevention into the standard care of patients with advanced cancer.

Predicting High-Grade Patterns in Stage I Solid Lung Adenocarcinoma: A Study of 371 Patients Using Refined Radiomics and Deep Learning-Guided CatBoost Classifier

INTRODUCTION

This study aimed to devise a diagnostic algorithm, termed the Refined Radiomics and Deep Learning Features-Guided CatBoost Classifier (RRDLC-Classifier), and evaluate its efficacy in predicting pathological high-grade patterns in patients diagnosed with clinical stage I solid lung adenocarcinoma (LADC).

METHODS

In this retrospective study, a total of 371 patients diagnosed with clinical stage I solid LADC were randomly categorized into training and validation sets in a 7:3 ratio. Uni- and multivariate logistic regression analyses were performed to examine the imaging findings that can be used to predict pathological high-grade patterns meticulously. Employing redundancy and the least absolute shrinkage and selection operator regression, a radiomics model was developed. Subsequently, radiomics refinement and deep learning features were employed using a machine learning algorithm to construct the RRDLC-Classifier, which aims to predict high-grade patterns in clinical stage I solid LADC. Evaluation metrics, such as receiver operating characteristic curves, areas under the curve (AUCs), accuracy, sensitivity, and specificity, were computed for assessment.

RESULTS

The RRDLC-Classifier attained the highest AUC of 0.838 (95% confidence interval [CI]: 0.766-0.911) in predicting high-grade patterns in clinical stage I solid LADC, followed by radiomics with an AUC of 0.779 (95% CI: 0.675-0.883), and imaging findings with an AUC of 0.6 (95% CI: 0.472-0.726).

CONCLUSIONS

This study introduces the RRDLC-Classifier, a novel diagnostic algorithm that amalgamates refined radiomics and deep learning features to predict high-grade patterns in clinical stage I solid LADC. This algorithm may exhibit excellent diagnostic performance, which can facilitate its application in precision medicine.

Suicidal ideation in patients with cancer: Its prevalence and results of structural equation modelling

OBJECTIVE

Patients with cancer have a higher risk of suicidal ideation (SI) and suicidality than the general population. This study was designed to investigate the prevalence of SI and its association with psychosocial and sociodemographic factors and tumour entity.

METHODS

In this observational cross-sectional study, 4372 adult patients with different cancer entities were enrolled. We assessed the outcome variables (i.e. SI, depressive and anxiety symptoms, mental and physical fatigue and sociodemographic data) using self-report questionnaires. Data were analysed via descriptive statistics, binomial logistic regression and structural equation modelling (SEM).

RESULTS

Among all patients, 627 (14.3%) reported SI, of whom 12.8% reported SI on several days, 0.9% on half of the days and 0.6% nearly every day. Age, anxiety, mental fatigue and the Patient Health Questionnaire-9 items 'feeling down, depressed and hopeless', 'feeling bad about oneself' and 'slowing or agitation' were significant predictors of SI. SEM, including all significant predictors with a latent depressiveness-demoralisation variable, explained 30.3% variance of SI, showing a good fit.

CONCLUSIONS

Our results showed that a significant number of patients with cancer show SI. Future long-term studies are needed to address the differential contribution of depression and demoralisation on SI in patients with cancer.

Incidence and risk factors for suicidal ideation in a sample of Chinese patients with mixed cancer types

PURPOSE

Suicidal ideation (SI) is often overlooked as a risk factor for people with cancer. Because it is often a precursor for suicidal behavior, it is critical to identify and address SI in a timely manner. This study investigated SI incidence and risk factors in a cohort of Chinese patients with mixed cancer types.

METHODS

Data from this cross-sectional study were collected from 588 patients receiving medical therapy for tumors at Nanfang Hospital and the Integrated Hospital of Traditional Chinese Medicine at Southern Medical University. SI was measured using the Self-rating Idea of Suicide Scale (SIOSS). Anxiety and depression were assessed using the Hospital Anxiety and Depression Scale (HADS). The Chinese version of the Demoralization Scale II (DS-II-C) was used to assess demoralization. Univariate and correlation analyses were used to identify correlative factors of SI and multiple stepwise linear regression analysis was used to characterize potential risk factors.

RESULTS

SI was reported in 24.7% of participants and the SIOSS score was 14.00 (13.00, 15.00) in the SI group. Multiple linear regression results showed that demoralization, medical financial burden, cancer type, living condition, caretaker, working state, residence, gender, and marital status explained 32.1% of the SI in this cohort (F = 28.705, P < 0.001).

CONCLUSION

Approximately one-quarter of cancer patients in this study reported SI influenced by both external and internal factors. Characterizing these factors can be informative for prevention and treatment efforts.