Background
The Center for Epidemiologic Studies Depression scale (CESD) was widely used for screening of depressive symptoms. The purpose of the current study was to investigate the factor structure and measurement invariance of the CESD across genders and groups in a sample of Chinese undergraduates and clinical patients.
Methods
Participants included 3093 undergraduates from the Hunan province and 336 patients from psychological clinics. The structure of the CESD scale was analyzed by confirmatory factor analysis (CFA). Multiple sets of CFAs were used to test measurement invariance across genders among undergraduates and clinical patients. Internal consistency reliability was also evaluated.
Results
The five-factor model achieved satisfactory fit (in the undergraduate sample: WLSMVχ2 = 1662.385, df = 160, CFI = 0.973, TLI = 0.968, RMSEA = 0.055; in the clinical patients: WLSMVχ2 = 502.089, df = 160, CFI = 0.962, TLI = 0.955, RMSEA = 0.072). The measurement invariance of the five-factor model across genders was supported fully assuming different degrees of invariance. The CESD also showed acceptable internal consistency.
Conclusion
Due to its sound structure and measurement invariance, the five-factor model of the CESD is best suited for testing in Chinese mainland college students and clinical patients.
Background
To predict the risk of radiation pneumonitis (RP), deep learning (DL) models were built to stratify lung cancer patients. Our study also investigated the impact of RP on survival.
Methods
This study retrospectively collected 100 RP and 99 matched non-RP lung cancer patients treated with radiotherapy from two independent centers. These patients were randomly divided into training (n = 175) and validation cohorts (n = 24). The radiomics and dosiomics features were extracted from radiation planning computed tomography (CT). Clinical information was retrospectively collected from the electronic medical record database. All features were screened by LASSO cox regression. A multi-omics prediction model was developed by the optimal algorithm and estimated the area under the receiver operating characteristic curve (AUC). Overall survival (OS) between RP, non-RP, mild-RP, and severe-RP groups was analyzed by the Kaplan-Meier method.
Results
There were eventually selected 16 radiomics features, 2 dosiomics features, and 1 clinical feature to build the best multi-omics model. GLRLM_Gray Level Non Uniformity Normalized and GLCM_MCC from PTV were essential dosiomics features, and T stage was a paramount clinical feature. The optimal performance for predicting RP was the AUC of testing set [0.94, 95% confidence interval (CI) (0.939-1.000)] and the AUC of external validation set [0.92, 95% CI (0.80-1.00)]. All RP patients were divided into mild-RP and severe-RP group according to RP grade (≤ 2 grade and > 2 grade). The median OS was 31 months (95% CI, 28–39) for non-RP group compared with 49 months (95% CI, 36-NA) for RP group (HR = 0.53, P = 0.0022). Among RP subgroup, the median OS was 57months (95% CI, 47-NA) for mild-RP and 25 months (95% CI, 29-NA) for severe-RP, and mild-RP group exhibited a longer OS (HR = 3.72, P < 0.0001).
Conclusion
The multi-omics model contributed to improvement in the accuracy of the RP prediction. Interestingly, this study also demonstrated that compared with non-RP patients, RP patients displayed longer OS, especially mild-RP.
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