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Background: Fatigue is a common source of distress for cancer survivors. The severity of cancer-related fatigue varies significantly, which may be due to individual differences in host factors., Aim: This cross-sectional study aims to explore how demographic, oncological, sociological, psychological, and stress-related hormones levels interact to influence the distinct experiences of fatigue (Cancer-related fatigue [CRF] occurrence and fatigue degree)., Methods: A cross-sectional study carried out at the oncology outpatient and ward department of Xiyuan Hospital of China Academy of Chinese Medical Sciences recruited 306 cancer patients between January 2021 to December 2021. General information, fatigue, psychological factors was evaluated by general information questionnaire, the Revised Piper's Fatigue Scale-Chinese Version (RPFS-CV), and the self-report Hospital Anxiety and Depression Scale (HADS). Stress-related hormones were measured with chemiluminescent enzyme immunoassay (Zhengzhou Antobio)., Results: 306 patients were included, 229 (74.8%) were diagnosed with CRF, including 94 (41.0%) with mild fatigue, 121 (52.8%) with moderate fatigue, and 14 (6.1%) with severe fatigue. Multivariate regression analysis showed that higher depression scores, aldosterone levels may increase the risk of CRF. Patients who are obese (Body mass index ≥ 28 kg/m 2 ) may help to reduce the risk of CRF. Other contributing factors for increased levels of fatigue ( p < 0.05) include being female, having anxiety, depression and high aldosterone levels., Conclusion: The research suggested that CRF was a common symptom in cancer survivors and pay attention to these influencing factors may help to better identify patients susceptible to fatigue and provide long-term, targeted interventions., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Gu, Xu, Zhu, Lam, Yi, Gong, Wang, Guo, Fu, Shi, Wang and Liu.)

Objective: The purpose of this study is to establish model for assessing inert nodules predicting nodule volume-doubling., Methods: A total of 201 patients with T1 lung adenocarcinoma were analysed retrospectively pulmonary nodule information was predicted by an AI pulmonary nodule auxiliary diagnosis system. The nodules were classified into two groups: inert nodules (volume-doubling time (VDT)>600 days n=152) noninert nodules (VDT<600 days n=49). Then taking the clinical imaging features obtained at the first examination as predictive variables the inert nodule judgement model >(INM) volume-doubling time estimation model (VDTM) were constructed based on a deep learning-based neural network. The performance of the INM was evaluated by the area under the curve (AUC) obtained from receiver operating characteristic (ROC) analysis the performance of the VDTM was evaluated by R 2 (determination coefficient)., Results: The accuracy of the INM in the training and testing cohorts was 81.13% and 77.50%, respectively. The AUC of the INM in the training and testing cohorts was 0.7707 (95% CI 0.6779-0.8636) and 0.7700 (95% CI 0.5988-0.9412), respectively. The INM was effective in identifying inert pulmonary nodules; additionally, the R2 of the VDTM in the training cohort was 0.8008, and that in the testing cohort was 0.6268. The VDTM showed moderate performance in estimating the VDT, which can provide some reference during a patients' first examination and consultation., Conclusion: The INM and the VDTM based on deep learning can help radiologists and clinicians distinguish among inert nodules and predict the nodule volume-doubling time to accurately treat patients with pulmonary nodules., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Wang, Zhang, Li, Fu, Li, Gao, Wang, Yang, Xu, Wang and Hou.)