Wu Qiuxia, Su Cuiqun, Xing Manxia, Ouyang Liangmei
Department of Neurosurgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
People's Hospital of Guangdong Province Ganzhou Hospital, Ganzhou Municipal Hospital, Ganzhou, China.
Front Oncol. 2025 Jun 16;15:1497151. doi: 10.3389/fonc.2025.1497151. eCollection 2025.
Cancer-related fatigue (CRF) is one of the most prevalent symptoms which drastically affect patient health and quality of life. This study aimed to construct and validate a nomogram to accurately predict the occurrence of cancer-related fatigue in patients with glioma.
This cross-sectional study included 470 glioma patients from two hospitals (training cohort: n=284; validation cohort: n=186). All patients were categorized into two groups based on their Numerical Rating Scale scores of cancer-related fatigue: a no or mild fatigue group (scores 0-3) and a moderate to severe fatigue group (scores 4-10). LASSO model and multivariable logistic regression analyses were used to determine the significant risk factors contributing to the occurrence of cancer-related fatigue in glioma patients. A nomogram was constructed and its predictive accuracy and conformity was validated by ROC curves, calibration curves and decision curve analysis.
Combining LASSO algorithm and multivariable logistic regression analyses, the cancer stage (p=0.014), and the scores of Perceived Social Support Scale (PSSS) (p<0.001), physical functioning (PF) (p<0.001), bodily pain (BP) (p=0.031), general health (GH) (p<0.001), and mental health (MH) (p=0.009) were the independent risk factors for cancer-related fatigue of glioma patients. A clinically quantitative predictive model nomogram was developed based on these extracted risk factors. The concordance-index of nomogram was 0.964 (0.935-0.993). The AUC values of nomogram were 0.916 (CI: 0.879-0.953) in the training cohort and 0.885 (CI: 0.829-0.941) in the validation cohort. The calibration curves of this nomogram exhibited a notable concordance with the ideal diagonal line. The decision curve analyses illuminated that this nomogram achieved high clinical net benefit.
The nomogram, incorporating the cancer stage of glioma, perceived social support, and quality of life of patients, demonstrated good accuracy and clinical practicality. It can serve as a valuable prediction and evaluation tool for anticipating the occurrence of cancer-related fatigue in patients with glioma.
癌症相关疲劳（CRF）是最常见的症状之一，严重影响患者的健康和生活质量。本研究旨在构建并验证一种列线图，以准确预测胶质瘤患者发生癌症相关疲劳的情况。
这项横断面研究纳入了来自两家医院的470例胶质瘤患者（训练队列：n = 284；验证队列：n = 186）。根据癌症相关疲劳的数字评定量表得分，将所有患者分为两组：无或轻度疲劳组（得分0 - 3）和中度至重度疲劳组（得分4 - 10）。采用LASSO模型和多变量逻辑回归分析来确定导致胶质瘤患者发生癌症相关疲劳的显著危险因素。构建列线图，并通过ROC曲线、校准曲线和决策曲线分析验证其预测准确性和一致性。
结合LASSO算法和多变量逻辑回归分析，癌症分期（p = 0.014）、领悟社会支持量表（PSSS）得分（p < 0.001）、生理功能（PF）得分（p < 0.001）、身体疼痛（BP）得分（p = 0.031）、总体健康状况（GH）得分（p < 0.001）和心理健康（MH）得分（p = 0.009）是胶质瘤患者癌症相关疲劳的独立危险因素。基于这些提取的危险因素，开发了一种临床定量预测模型列线图。列线图的一致性指数为0.964（0.935 - 0.993）。训练队列中列线图的AUC值为0.916（CI：0.879 - 0.953），验证队列中为0.885（CI：0.829 - 0.941）。该列线图的校准曲线与理想对角线表现出显著的一致性。决策曲线分析表明，该列线图具有较高的临床净效益。
该列线图纳入了胶质瘤的癌症分期、患者领悟的社会支持和生活质量，显示出良好的准确性和临床实用性。它可以作为预测和评估胶质瘤患者发生癌症相关疲劳的有价值工具。
