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Aims: To develop a model to predict the risk of moderate to severe cancer-related fatigue (CRF) in colorectal cancer patients after chemotherapy. Methods: The study population was colorectal cancer patients who received chemotherapy from September 2021 to June 2022 in a grade 3 and rst-class hospital. Demographic, clinical, physiological, psychological, and socioeconomic factors were collected 1 to 2 days before chemotherapy. Patients were followed for 1 to 2 days after chemotherapy to assess fatigue using the Piper Fatigue Scale. A random sampling method was used to select 181 patients with moderate to severe CRF as the case group. The risk set sampling method was used to select 181 patients with mild or no CRF as the control group.Logistic regression, back-propagation arti cial neural network (BP-ANN) and decision tree models were constructed and compared.Results: A total of 362 patients consisting of 241 derivation samples and 121 validation samples were enrolled. Comparing the three models, the prediction effect of BP-ANN was the best, with a receiver operating characteristic curve (ROC) of 0.83. Internal and external veri cation indicated the accuracy of prediction was 70.4% and 80.8%, respectively. Signi cant predictors identi ed were surgery, complications, hypokalaemia, albumin, neutrophil percentage, pain (VAS score), Activities of Daily Living (ADL) score, sleep quality (PSQI score), anxiety (HAD-A score), depression (HAD-D score) and nutrition (PG-SGA score).Conclusions: BP-ANN was the best model, offering theoretical guidance for clinicians to formulate a tool to identify patients at high risk of moderate to severe CRF. Impact• A prediction model can be developed to predict the risk of moderate to severe cancer-related fatigue in colorectal cancer patients after chemotherapy.• The BP-ANN model offers theoretical guidance for a clinically predictable tool to assist nurses in identifying and supporting patients at high risk of moderate to severe CRF.• There are 11 risk factors for moderate to severe CRF in patients with colorectal cancer after chemotherapy, and the BP-ANN is the best prediction model with strong predictive performance.

Background Recurrence after initial primary resection is still a major and ultimate cause of death for non-small cell lung cancer patients. We attempted to build an early recurrence associated gene signature to improve prognostic prediction of non-small cell lung cancer. Methods Propensity score matching was conducted between patients in early relapse group and long-term survival group from The Cancer Genome Atlas training series (N = 579) and patients were matched 1:1. Global transcriptome analysis was then performed between the paired groups to identify tumour-specific mRNAs. Finally, using LASSO Cox regression model, we built a multi-gene early relapse classifier incorporating 40 mRNAs. The prognostic and predictive accuracy of the signature was internally validated in The Cancer Genome Atlas patients. Results A total of 40 mRNAs were finally identified to build an early relapse classifier. With specific risk score formula, patients were classified into a high-risk group and a low-risk group. Relapse-free survival was significantly different between the two groups in both discovery (HR: 3.244, 95% CI: 2.338-4.500, P < 0.001) and internal validation series (HR 1.970, 95% CI 1.181-3.289, P = 0.009). Further analysis revealed that the prognostic value of this signature was independent of tumour stage, histotype and epidermal growth factor receptor mutation (P < 0.05). Time-dependent receiver operating characteristic analysis showed that the area under receiver operating characteristic curve of this signature was higher than TNM stage alone (0.771 vs 0.686, P < 0.05). Further, decision curve analysis curves analysis at 1 year revealed the considerable clinical utility of this signature in predicting early relapse. Conclusions We successfully established a reliable signature for predicting early relapse in stage I–III non-small cell lung cancer.