ArticleInternational journal of general medicine2024
A Novel Scoring System to Predict Acute Radiation Enteritis Recovery in Cervical Cancer Patients Undergoing Concurrent Chemoradiotherapy: A Southwest China Cohort Study.
Article in International journal of general medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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7 authors.
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Abstract
Purpose: To establish a pragmatic and effective predictive model for monitoring the recovery of radiation enteritis (RE) in cervical cancer patients undergoing concurrent chemoradiotherapy (CCRT). Methods: This study included 105 cervical cancer patients undergoing CCRT. We assessed baseline clinicopathologic characteristics, evaluated the effects of CCRT on circulating immune cells, tumor biomarkers, and inflammatory cytokines, and developed a predictive scoring system, the Immune-Tumor-Score (ITS), using the LASSO-Cox regression model. The model performance of LASSO-Cox and nomogram was compared via ROC curve and calibration curve. Results: The median age of the patients was 55 years, with 53.3% having a normal BMI and 46.7% having positive lymph nodes. Post-CCRT, significant decreases were observed in lymphocyte counts, T-cell subpopulations, and tumor markers (CA125, TPA, SCCA, CYFRA21). The CD4/CD8 ratio and IL10 levels were significantly higher post-CCRT, while inflammation indexes (NLR, ELR) increased, and LMR decreased. The ITS, derived from 11 significant parameters, effectively predicted RE recovery, outperforming a traditional nomogram. Higher ITS scores correlated with shorter RE recovery times, as validated by Kaplan-Meier analyses and ROC curves (AUC = 0.822). Conclusion: The ITS system provides a robust and reliable tool for predicting RE recovery in cervical cancer patients undergoing CCRT, surpassing traditional models in accuracy and reliability. This tool enables better patient management by allowing for timely interventions and personalized treatment strategies. Future research should focus on validating these findings in larger cohorts and integrating additional clinical parameters to enhance the predictive power of the ITS.
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