A nomogram based on hematological markers to predict radiosensitivity in patients with esophageal squamous cell carcinoma. Issue 11 (17th March 2023) - Sun, Lijun; Wei, Peng; Ge, Shuang; Zheng, Jie; Ye, Shucheng; Zhang, Yanhui
Abstract : This study aimed to determine the predictive value of pretreatment levels of hematological markers on the radiosensitivity of patients with esophageal squamous cell carcinoma (ESCC). The specific hematological markers assessed included total lymphocyte count (TLC), neutrophil count, platelet count, monocyte count, neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), and lymphocyte-monocyte ratio (LMR). A total of 353 ESCC patients who received radiotherapy (RT) alone or concurrent RT between 2015 and 2019 were reviewed. Pretreatment levels of hematological markers (NLR, PLR, LMR, and TLC) were used to assess the radiosensitivity of individual patients. Receiver operating characteristics curves were used to determine optimal cutoff values. Multivariate logistic models with radiosensitivity were established with meaningful results used for univariate analyses. Finally, a nomogram was developed and validated from the calibration curve and concordance index. One month after RT, 121 (34.3%) cases were shown to have low levels of radiosensitivity based on hematological markers. Univariate analyses showed that NLR, PLR, LMR, and TLC were associated with high levels of radiosensitivity (all markers, P < .05). Due to the collinearity between NLR, PLR, and LMR, these markers were separately evaluated by multivariate analysis. Multivariate analysis showed that high pretreatment NLP and PLR were independently associated with high radiosensitivity. In contrast, high pretreatment LMR and TLC were independent biomarkers associated with lower radiosensitivity. The concordance index of the nomogram was 0.737, and the calibration curves predicted by the nomogram were highly consistent with the observed experimental findings. Pretreatment hematologic markers (NLR, PLR, LMR, and TLC) can be used to predict the radiosensitivity of patients with ESCC accurately.