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| The significance of prognostic nutritional index in the prognosis of patients with esophageal cancer undergoing radiotherapy
and the construction of predictive model |
| Feng Dan ,Wang Guangming |
| Department of Radiotherapy Fuyang Cancer Hospital Fuyang 236000 Anhui China |
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Abstract To explore the prognostic nutritional index PNI in patients with esophageal cancer undergoing
radiotherapy and to establish an associated nomogram risk model. Method Patients with esophageal cancer who received radiotherapy
in our hospital from March 2018 to October 2019 were selected as subjects. PNI was calculated according to the laboratory examination
data within one week before the first radiotherapy. The best cut-off value of PNI was calculated by ROC curve and patients were
divided into low PNI group and high PNI group. The clinical characteristics of the two groups were compared. Univariate and Cox
regression were used to analyze the factors influencing the prognosis of patients with esophageal cancer radiotherapy. Result The
optimal PNI cut-off value was 48. 03. There were statistically significant differences in age TNM stage and tumor diameter between the
low PNI group and the high PNI group P<0. 05 . High TNM stage tumor diameter ≥3 cm prescription dose < 60 Gy and PNI <
48. 03 were independent risk factors for death in patients with esophageal cancer radiotherapy P < 0. 05 . The results of model
verification showed that the C-index was 0. 846 the calibration curve was close to the ideal curve and the AUC of ROC curve was
0. 881 95%CI = 0. 847-0. 912 indicating that the model had good predictive ability. Conclusion PNI has a high predictive value for
the prognosis of patients with esophageal cancer radiotherapy and the nomogram model constructed based on PNI can effectively predict
the risk of death in patients with esophageal cancer radiotherapy.
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| Cite this article: |
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Feng Dan,Wang Guangming. The significance of prognostic nutritional index in the prognosis of patients with esophageal cancer undergoing radiotherapy
and the construction of predictive model[J]. Electron J Metab Nutr Cancer, 2022, 9(6): 794-799.
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| URL: |
| http://182.92.200.144/EN/ OR http://182.92.200.144/EN/Y2022/V9/I6/794 |
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