Study on the relationship between peripheral blood inflammation-nutritional parameters and pathological characteristics in
patients with endometrial cancer based on automatic classifier and its short-term prognostic predictive value
Ma Jingjing, Liu Wenting, Gao Lu, Liu Yanjia
Department of gynecology the First Affiliated Hospital of Xinjiang Medical University Urumqi 830054 Xinjiang China
Abstract：To analyze the relationship between peripheral blood inflammation-nutritional parameters and pathological
characteristics of patients with endometrial cancer based on automatic classifiers and its short - term prognostic predictive value.
Method A retrospective study included 156 patients with endometrial cancer who underwent surgical treatment in the First Affiliated
Hospital of Xinjiang Medical University from April 2020 to April 2024 the experimental group and 110 age-matched healthy women
the control group . The fibrinogen - albumin ratio FAR prognostic nutritional index PNI lymphocyte-monocyte ratio LMR
and systemic immune inflammation index SII were compared between the two groups. Further analyze the differences of each index
under different pathological characteristics FIGO stage grade etc. in the experimental group. Based on the Modeler automatic
classifier screen the prognostic influencing factors of patients with endometrial cancer analyze the relationship between peripheral
blood inflammation-nutrition parameters and prognosis and explore its predictive value. Result The research results showed that there
were significant differences in inflammation and nutritional indicators between the experimental patients and the control group. The FAR
0. 08±0. 02 vs 0. 07±0. 01 and SII 610. 04±90. 86 vs 425. 64±102. 12 of the experimental group were significantly higher than
those of the control group P<0. 05 . However PNI 49. 61±8. 05 vs 52. 69±1. 57 and LMR 3. 96±0. 78 vs 4. 35±0. 51 were
significantly lower than those of the control group P<0. 05 . Further analysis revealed that patients with positive vascular invasion or
nerve invasion had higher FAR and SII than those with negative results and lower PNI and LMR than those with negative results. The
FAR and SII of patients in FIGO stage II were significantly lower than those of patients in stage Ⅲ and IV P<0. 05 . Among the
prognosis prediction models established through the Modeler algorithm neural networks with an accuracy rate of 96. 15% Bayesian
networks 94. 87% and C5 decision trees 94. 23% all demonstrated excellent prediction performance with FAR being the most important predictor. Conclusion The inflammation-nutrition parameters in the peripheral blood of patients with endometrial cancer are
significantly correlated with pathological characteristics. The accuracy rate of the prediction model constructed based on Modeler is over
94%. The combination of inflammation - nutrition indicators and pathological characteristics can be used as an effective prognostic
evaluation tool.
马晶晶,刘文婷,高 璐,刘艳佳. 基于自动分类器的子宫内膜癌患者外周血炎症-营养
参数与病理特征的关系及短期预后预测价值研究[J]. 肿瘤代谢与营养电子杂志, 2025, 12(6): 761-770.
Ma Jingjing, Liu Wenting, Gao Lu, Liu Yanjia. Study on the relationship between peripheral blood inflammation-nutritional parameters and pathological characteristics in
patients with endometrial cancer based on automatic classifier and its short-term prognostic predictive value. Electron J Metab Nutr Cancer, 2025, 12(6): 761-770.
