A prognostic prediction model was constructed based on the nutritional status of patients with non-small cell lung cancer

Zhang Hui; Zhao Lisha; Wu Xiaoran; Ji Chang; Gao Zhiguo

doi:10.16689/j.cnki.cn11-9349/r.2026.04.011

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Electronic Journal of Metabolism and Nutrition of Cancer ›› 2026, Vol. 13 ›› Issue (4) : 526-534. DOI: 10.16689/j.cnki.cn11-9349/r.2026.04.011

Original Articles

A prognostic prediction model was constructed based on the nutritional status of patients with non-small cell lung cancer

¹Zhang Hui, ²Zhao Lisha, ¹Wu Xiaoran, ¹Ji Chang, ¹Gao Zhiguo

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Abstract

Objective This study aims to screen nutritional status indicators with predictive value for the prognosis of lung cancer patients through Cox proportional hazards regression analysis and nomogram modeling, and construct a comprehensive predictive model. The nutrition-inflammation-immune axis indicators reflecting the overall condition of patients will be incorporated into the evaluation system. Method This study included 177 patients with non-small cell lung cancer (NSCLC) who underwent surgical treatment and were admitted to Xingtai People's Hospital from January 2019 to January 2022. They were divided into the death group (n=63) and the survival group (n=114) based on their 3-year survival status after surgery. The clinical data and nutritional status indicators [body mass index (BMI), musculoskeletal mass index (SMI), prealbumin (PA), transferrin (TRF), hemoglobin - albumin - lymphocyte - platelet composite index (HALP), prognostic nutritional index (PNI), and controlled nutritional status score (CONUT)] of the two groups of patients were collected. The differences in the above indicators between the death group and the survival group were compared. Cox proportional hazards regression was used to analyze the independent risk factors affecting prognosis, and a nomogram prediction model was constructed based on the screened key variables. The model discrimination was evaluated by the ROC curve, the prediction accuracy was tested by the calibration curve, and the clinical benefits were analyzed by the decision curve. Result The single-factor analysis showed that the patients in the death group had a higher proportion of TNM stage (stage Ⅲ/Ⅳ) and lower degree of malignancy, and the levels of SMI and HALP were significantly lower than those in the survival group (P < 0.05). The Cox regression results indicated that the degree of tumor differentiation and HALP were the influencing factors for poor prognosis in NSCLC patients (P < 0.05). The model efficacy analysis revealed that the AUC of a single indicator (degree of differentiation, HALP) for prediction was 0.591 and 0.767 respectively, and the AUC of the comprehensive prediction model reached 0.776. The calibration curve showed that the predicted values of the model were highly consistent with the actual observations (the Bias-corrected curve was close to the Ideal line). The decision curve indicated that the clinical net benefit of this model was > 0, which was superior to relying solely on TNM stage or nutritional indicators. Conclusion This study, through Cox regression analysis, clearly confirmed that HALP is an independent nutritional predictor for the prognosis of NSCLC patients. On this basis, by integrating the key clinical pathological feature of tumor differentiation degree, a comprehensive nomogram prediction model was successfully constructed.

Key words

Non-small cell lung cancer / Nutritional sensitivity index / Prognostic nutritional index / Control nutritional status score / Prognosis / Influencing factors / Nomograph

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Zhang Hui, Zhao Lisha, Wu Xiaoran, Ji Chang, Gao Zhiguo. A prognostic prediction model was constructed based on the nutritional status of patients with non-small cell lung cancer[J]. Electronic Journal of Metabolism and Nutrition of Cancer. 2026, 13(4): 526-534 https://doi.org/10.16689/j.cnki.cn11-9349/r.2026.04.011

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