This study aims to explore the nomogram risk prediction model for chemotherapy prognosis of patients with non-small cell lung cancer (NSCLC) based on clinical features and pathological parameters, and its clinical application value. Patients with NSCLC were randomly divided into a training set (n = 168) and a validation set (n = 72) according to the ratio of 7:3. In the training set, multivariate logistic regression was used to analyze the risk factors of chemotherapy prognosis and build a nomogram prediction model. Receiver operating characteristic curve and calibration curve were drawn to evaluate the prediction efficiency of the nomogram model, which was verified in the validation set. Among the 168 patients in the training set, 68 (40.48%) had a poor prognosis, while 30 (41.67%) of the 72 patients in the validation set had a poor prognosis. There was no significant difference in the incidence, clinical features, and pathological parameters between the training set and the validation set ( P > .05). The results of univariate analysis showed that there were significant differences in age, hypertension, smoking history, cough, hemoptysis, dyspnea, tumor diameter, histological subtypes, contact area between tumor and blood vessels, and short diameter of lymph nodes between patients with poor prognosis and patients with good prognosis in the training set ( P < .05). Multivariate logistic regression analysis showed that age, cough, hemoptysis, dyspnea, tumor diameter, histological subtypes, and contact area between tumor and blood vessels were independent risk factors for poor prognosis ( P < .05). Further construct the nomogram prediction model; the nomogram model has good calibration and fitting degree between prediction and reality in the training set and validation set (C-index is 0.923 and 0.947). The results of the Hosmer–Lemeshow test are χ 2 = 10.692, P = .219 and χ 2 = 6.350, P = .608, respectively; the area under the curve of the nomogram model in predicting poor prognosis is 0.918 (95% confidence interval: 0.870–0.966) and 0.946 (95% confidence interval: 0.887–1.000). The model shows good calibration and fitting in both the training set and validation set, and can accurately predict the prognosis of patients with NSCLC after chemotherapy, which can provide valuable reference for clinicians to formulate individualized treatment plans and evaluate the prognosis of patients.
Sh et al. (2026) studied this question.