Nomogram for Predicting Pathological Complete Response to Neoadjuvant Chemotherapy Combined with Immunotherapy in NSCLC Patients

Background The application of neoadjuvant immunotherapy combined with chemotherapy in intermediate and high-risk resectable non-small cell lung cancer (NSCLC) is increasing, with some patients achieving pathological complete response (pCR). However, there is no effective tool for clinical precision prediction of pCR. This study aims to integrate clinical data to develop and construct a nomogram model capable of predicting the risk of pCR in NSCLC patients following neoadjuvant chemotherapy combined with immunotherapy, thereby providing a reference for individualized treatment decisions. Methods A retrospective analysis was conducted on patients with resectable NSCLC who underwent neoadjuvant chemotherapy combined with immunotherapy between 2019 and 2022. Inclusion criteria were as follows: diagnosis of NSCLC (stages IIB–IIIB), completion of neoadjuvant chemotherapy and immunotherapy followed by surgical resection, and a definitive postoperative pathological assessment. Clinical parameters such as age, gender, smoking history, comorbidities, neoadjuvant treatment regimens, and treatment-related adverse events were collected. Univariate and multivariate logistic regression analyses were performed to identify predictive factors for pCR, which were subsequently used to construct a nomogram model. Results A total of 179 patients were included in the study, comprising 92 cases (51.4%) in the pCR group and 87 cases (48.6%) in the non-pCR group. Multivariate analysis identified the following factors as significantly associated with pCR (P < 0.05): pathological type, family history of tumors, duration of smoking cessation, age, and number of neoadjuvant treatment cycles. A multivariate logistic regression model incorporating these factors demonstrated an area under the curve (AUC) of 0.709 (95% CI: 0.633–0.785), indicating good predictive performance. The calibration curve showed strong agreement between predicted probabilities and observed outcomes. Conclusions Based on real clinical data, this study developed and constructed a nomogram to predict pCR in NSCLC patients after neoadjuvant chemotherapy combined with immunotherapy. This tool not only performs well in terms of discrimination and calibration but also has potential clinical application value, providing new ideas and basis for the formulation of individualized treatment strategies.