Work

A Multifactorial Model to Predict the Surgical Complexity of Lung Resection After Neoadjuvant Chemoimmunotherapy

Year: 2025

Type: article

Abstract: Background This study aims to develop an predictive model for surgical complexity after neoadjuvant chemoimmunotherapy. Methods This is real clinical practice study of consecutive patients undergoing surgery after neoadjuvant nivolumab and chemotherapy for locally advanced lung cancer (April 23, 2023 through December 24, 2024). Surgical complexity was graded by the operating surgeon using a 4-dimension published score. An operation was defined as complex if at least 1 of the dimensions scored severely more complex than a standard lobectomy. Logistic regression analysis was used to test the association of several patient- and tumor-related factors with the presence of a complex procedure. The model was constructed by proportionally weighing the regression coefficients. Results The analysis included 65 patients. The most frequent procedure was lobectomy (86%), of which 28 cases (43%) were classified as complex. Complex procedures were longer (median 228 minutes vs 180 minutes; P = .003) and were more frequently converted to thoracotomy (48% vs 6.7%, P = .001). Logistic regression analysis showed that absence of radiologic nodal response to neoadjuvant treatment (score, 1 point; regression coefficient, 2.4; P = .007), pretreatment cN2 stage (score 1 point; regression coefficient, 2.1; P = .024), and programmed death-ligand 1 ≥50% (score 1 point; regression coefficient, 1.67; P = .012) were independent predictors of a complex procedure. The final predictive surgical complexity model ranged from 0 to 3. The proportion of complex operations significantly increased with the higher risk scores (ie, 0% in patients with no risk factors, to 100% in those with all 3 risk factors, P < .001). Conclusions The proposed model can assist to efficiently planning lung cancer surgery in the neoadjuvant setting and for patient counseling.

Source: Annals of Thoracic Surgery Short Reports

DOI: 10.1016/j.atssr.2025.09.014