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A Multifactorial Model to Predict the Surgical Complexity of Lung Resection After Neoadjuvant Chemoimmunotherapy
Alessandro Brunelli1, Marco Nardini1, Joshil Lodhia1
1Department of Thoracic Surgery, St James's University Hospital, Leeds, United Kingdom.
Annals of Thoracic Surgery Short Reports
|April 24, 2026
Summary
A new model predicts surgical complexity in lung cancer patients receiving neoadjuvant chemoimmunotherapy. Key predictors include lack of nodal response, advanced N2 stage, and high PD-L1 expression, aiding surgical planning and patient counseling.
Area of Science:
- Oncology
- Thoracic Surgery
- Medical Informatics
Background:
- Neoadjuvant chemoimmunotherapy is increasingly used for locally advanced lung cancer.
- Predicting surgical complexity post-neoadjuvant treatment is crucial for patient management.
- Current methods for assessing surgical complexity in this setting are limited.
Purpose of the Study:
- To develop and validate a predictive model for surgical complexity after neoadjuvant chemoimmunotherapy in lung cancer patients.
- To identify independent predictors of complex surgical procedures in this patient cohort.
Main Methods:
- A retrospective study of 65 patients undergoing surgery after neoadjuvant nivolumab and chemotherapy.
- Surgical complexity was assessed using a 4-dimension score; operations with at least one severe dimension were deemed complex.
- Logistic regression analysis identified predictors of complex procedures, and a weighted model was constructed.
Main Results:
- 43% of lobectomies (n=28) were classified as complex, associated with longer operative times and higher thoracotomy conversion rates.
- Independent predictors of complexity included absence of radiologic nodal response, pretreatment cN2 stage, and PD-L1 expression ≥50%.
- The final model demonstrated a significant increase in complexity proportion with rising risk scores (0-3).
Conclusions:
- The developed predictive model effectively stratifies surgical complexity risk in lung cancer patients.
- This tool can aid in optimizing surgical planning and enhancing patient counseling in the neoadjuvant setting.
- The model's findings highlight the importance of treatment response and disease stage in predicting surgical outcomes.
