Predicting Incomplete Resection in Non-Small Cell Lung Cancer Preoperatively: A Validated Nomogram.

Area of Science:

• Oncology
• Surgical Oncology
• Cancer Research

Background:

• Incomplete resection (R1-R2) in surgically treated stage I-III non-small cell lung cancer (NSCLC) is associated with poor prognosis.
• Accurate prediction of incomplete resection risk is crucial for optimizing treatment strategies in NSCLC patients.

Purpose of the Study:

• To develop and validate a predictive model for estimating the likelihood of incomplete resection in patients undergoing surgery for NSCLC.
• To identify key preoperative patient-, tumor-, and treatment-related factors influencing resection completeness.

Main Methods:

• Utilized a Dutch national cancer database to identify NSCLC patients who underwent surgical treatment without neoadjuvant therapy.
• Employed multivariable logistic regression to construct a prediction model, incorporating thirteen potential predictors.
• Validated the model internally and externally using the American National Cancer Database, presenting it as a nomogram.

Main Results:

• Identified histology, cT stage, cN stage, extent of surgery, and surgical approach (open vs. thoracoscopic) as independent predictors of incomplete resection.
• The nomogram demonstrated good discriminatory ability with a corrected C statistic of 0.72 after internal validation and 0.71 after external validation.
• The model showed good overall fit and calibration in both internal and external patient cohorts.

Conclusions:

• An internationally validated nomogram is available to predict the individual risk of incomplete resection in stage I-III NSCLC patients.
• High predicted risk may prompt consideration of alternative treatment strategies, while low risk supports surgical intervention.
• This tool aids in personalized treatment planning for NSCLC surgical candidates.