Abstract
Introduction: Despite significant development in systemic therapy and radiotherapy, surgery is still the cornerstone for curative lung cancer treatment. Although predicted postoperative function (ppo) somewhat exactly correlates with actual postoperative function bigger differences may be a cause of serious clinical outcome.
Aim: The aim of our study was to identify clinical factors affecting prediction accuracy of postoperative lung function for more careful selection of operable lung cancer patients.
Patients and methods: Seventy patients were studied prospectively. The preoperative lung function tests (FEV1 and DL,CO) were performed within a week before surgery, and the follow-up tests were performed 4 to 6 weeks after surgery. Calculation of predicted postoperative values were calculated by three methods: two segment formulas and vibration response imaging (VRI). The correlation between each clinical parameter and accuracy of prediction was screened on univariate analysis of Pearson’s correlation coefficient, and significant factors were confirmed by multivariate linear regression analysis applying backward stepwise elimination approach.
Results: Univariate linear regression analysis between the predicted and the actual postoperative values of FEV1% and DL,CO showed the highest prediction accuracy with acoustic mapping (VRI). Multivariate regression analysis showed that prediction accuracy of postoperative lung function is significantly affected by COPD (p<0.001) and volume of resection (p<0.001).
Conclusion: Vibration response imaging (VRI) is a more accurate method for predicting postoperative lung function than segment method formulas. Anatomical calculation significantly underestimates the postoperative values of FEV1% in patients with COPD. Prediction of FEV1% and DL,CO with segment counting is significantly influenced by the volume of resection.