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Author Spotlight: Enhancing Diagnostic Strategies and Biomarker Development for Comprehensive Lung Function Analysis
Published on: August 9, 2024
Prediction of postoperative lung function in lung cancer patients using perfusion scintigraphy
Ik Dong Yoo1, Jooyeon Jamie Im2, Yong-An Chung2
11 Department of Nuclear Medicine, Soonchunhyang University Cheonan Hospital, Cheonan, Republic of Korea.
Background:
Predicting postoperative lung function is critical in lung cancer patients. Perfusion scintigraphy has been used to estimate postoperative function after lung resection.
Purpose:
To evaluate the usefulness of the posterior oblique method in relation to other conventional processing methods for predicting postoperative lung function using lung perfusion scintigraphy.
Material And Methods:
Fifty-five patients with non-small-cell lung cancer who underwent lobectomy were enrolled. Forced expiratory volume in 1 s (FEV1) values were obtained from preoperative and postoperative pulmonary function tests. After performing lung perfusion scintigraphy, predicted FEV1 values were calculated using the segment, conventional, posterior, and posterior oblique methods. Postoperative FEV1 values were compared with predicted FEV1 values.
Results:
The mean value of the preoperative FEV1 was 2.29 L and that of the postoperative FEV1 was 1.89 L. The mean values of the predicted postoperative FEV1 values for the segment, conventional, posterior, and posterior oblique were 1.83 L, 1.94 L, 1.88 L, and 1.89 L, respectively. Between the observed and predicted FEV1 values, there was a strong correlation without significant difference except for conventional method. Bland-Altman analysis showed that segment and posterior methods underestimated the FEV1, whereas conventional and posterior oblique methods overestimated the FEV1.
Conclusion:
Predictions with each processing method of lung perfusion scintigraphy showed nearly similar results to the actual postoperative lung function. The posterior oblique method of lung perfusion scintigraphy showed a very small difference to such an extent as to be equal to the observed FEV1, implying that this method may be applied for predicting postoperative lung function in lung cancer patients.
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Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

