Related Experiment Video
Updated: Jul 17, 2026
Evaluating Regional Pulmonary Deposition using Patient-Specific 3D Printed Lung Models
Published on: November 11, 2020
Patient selection for lung volume reduction surgery: An objective model based on prior clinical decisions and
D S Gierada1, R D Yusen, I A Villanueva
1Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA. gieradad@mir.wustl.edu
Objectives:
We used whole-lung quantitative CT analysis (QCT)-an objective method of evaluating emphysema severity and distribution based on measurement of lung density-to determine whether subjective selection criteria for lung volume reduction surgery are applied consistently and to model the patient selection process, and assessed the relationship of the model to postoperative outcome.
Design:
Logistic regression analysis using QCT indexes of emphysema and preoperative physiologic test results as the independent variables, and the decision to operate as the dependent variable.
Setting:
University hospital.
Patients:
Seventy patients selected for bilateral lung volume reduction surgery and 32 otherwise operable patients excluded from surgery based on subjective assessment of emphysema morphology on chest radiography, CT, and perfusion scintigraphy.
Intervention:
Bilateral lung volume reduction surgery in the selected group.
Measurements And Results:
Emphysema in patients selected for surgery was more severe overall and in the upper lungs by multiple QCT indexes (p < 0.01, unpaired two-tailed t test). Physiologic abnormalities were slightly more severe in selected patients (p < 0.05, unpaired two-tailed t test). The range of many QCT and physiologic values overlapped considerably between the selected and excluded groups. The percent severe emphysema (<- 960 Hounsfield units [HU]), upper/lower lung emphysema ratio (- 900 HU threshold), and residual volume were the key variables in the model predicting selection decisions (model r(2) = 0.48; p < 0.0001). The model correctly predicted selection decisions in 87% of all cases, 91% of the selected group, and 78% of the excluded group. Surgical patients with a higher model-derived probability of selection had greater postoperative improvement in FEV(1) and 6-min walk distance.
Conclusions:
Radiologic selection criteria are applied consistently to the majority of patients. QCT features are strongly associated with selection decisions, are related to outcome, and may help improve consistency and confidence in patient selection.
