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Estimating Postoperative Lung Function Using Three-Dimensional Segmental HRCT-Reconstruction: A Retrospective Pilot

Aljaz Hojski1, Brigitta Gahl2, Michael Tamm3

  • 1Department of Thoracic Surgery, University Hospital Basel, Spitalstrasse 21, 4001 Basel, Switzerland.

Journal of Personalized Medicine
|August 27, 2025
PubMed
Summary
This summary is machine-generated.

Three-dimensional (3D) reconstruction software shows promise in identifying optimal candidates for anatomical lung resection, potentially improving personalized surgical planning for non-small cell lung cancer (NSCLC). This advanced imaging may offer better patient selection than traditional methods.

Keywords:
3D lung reconstructionanatomical lung resectionpostoperative pulmonary function prediction

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Area of Science:

  • Thoracic Surgery
  • Pulmonary Medicine
  • Medical Imaging

Background:

  • Assessing suitability for anatomical lung resection, particularly for non-small cell lung cancer (NSCLC), is crucial for optimizing patient outcomes.
  • Conventional methods for predicting postoperative pulmonary function may lack precision in personalized surgical planning.

Purpose of the Study:

  • To evaluate if three-dimensional (3D) reconstruction software simulating postoperative lung volumes can more effectively identify suitable candidates for anatomical lung resection compared to conventional methods.
  • To assess the role of 3D-HRCT reconstruction in personalized surgical planning for NSCLC.

Main Methods:

  • A single-center pilot study involving 20 patients undergoing VATS resection for NSCLC.
  • Correlation of 3D simulations from preoperative HRCT scans with measured pulmonary function.
  • Comparison of 3D simulation predictions with the "5% per segment rule" and the Brunelli model.

Main Results:

  • Patients with increased postoperative FEV1 had a lower proportion of healthy lung tissue in 3D simulations (p=0.045).
  • Both conventional models (Brunelli and "5% rule") underestimated postoperative pulmonary function.
  • The "5% rule" was found to be more accurate than the Brunelli model, but both were less precise than 3D simulation in this pilot study.

Conclusions:

  • 3D-HRCT reconstruction software shows potential for patient-tailored identification of candidates benefiting from surgical intervention.
  • Further research is needed to validate this technology for predicting postoperative pulmonary function, especially in patients with severe COPD.
  • 3D-segmental-HRCT-reconstruction software enables individualized assessment of lung resection benefits for NSCLC.