Preoperative Three-Dimensional Virtual Resection Predicts Postoperative Pulmonary Function After Anatomical Resection : A Prospective Longitudinal Study
Sponsor study identifier
observational
60
1 country
1
Study Start
April 7, 2026
Actual (reported)Primary Completion
June 30, 2027
EstimatedPlanned date reported by the registry; this milestone is not confirmed as achieved.
Study Completion
June 30, 2027
EstimatedPlanned date reported by the registry; this milestone is not confirmed as achieved.
This study aims to validate a novel preoperative assessment strategy using three-dimensional (3-D) computed tomography (CT) reconstruction and virtual resection simulation. The goal is to accurately predict postoperative pulmonary function in patients with non-small cell lung cancer (NSCLC) undergoing Video-Assisted Thoracoscopic Surgery (VATS) anatomical resection. Accurate prediction of postoperative lung function is crucial for patient safety. Traditional methods, such as segment counting, often lack precision because they assume all lung segments contribute equally to function, ignoring variations caused by tumors or emphysema. This study utilizes 3-D "virtual resection" to quantify the "Planned Resected Ventilated Lung Volume Fraction" (pRVLVF) before surgery. The study will recruit 60 participants divided into two groups: those undergoing lobectomy (n=30) and those undergoing segmentectomy (n=30). Participants will undergo standard thin-slice CT scans and pulmonary function tests (PFT) before surgery. Postoperatively, lung function and recovery will be tracked at 3, 6, and 12 months to develop a dynamic prediction model and evaluate the compensatory capacity of the residual lung.
Registry-reported information. Elapsed time and enrollment totals alone do not measure study progress or recruitment pace.
June 30, 2027
EstimatedPlanned date reported by the registry; this milestone is not confirmed as achieved.
Click on a node to explore related trials.
Dates and qualifiers reported by the registry. Estimated dates do not confirm that a milestone has occurred.
February 22, 2026
Actual (reported)February 27, 2026
Actual (reported)April 7, 2026
Actual (reported)June 30, 2027
EstimatedPlanned date reported by the registry; this milestone is not confirmed as achieved.
June 30, 2027
EstimatedPlanned date reported by the registry; this milestone is not confirmed as achieved.
Mean Absolute Error (MAE) of Predicted Postoperative FEV1
The accuracy of the preoperative 3D virtual resection model will be evaluated by calculating the Mean Absolute Error (MAE) between the predicted FEV1 and the actual measured FEV1. A lower MAE indicates higher prediction accuracy. The study targets an MAE of less than 180 mL.
3 months post-operation
Long-term Prediction Error of FEV1 and FVC
6 months and 12 months post-operation
Patients with non-small cell lung cancer scheduled to undergo video-assisted thoracoscopic segmentectomy.
Patients with non-small cell lung cancer scheduled to undergo video-assisted thoracoscopic lobectomy.
Patients with non-small cell lung cancer recruited from the thoracic surgery outpatient clinics and inpatient wards of National Taiwan University Hospital.
National Taiwan University Cancer Center
Taipei, Taiwan
Chen L, Yang J, Zhang C, Zhang L, Han X, Dong C, Gui S, Liu X, Shi H. Quantitative computed tomography assessment of pulmonary function and compensation after lobectomy and segmentectomy in lung cancer patients. J Thorac Dis. 2024 Sep 30;16(9):5765-5778. doi: 10.21037/jtd-24-492. Epub 2024 Sep 6.
Colombi D, Risoli C, Delfanti R, Chiesa S, Morelli N, Petrini M, Capelli P, Franco C, Michieletti E. Software-Based Assessment of Well-Aerated Lung at CT for Quantification of Predicted Pulmonary Function in Resected NSCLC. Life (Basel). 2023 Jan 10;13(1):198. doi: 10.3390/life13010198.
First Submitted
February 22, 2026
First Posted
February 27, 2026
Study Start
April 7, 2026
Actual (reported)Primary Completion
June 30, 2027
EstimatedFeasibility and Uptake of Low-Dose CT Lung Cancer Screening in Kuwait
Contact the study team to confirm eligibility.
Jeong YH, Lee H, Jang HJ, Park DW, Choi YY, Lee SJ. Predicting postoperative lung function using ventilation SPECT/CT in patients with lung cancer. J Thorac Dis. 2024 Feb 29;16(2):1054-1062. doi: 10.21037/jtd-23-1563. Epub 2024 Feb 26.
Kang HJ, Lee SS. Comparison of Predicted Postoperative Lung Function in Pneumonectomy Using Computed Tomography and Lung Perfusion Scans. J Chest Surg. 2021 Dec 5;54(6):487-493. doi: 10.5090/jcs.21.084.
Bolliger CT, Guckel C, Engel H, Stohr S, Wyser CP, Schoetzau A, Habicht J, Soler M, Tamm M, Perruchoud AP. Prediction of functional reserves after lung resection: comparison between quantitative computed tomography, scintigraphy, and anatomy. Respiration. 2002;69(6):482-9. doi: 10.1159/000066474.
Wu MT, Chang JM, Chiang AA, Lu JY, Hsu HK, Hsu WH, Yang CF. Use of quantitative CT to predict postoperative lung function in patients with lung cancer. Radiology. 1994 Apr;191(1):257-62. doi: 10.1148/radiology.191.1.8134584.
Wu MT, Pan HB, Chiang AA, Hsu HK, Chang HC, Peng NJ, Lai PH, Liang HL, Yang CF. Prediction of postoperative lung function in patients with lung cancer: comparison of quantitative CT with perfusion scintigraphy. AJR Am J Roentgenol. 2002 Mar;178(3):667-72. doi: 10.2214/ajr.178.3.1780667.
Ueda K, Tanaka T, Hayashi M, Li TS, Tanaka N, Hamano K. Computed tomography-defined functional lung volume after segmentectomy versus lobectomy. Eur J Cardiothorac Surg. 2010 Jun;37(6):1433-7. doi: 10.1016/j.ejcts.2010.01.002. Epub 2010 Feb 11.
Ueda K, Tanaka T, Li TS, Tanaka N, Hamano K. Quantitative computed tomography for the prediction of pulmonary function after lung cancer surgery: a simple method using simulation software. Eur J Cardiothorac Surg. 2009 Mar;35(3):414-8. doi: 10.1016/j.ejcts.2008.04.015. Epub 2008 May 16.
Fernandez-Rodriguez L, Torres I, Romera D, Galera R, Casitas R, Martinez-Ceron E, Diaz-Agero P, Utrilla C, Garcia-Rio F. Prediction of postoperative lung function after major lung resection for lung cancer using volumetric computed tomography. J Thorac Cardiovasc Surg. 2018 Dec;156(6):2297-2308.e5. doi: 10.1016/j.jtcvs.2018.07.040. Epub 2018 Aug 2.
Oswald NK, Halle-Smith J, Mehdi R, Nightingale P, Naidu B, Turner AM. Predicting Postoperative Lung Function Following Lung Cancer Resection: A Systematic Review and Meta-analysis. EClinicalMedicine. 2019 Sep 10;15:7-13. doi: 10.1016/j.eclinm.2019.08.015. eCollection 2019 Oct.
Park H, Yun J, Lee SM, Hwang HJ, Seo JB, Jung YJ, Hwang J, Lee SH, Lee SW, Kim N. Deep Learning-based Approach to Predict Pulmonary Function at Chest CT. Radiology. 2023 Apr;307(2):e221488. doi: 10.1148/radiol.221488. Epub 2023 Feb 14.
Planned date reported by the registry; this milestone is not confirmed as achieved.
Study Completion
June 30, 2027
EstimatedPlanned date reported by the registry; this milestone is not confirmed as achieved.
Last Updated
May 13, 2026
Record last verified: 2026-02
Individual participant data will not be shared.
Download the complete ClinicalTrials.gov record imported by Syfrah, including fields not shown in this page. The registry may contain a newer revision.
Source: ClinicalTrials.gov.
JSON downloads include source attribution and retrieval details alongside the unchanged original record or the separately normalized values.
Source retrieved: