3D Virtual Resection for Predicting Lung Function in VATS

NCT07436598 | Recruiting

• 1 other identifier: 202510086RINC
• Study Type: observational
• Target: 60
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Non-small Cell Lung Cancer; Lung Cancer; Lung Neoplasms

Keywords

Three-dimensional virtual resection; Pulmonary Function Prediction; Ventilated Lung Volume Fraction (VLVF); VATS Anatomical Resection; Segmentectomy; Lobectomy; Postoperative Compensation Coefficient

Outcome Measures

Primary Outcomes (1)

• 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

Secondary Outcomes (1)

• Long-term Prediction Error of FEV1 and FVC

  6 months and 12 months post-operation

Study Arms (2)

VATS Segmentectomy Group

Patients with non-small cell lung cancer scheduled to undergo video-assisted thoracoscopic segmentectomy.

VATS Lobectomy Group

Patients with non-small cell lung cancer scheduled to undergo video-assisted thoracoscopic lobectomy.

Eligibility Criteria

• Age: 18 Years - 80 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients with non-small cell lung cancer recruited from the thoracic surgery outpatient clinics and inpatient wards of National Taiwan University Hospital.

You may qualify if:

• Patients scheduled for video-assisted thoracoscopic (VATS) lobectomy or segmentectomy at National Taiwan University Hospital or NTU Cancer Center.
• Age between 18 and 80 years.
• Patients who have signed the informed consent form agreeing to provide imaging data for 3D modeling.

You may not qualify if:

• Age younger than 18 or older than 80 years.
• Patients not scheduled for VATS lobectomy or segmentectomy.
• Patients diagnosed with Chronic Obstructive Pulmonary Disease (COPD).
• Patients unable or unwilling to sign the informed consent form.
• Vulnerable populations.

Sponsors & Collaborators

• National Taiwan University Hospital: lead

Study Sites (1)

National Taiwan University Cancer Center

Taipei, Taiwan

RECRUITING

Related Publications (12)

• 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.

  PMID: 39444877 BACKGROUND

• 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.

  PMID: 36676147 BACKGROUND

• 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.

  PMID: 38505088 BACKGROUND

• 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.

  PMID: 34815369 BACKGROUND

• 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.

  PMID: 12456999 BACKGROUND

• 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.

  PMID: 8134584 BACKGROUND

• 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.

  PMID: 11856695 BACKGROUND

• 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.

  PMID: 20153214 BACKGROUND

• 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.

  PMID: 18485724 BACKGROUND

• 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.

  PMID: 30195604 BACKGROUND

• 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.

  PMID: 31709409 BACKGROUND

• 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.

  PMID: 36786699 BACKGROUND

MeSH Terms

Conditions

Carcinoma, Non-Small-Cell Lung; Lung Neoplasms

Condition Hierarchy (Ancestors)

Carcinoma, Bronchogenic; Bronchial Neoplasms; Respiratory Tract Neoplasms; Thoracic Neoplasms; Neoplasms by Site; Neoplasms; Lung Diseases; Respiratory Tract Diseases

Central Study Contacts

Chih-Hsiang Chang, MD

CONTACT

+886-0972653384; thenightdeity@gmail.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor, Department of Surgery

Study Record Dates

• First Submitted: February 22, 2026
• First Posted: February 27, 2026
• Study Start: April 7, 2026
• Primary Completion (Estimated): June 30, 2027
• Study Completion (Estimated): June 30, 2027
• Last Updated: May 13, 2026

Record last verified: 2026-02

Data Sharing

• IPD Sharing: Will not share

Locations

TW (1)