Non-small Cell Lung CancerNCT07436598
3D Virtual Resection for Predicting Lung Function in VATS
An observational study of Non-small Cell Lung Cancer and Lung Cancer, run by National Taiwan University Hospital at 1 site in Taiwan.
Trial 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… Read more about this study
- Who is it for?
- Ages 18 to 80 years
- Conditions the study is for
- Non-small Cell Lung Cancer, Lung Cancer, Lung Neoplasms
- How many people
- 60 participants
- Where
- 1 siteTaiwan
- Study dates
- 7 Apr 2026to 30 Jun 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 80 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Patients with non-small cell lung cancer recruited from the thoracic surgery outpatient clinics and inpatient wards of National Taiwan University Hospital.
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Taiwan1 site, 1 recruiting
- Recruiting
National Taiwan University Cancer Center
Taipei
About this study
Official title: Preoperative Three-Dimensional Virtual Resection Predicts Postoperative Pulmonary Function After Anatomical Resection : A Prospective Longitudinal Study
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.
Read the detailed description
Background: Lung cancer remains a leading cause of cancer mortality. For early-stage NSCLC, VATS anatomical resection (lobectomy or segmentectomy) is the standard treatment. However, the safety of surgery depends heavily on the patient's pulmonary reserve. Traditional prediction methods, such as the segment-counting rule, have shown prediction errors of up to 20-30% because they do not account for regional heterogeneity in lung ventilation.
Study Design: This is a prospective, multi-center, longitudinal cohort study. The study intends to enroll 60 patients eligible for VATS anatomical resection. Patients will be stratified into two groups:
- VATS Segmentectomy Group (n=30)
- VATS Lobectomy Group (n=30)
Methodology:
1.Preoperative Assessment: Within 30 days before surgery, all participants will undergo high-resolution thin-slice (1 mm) chest CT and standard Pulmonary Function Tests (PFT).
2.3-D Virtual Resection: Using Synapse 3-D software, a patient-specific anatomical model will be reconstructed. The investigator will perform a "virtual resection" simulation to mark the planned resection area. The system will calculate the Planned Resected Ventilated Lung Volume Fraction (pRVLVF), defined based on well-aerated lung tissue (CT attenuation -950 to -700 HU).
3.Surgical Procedure: Patients will undergo standard VATS lobectomy or segmentectomy as clinically indicated.
4.Postoperative Follow-up: PFTs will be performed at 3, 6, and 12 months post-surgery. Follow-up CT scans will be performed at 6 and 12 months to assess structural remodeling.
Objectives and Analysis:
Primary Objective: To validate the accuracy of the pRVLVF-based prediction model. The primary endpoint is the Mean Absolute Error (MAE) of the predicted FEV1 at 3 months post-surgery, with a target accuracy of MAE < 180 mL.
Secondary Objectives:
- To assess long-term prediction accuracy at 6 and 12 months.
- To quantify the "Compensation Coefficient" (CC) of the residual lung using Linear Mixed-Effects (LME) models, adjusting for age, BMI, and smoking history.
- To evaluate the impact of postoperative complications on the functional recovery curve.
This study seeks to establish a precise, accessible, and dynamic tool for surgical risk assessment and decision-making in thoracic surgery.
- 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
- Medical subject headings
- Carcinoma, Non-Small-Cell Lung, Lung Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| 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. |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 3 months post-operation
Secondary outcomes (1)
Long-term Prediction Error of FEV1 and FVC
Evaluation of the prediction model's accuracy at 6 and 12 months to assess stability over time.
Measured: 6 months and 12 months post-operation
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted27 Feb 2026
- Study start7 Apr 2026
- Primary completion30 Jun 2027 (estimated)
- Study completion30 Jun 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (12)Papers the sponsor has linked to this study.
- Other trials for Non-small Cell Lung CancerStudies of the same condition, open and under way.
- More trials run by National Taiwan University HospitalThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 60 participants (estimated)
Sponsor and investigators
- Lead sponsor
- National Taiwan University Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, National Taiwan University Clinical Trial Center, National Taiwan University Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07436598
- Sponsor's protocol ID
- 202510086RINC
- Status last verified
- Feb 2026
- First submitted
- 22 Feb 2026
- Last updated
- 13 May 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Individual participant data will not be shared.
Publications
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.
PubMed 39444877Colombi 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.
PubMed 36676147Jeong 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.
PubMed 38505088Kang 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.
PubMed 34815369Bolliger 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.
PubMed 12456999Wu 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.
PubMed 8134584Wu 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.
PubMed 11856695Ueda 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.
PubMed 20153214Ueda 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.
PubMed 18485724Fernandez-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.
PubMed 30195604Oswald 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.
PubMed 31709409Park 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.
PubMed 36786699
Study at a glance
From the registry record, last updated by the sponsor on 13 May 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Preoperative Three-Dimensional Virtual Resection Predicts Postoperative Pulmonary Function After Anatomical Resection : A Prospective Longitudinal Study
- ClinicalTrials.gov ID
- NCT07436598
- Acronym / sponsor ID
- 202510086RINC
- 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
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 60 participants (estimated)
- Lead sponsor
- National Taiwan University HospitalUniversity, hospital or other
- Study start
- 7 Apr 2026 (actual)
- Primary completion
- 30 Jun 2027 (estimated)
- Study completion
- 30 Jun 2027 (estimated)
- First posted
- 27 Feb 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
Background: Lung cancer remains a leading cause of cancer mortality. For early-stage NSCLC, VATS anatomical resection (lobectomy or segmentectomy) is the standard treatment. However, the safety of surgery depends heavily on the patient's pulmonary reserve. Traditional prediction methods, such as the segment-counting rule, have shown prediction errors of up to 20-30% because they do not account for regional heterogeneity in lung ventilation.
Study Design: This is a prospective, multi-center, longitudinal cohort study. The study intends to enroll 60 patients eligible for VATS anatomical resection. Patients will be stratified into two groups:
- VATS Segmentectomy Group (n=30)
- VATS Lobectomy Group (n=30)
Methodology:
1.Preoperative Assessment: Within 30 days before surgery, all participants will undergo high-resolution thin-slice (1 mm) chest CT and standard Pulmonary Function Tests (PFT).
2.3-D Virtual Resection: Using Synapse 3-D software, a patient-specific anatomical model will be reconstructed. The investigator will perform a "virtual resection" simulation to mark the planned resection area. The system will calculate the Planned Resected Ventilated Lung Volume Fraction (pRVLVF), defined based on well-aerated lung tissue (CT attenuation -950 to -700 HU).
3.Surgical Procedure: Patients will undergo standard VATS lobectomy or segmentectomy as clinically indicated.
4.Postoperative Follow-up: PFTs will be performed at 3, 6, and 12 months post-surgery. Follow-up CT scans will be performed at 6 and 12 months to assess structural remodeling.
Objectives and Analysis:
Primary Objective: To validate the accuracy of the pRVLVF-based prediction model. The primary endpoint is the Mean Absolute Error (MAE) of the predicted FEV1 at 3 months post-surgery, with a target accuracy of MAE < 180 mL.
Secondary Objectives:
- To assess long-term prediction accuracy at 6 and 12 months.
- To quantify the "Compensation Coefficient" (CC) of the residual lung using Linear Mixed-Effects (LME) models, adjusting for age, BMI, and smoking history.
- To evaluate the impact of postoperative complications on the functional recovery curve.
This study seeks to establish a precise, accessible, and dynamic tool for surgical risk assessment and decision-making in thoracic surgery.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| 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. |
Who may qualify
- Ages
- 18 to 80 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients with non-small cell lung cancer recruited from the thoracic surgery outpatient clinics and inpatient wards of National Taiwan University Hospital.
Key inclusion criteria (3 listed)
- 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.
Key exclusion criteria (5 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Mean Absolute Error (MAE) of Predicted Postoperative FEV1
Time frame: 3 months post-operation
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.
1 secondary outcome
Long-term Prediction Error of FEV1 and FVC
Time frame: 6 months and 12 months post-operation
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 13 May 2026.
- Background: PubMed 39444877Chen 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.
- Background: PubMed 36676147Colombi 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.
- Background: PubMed 38505088Jeong 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.
- Background: PubMed 34815369Kang 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.
- Background: PubMed 12456999Bolliger 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.
- Background: PubMed 8134584Wu 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.
- Background: PubMed 11856695Wu 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.
- Background: PubMed 20153214Ueda 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.
- Background: PubMed 18485724Ueda 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.
- Background: PubMed 30195604Fernandez-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.
- Background: PubMed 31709409Oswald 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.
- Background: PubMed 36786699Park 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.
- Individual participant dataSharing planned: No. Individual participant data will not be shared.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Non-small Cell Lung Cancer and Lung Cancer, run by National Taiwan University Hospital at 1 site in Taiwan.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 13 May 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Taiwan. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Chih-Hsiang Chang, MD (+886-0972653384, thenightdeity@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Mean Absolute Error (MAE) of Predicted Postoperative FEV1 (time frame: 3 months post-operation). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07436598, which also lists 12 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 13 May 2026; AzTrials last refreshed its copy on 28 Oct 2026.