Elective Thoracic SurgeryNCT06477458
Deep Learning for Preoperative Pulmonary Assessment in Thoracic CT
An observational study of Elective Thoracic Surgery and Pulmonary Function, run by The First Affiliated Hospital of Guangzhou Medical University at 1 site in China.
Trial summary
The trial was designed as a single-centre, non-interventional prospective observational study to utilize deep learning technology combined with computed tomography (CT) images to precisely predict the pulmonary function indicators of thoracic surgery preoperative patients. Read more about this study
- Who is it for?
- Ages 18 to 75 years
- Conditions the study is for
- Elective Thoracic Surgery, Pulmonary Function, Deep Learning
- What is being tested?
- Single inspiratory phase computed tomography. and Respiratory dual-phase computed tomography.
- How many people
- 2,000 participants
- Where
- 1 siteChina
- Study dates
- 1 Oct 2023to 30 Dec 2024
- Phase
- Observational
- Are there results?
- None posted in the registry
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 75 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Elective Thoracic Surgery Patients
Who may be able to join
- (1) Signing of the informed consent form;
- (2) Male or female, aged 18-75 years;
- (3) Undergoing elective thoracic surgery;
- (4) Good preoperative pulmonary function cooperation and complete reporting;
- (5) Preoperative chest single/dual phase CT scans without significant artefacts and with complete imaging;
- (6) The interval between preoperative pulmonary function and single/dual phase CT scans does not exceed one month.
Who cannot join
- (1) Poor preoperative pulmonary function cooperation or missing reports;
- (2) Preoperative chest single/dual phase CT scans exhibit significant artefacts or image omission;
- (3) The interval between preoperative pulmonary function and single/dual phase CT scans exceeds one month;
- (4) Complication with severe respiratory disorders (such as lung transplantation, pneumothorax, giant bullae, etc.);
- (5) Coexisting with other severe functional impairments;
- (6) Patients with obstructive lesions such as airway or esophageal stenosis;
Show all 9Show fewer
- (7) Height beyond the predicted equation range (Female < 1.45m; Male < 1.55m);
- (8) Medication use before pulmonary function testing that does not meet the cessation guidelines;
- (9) Pulmonary function report quality graded D-F.
Trial locations and contacts
1 site in 1 country.
China1 site, 1 recruiting
- Recruiting
Department of Cardiothoracic Surgery, the First Affiliated Hospital of Guangzhou Medical College
Guangzhou, Guangdong, 510120
Jianxing He, MD, 86-20-83337792, drjianxing.he@gmail.com
About this study
Official title: Application of Deep Learning in CT Imaging of Elective Thoracic Surgery Patients: Assessing Preoperative Abnormal Pulmonary Function
The trial was designed as a single-centre, non-interventional prospective observational study to utilize deep learning technology combined with computed tomography (CT) images to precisely predict the pulmonary function indicators of thoracic surgery preoperative patients.
Read the detailed description
Preoperative pulmonary function tests are crucial in assessing perioperative complications or mortality risks and providing decision support for thoracic surgery. However, traditional pulmonary function assessment methods have significant limitations, including long testing durations, difficulties in patient cooperation, high false-negative rates, and numerous contraindications. Thus, our study optimized the final model based on 1500 single inspiratory phase CTs by transferring model parameters trained on 500 dual-phase respiratory CTs, enhancing its predictive capabilities for pulmonary function. This adjustment suits real-world application demands, offering more convenient, comprehensive, and personalized preoperative pulmonary function assessment support. Our study optimized the final model based on 1500 single inspiratory phase CTs by transferring model parameters trained on 500 dual-phase respiratory CTs, enhancing its predictive capabilities for pulmonary function. This adjustment suits real-world application demands, offering more convenient, comprehensive, and personalized preoperative pulmonary function assessment support.
- Conditions
- Elective Thoracic Surgery, Pulmonary Function, Deep Learning
- Being tested
- Single inspiratory phase computed tomography., Respiratory dual-phase computed tomography.
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Single inspiratory phase computed tomography.Other
Utilizing deep learning technology in conjunction with single inspiratory phase computed tomography images to accurately predict the pulmonary function indicators of preoperative thoracic surgery patients.
Respiratory dual-phase computed tomography.Other
Utilizing deep learning technology in conjunction with respiratory dual-phase computed tomography images to accurately predict the pulmonary function indicators of preoperative thoracic surgery patients.
| Study group | Role | What they receive |
|---|---|---|
| Single inspiratory phase cohort | Patients in this cohort undergo single inspiratory phase CT and pulmonary function tests preoperatively. Receives: Other: Single inspiratory phase computed tomography. | |
| Respiratory dual-phase cohort | Patients in this cohort undergo respiratory dual-phase CT and pulmonary function tests preoperatively. Receives: Other: Respiratory dual-phase computed tomography. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Mean Absolute Error（MAE）
Used to assess the discrepancy between pulmonary function predictions made by the deep learning algorithm and actual results obtained from pulmonary function tests (measured with a spirometer).
Measured: 2 years
Secondary outcomes (1)
Concordance Correlation Coefficient（CCC）
Used to assess the discrepancy between pulmonary function predictions made by the deep learning algorithm and actual results obtained from pulmonary function tests (measured with a spirometer).
Measured: 2 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted27 Jun 2024
- Study start1 Oct 2023
- Primary completion30 Sep 2024 (estimated)
- Study completion30 Dec 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Elective Thoracic SurgeryStudies of the same condition, open and under way.
- 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
- Other
- Time perspective
- Prospective
- Enrolment
- 2,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- The First Affiliated Hospital of Guangzhou Medical University
- Sponsor type
- University, hospital or other
- Collaborators
- GE Healthcare
- Responsible party
- Principal investigator, Jianxing He, The First Affiliated Hospital of Guangzhou Medical University
- Investigators
- Jianxing He, MD, Principal investigator, Department of Cardiothoracic Surgery, the First Affiliated Hospital of Guangzhou Medical College
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06477458
- Sponsor's protocol ID
- ES-2024-091-02
- Status last verified
- Jun 2024
- First submitted
- 21 Jun 2024
- Last updated
- 27 Jun 2024
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 27 Jun 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Application of Deep Learning in CT Imaging of Elective Thoracic Surgery Patients: Assessing Preoperative Abnormal Pulmonary Function
- ClinicalTrials.gov ID
- NCT06477458
- Acronym / sponsor ID
- ES-2024-091-02
- Conditions
- Elective Thoracic Surgery, Pulmonary Function, Deep Learning
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 2,000 participants (estimated)
- Lead sponsor
- The First Affiliated Hospital of Guangzhou Medical UniversityUniversity, hospital or other
- Collaborators
- GE Healthcare
- Study start
- 1 Oct 2023 (actual)
- Primary completion
- 30 Sep 2024 (estimated)
- Study completion
- 30 Dec 2024 (estimated)
- First posted
- 27 Jun 2024 (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.
The trial was designed as a single-centre, non-interventional prospective observational study to utilize deep learning technology combined with computed tomography (CT) images to precisely predict the pulmonary function indicators of thoracic surgery preoperative patients.
Detailed description
Preoperative pulmonary function tests are crucial in assessing perioperative complications or mortality risks and providing decision support for thoracic surgery. However, traditional pulmonary function assessment methods have significant limitations, including long testing durations, difficulties in patient cooperation, high false-negative rates, and numerous contraindications. Thus, our study optimized the final model based on 1500 single inspiratory phase CTs by transferring model parameters trained on 500 dual-phase respiratory CTs, enhancing its predictive capabilities for pulmonary function. This adjustment suits real-world application demands, offering more convenient, comprehensive, and personalized preoperative pulmonary function assessment support. Our study optimized the final model based on 1500 single inspiratory phase CTs by transferring model parameters trained on 500 dual-phase respiratory CTs, enhancing its predictive capabilities for pulmonary function. This adjustment suits real-world application demands, offering more convenient, comprehensive, and personalized preoperative pulmonary function assessment support.
Study design
- Observational model
- Other
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Single inspiratory phase cohort | – | Other: Single inspiratory phase computed tomography.Patients in this cohort undergo single inspiratory phase CT and pulmonary function tests preoperatively. |
| Respiratory dual-phase cohort | – | Other: Respiratory dual-phase computed tomography.Patients in this cohort undergo respiratory dual-phase CT and pulmonary function tests preoperatively. |
Who may qualify
- Ages
- 18 to 75 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Elective Thoracic Surgery Patients
Key inclusion criteria (6 listed)
- (1) Signing of the informed consent form;
- (2) Male or female, aged 18-75 years;
- (3) Undergoing elective thoracic surgery;
- (4) Good preoperative pulmonary function cooperation and complete reporting;
- (5) Preoperative chest single/dual phase CT scans without significant artefacts and with complete imaging;
Key exclusion criteria (9 listed)
- (1) Poor preoperative pulmonary function cooperation or missing reports;
- (2) Preoperative chest single/dual phase CT scans exhibit significant artefacts or image omission;
- (3) The interval between preoperative pulmonary function and single/dual phase CT scans exceeds one month;
- (4) Complication with severe respiratory disorders (such as lung transplantation, pneumothorax, giant bullae, etc.);
- (5) Coexisting with other severe functional impairments;
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Single inspiratory phase computed tomography.Utilizing deep learning technology in conjunction with single inspiratory phase computed tomography images to accurately predict the pulmonary function indicators of preoperative thoracic surgery patients. | Other | Single inspiratory phase cohort |
| Respiratory dual-phase computed tomography.Utilizing deep learning technology in conjunction with respiratory dual-phase computed tomography images to accurately predict the pulmonary function indicators of preoperative thoracic surgery patients. | Other | Respiratory dual-phase cohort |
Primary outcome
Mean Absolute Error（MAE）
Time frame: 2 years
Used to assess the discrepancy between pulmonary function predictions made by the deep learning algorithm and actual results obtained from pulmonary function tests (measured with a spirometer).
1 secondary outcome
Concordance Correlation Coefficient（CCC）
Time frame: 2 years
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 27 Jun 2024.
Related trials for Elective Thoracic Surgery
Recruiting
Sodium Oxybate for Postoperative Sleep Disturbance After Thoracic Surgery
Thoracic Surgical Procedures
Not yet recruiting
Comparison of Paravertebral Block and Deep Parasternal Intercostal Plane Block in Cardiac Surgery
Postoperative Pain, Cardiac Surgery
Not yet recruiting
Impact of Total Coronary Revascularization Via Left Anterior Thoracotomy (TCRAT) vs. Robotic-Assisted Harvesting of LIMA (ITcrats)
Minimally Invasive Coronary Revascularization Surgery, Elective Surgery
Recruiting
C-MAC Blade D Video Vs Macintosh Laryngoscope for Double Lumen Tube Insertion
Video Laryngoscope, Double Lumen Tube Intubation
RecruitingPhase 4
Opioid Free Anaesthesia-Analgesia Strategy on Surgical Stress and Immunomodulation in Elective VATS-Lobectomy for NSCLC
Systemic Inflammatory Response Syndrome, Postoperative Pain, Acute
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. This study is not recruiting at the moment.
Jianxing He, MD principal investigator
Department of Cardiothoracic Surgery, the First Affiliated Hospital of Guangzhou Medical College
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 Elective Thoracic Surgery and Pulmonary Function, run by The First Affiliated Hospital of Guangzhou Medical University at 1 site in China.
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 6 inclusion and 9 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: status not confirmed, as of the sponsor's last update on 27 Jun 2024. 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 China. 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?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Jianxing He, MD (86-20-83337792, drjianxing.he@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） (time frame: 2 years). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
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 27 Jun 2024.