Deep Learning for Preoperative Pulmonary Assessment in Thoracic CT

NCT06477458 | Recruiting

• 1 other identifier: ES-2024-091-02
• Study Type: observational
• Target: 2,000
• Locations: 1 country
• Sites: 1

Brief 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.

Conditions

Elective Thoracic Surgery; Pulmonary Function; Deep Learning

Outcome Measures

Primary Outcomes (1)

• 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).

  2 years

Secondary Outcomes (1)

• Concordance Correlation Coefficient（CCC）

  2 years

Study Arms (2)

Single inspiratory phase cohort

Patients in this cohort undergo single inspiratory phase CT and pulmonary function tests preoperatively.

Other: Single inspiratory phase computed tomography.

Respiratory dual-phase cohort

Patients in this cohort undergo respiratory dual-phase CT and pulmonary function tests preoperatively.

Other: Respiratory dual-phase computed tomography.

Interventions

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.

Single inspiratory phase cohort

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.

Respiratory dual-phase cohort

Eligibility Criteria

• Age: 18 Years - 75 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Elective Thoracic Surgery Patients

You may qualify if:

• (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.

You may not qualify if:

• (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;
• (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.

Sponsors & Collaborators

• The First Affiliated Hospital of Guangzhou Medical University: lead
• GE Healthcare: collaborator

Study Sites (1)

Department of Cardiothoracic Surgery, the First Affiliated Hospital of Guangzhou Medical College

Guangzhou, Guangdong, 510120, China

RECRUITING

Study Officials

• Jianxing He, MD

  Department of Cardiothoracic Surgery, the First Affiliated Hospital of Guangzhou Medical College

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Jianxing He, MD

CONTACT

86-20-83337792; drjianxing.he@gmail.com

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Director

Study Record Dates

• First Submitted: June 21, 2024
• First Posted: June 27, 2024
• Study Start: October 1, 2023
• Primary Completion: September 30, 2024
• Study Completion: December 30, 2024
• Last Updated: June 27, 2024

Record last verified: 2024-06

Locations

CN (1)