Deep Learning for Preoperative Pulmonary Assessment in Thoracic CT

Recruiting

• Conditions: Elective Thoracic Surgery; Pulmonary Function; Deep Learning

• Registration Number: NCT06477458
• Lead Sponsor: The First Affiliated Hospital of Guangzhou Medical University

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.

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 Timeline

• Study Start: 2023-10-01
• Status Verified: 2024-06
• Study First Submitted: 2024-06-21
• Study First Submitted QC: 2024-06-26
• Last Update Submitted: 2024-06-26
• Study First Posted: 2024-06-27
• Last Update Posted: 2024-06-27
• Primary Completion: 2024-09-30
• Study Completion: 2024-12-30

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 2000

Inclusion Criteria

• (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.

Exclusion Criteria

• (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.

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Mean Absolute Error（MAE）	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).

Secondary Outcome Measures

Name	Time	Method
Concordance Correlation Coefficient（CCC）	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).

Trial Locations

Locations (1)

Department of Cardiothoracic Surgery, the First Affiliated Hospital of Guangzhou Medical College; 🇨🇳 Guangzhou, Guangdong, China