Clinical trial NCT06477458: Deep Learning for Preoperative Pulmonary Assessment in Thoracic CT
Official title: Application of Deep Learning in CT Imaging of Elective Thoracic Surgery Patients: Assessing Preoperative Abnormal Pulmonary Function
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Elective Thoracic Surgery
Pulmonary Function
Deep Learning
Lead sponsor: The First Affiliated Hospital of Guangzhou Medical University
Sponsor class: OTHER
Collaborators: name: GE Healthcare
First posted: 2024-06-27
Enrollment: 2000
Sex: ALL
Minimum age (years): 18
Maximum age (years): 75
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: OTHER
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Elective Thoracic Surgery Patients

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.

Eligibility criteria: 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.

Primary outcomes:
measure: Mean Absolute Error（MAE）
timeFrame: 2 years
Secondary outcomes:
measure: Concordance Correlation Coefficient（CCC）
timeFrame: 2 years
Other outcomes:
Locations:
city: Guangzhou
country: China
facility: Department of Cardiothoracic Surgery, the First Affiliated Hospital of Guangzhou Medical College
state: Guangdong
Study officials:
affiliation: Department of Cardiothoracic Surgery, the First Affiliated Hospital of Guangzhou Medical College
name: Jianxing He, MD
role: PRINCIPAL_INVESTIGATOR
References: