Deep Learning for Preoperative Pulmonary Assessment in Thoracic CT

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.

Study Overview

• Status: Recruiting
• Conditions: Pulmonary Function; Deep Learning; Elective Thoracic Surgery
• Intervention / Treatment: Other: Single inspiratory phase computed tomography.; Other: Respiratory dual-phase computed tomography.

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 Type: Observational
• Enrollment (Estimated): 2000

Contacts and Locations

• Study Contact: Name: Jianxing He, MD; Phone Number: 86-20-83337792; Email: drjianxing.he@gmail.com

Study Locations

• China
  • Guangdong
    • Guangzhou, Guangdong, China, 510120
      • Recruiting
      • Department of Cardiothoracic Surgery, the First Affiliated Hospital of Guangzhou Medical College
      • Contact: Jianxing He, MD; Phone Number: 86-20-83337792; Email: drjianxing.he@gmail.com

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

Elective Thoracic Surgery Patients

Description

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 Plan

How is the study designed?

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Single inspiratory phase cohort; Patients in this cohort undergo single inspiratory phase CT and pulmonary function tests preoperatively.	Other: 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.
Respiratory dual-phase cohort; Patients in this cohort undergo respiratory dual-phase CT and pulmonary function tests preoperatively.	Other: 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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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 Outcome Measures

Outcome Measure	Measure Description	Time Frame
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).	2 years

Collaborators and Investigators

• Sponsor: The First Affiliated Hospital of Guangzhou Medical University
• Collaborators: GE Healthcare
• Investigators: Principal Investigator: Jianxing He, MD, Department of Cardiothoracic Surgery, the First Affiliated Hospital of Guangzhou Medical College

Study record dates

Study Major Dates

• Study Start (Actual): October 1, 2023
• Primary Completion (Estimated): September 30, 2024
• Study Completion (Estimated): December 30, 2024

Study Registration Dates

• First Submitted: June 21, 2024
• First Submitted That Met QC Criteria: June 26, 2024
• First Posted (Actual): June 27, 2024

Study Record Updates

• Last Update Posted (Actual): June 27, 2024
• Last Update Submitted That Met QC Criteria: June 26, 2024
• Last Verified: June 1, 2024

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Cardiovascular Diseases; Vascular Diseases; Aneurysm; Aortic Diseases; Dissection, Blood Vessel; Acute Aortic Syndrome; Aortic Dissection
• Other Study ID Numbers: ES-2024-091-02

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No