Deep Learning for Preoperative Pulmonary Assessment in Thoracic CT

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

Get a concise snapshot of the trial, including recruitment status, study phase, enrollment targets, and key timeline milestones.

Recruitment Status

RECRUITING

Total Enrollment

2000 participants

Study Classification

OBSERVATIONAL

Study Start Date

2023-10-01

Study Completion Date

2024-12-30

Brief Summary

Review the sponsor-provided synopsis that highlights what the study is about and why it is being conducted.

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.

Related Clinical Trials

Explore similar clinical trials based on study characteristics and research focus.

Predicting Symptom Trajectories After Thoracoscopic Lung Cancer Surgery Using an Interpretable Machine Learning Model

NCT06771947

Lung Cancer

NOT_YET_RECRUITING

Postoperative Pulmonary Function Assessment Based on Deep Learning Study

NCT07256457

Lung Cancer (NSCLC)

NOT_YET_RECRUITING NA

Potential of Deep Learning in Assessing Pneumoconiosis Depicted on Digital Chest Radiography

NCT04963348

Pneumoconiosis

COMPLETED

CT-based Radiomic Algorithm for Assisting Surgery Decision and Predicting Immunotherapy Response of NSCLC

NCT04452058

Predictive Cancer Model Lung Cancer Preinvasive Adenocarcinoma

UNKNOWN

Deep Learning Model Predicts Pathological Complete Response of Lung Cancer Following Neoadjuvant Immunochemotherapy

NCT06285058

Deep Learning Model Pathological Complete Response Non-small Cell Lung Cancer +1 more

NOT_YET_RECRUITING

Detailed Description

Dive into the extended narrative that explains the scientific background, objectives, and procedures in greater depth.

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

See the medical conditions and disease areas that this research is targeting or investigating.

Elective Thoracic Surgery Pulmonary Function Deep Learning

Study Design

Understand how the trial is structured, including allocation methods, masking strategies, primary purpose, and other design elements.

Observational Model Type

OTHER

Study Time Perspective

PROSPECTIVE

Study Groups

Review each arm or cohort in the study, along with the interventions and objectives associated with them.

Single inspiratory phase cohort

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

Single inspiratory phase computed tomography.

Intervention Type 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 cohort

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

Respiratory dual-phase computed tomography.

Intervention Type 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.

Interventions

Learn about the drugs, procedures, or behavioral strategies being tested and how they are applied within this trial.

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.

Intervention Type 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.

Intervention Type OTHER

Eligibility Criteria

Check the participation requirements, including inclusion and exclusion rules, age limits, and whether healthy volunteers are accepted.

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.

Minimum Eligible Age

18 Years

Maximum Eligible Age

75 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No