Using deep learning to predict aggressive patterns in lung cancer
Positron Emission Tomography/ Computed Tomography (PET/CT) Based Deep Learning Signature for Predicting Aggressive Histological Pattern in Resected Non-small Cell Lung Cancer
This study is testing a new way to use advanced computer technology to see if it can help doctors predict aggressive lung cancer patterns in patients who are having surgery for non-small cell lung cancer.
Quick facts
| Study type | Observational |
|---|---|
| Enrollment | 1500 (estimated) |
| Ages | 20 Years to 75 Years |
| Sex | All |
| Sponsor | Shanghai Pulmonary Hospital, Shanghai, China Academic / other |
| Locations | 3 sites (Zunyi, Guizhou and 2 other locations) |
| Trial ID | NCT05925738 on ClinicalTrials.gov |
What this trial studies
This study evaluates a deep learning signature based on PET/CT imaging to predict aggressive histological patterns in patients with resected non-small cell lung cancer (NSCLC). It involves a multicenter prospective cohort of participants scheduled for surgery due to pulmonary lesions identified on preoperative thin-section CT scans. The goal is to enhance the predictive accuracy of aggressive cancer characteristics, which could inform treatment decisions and patient management.
Who should consider this trial
Good fit: Ideal candidates are adults aged 20-75 who are scheduled for surgery due to confirmed primary NSCLC and have appropriate preoperative imaging.
Not a fit: Patients with multiple lung lesions or those who have received neoadjuvant therapy may not benefit from this study.
Why it matters
Potential benefit: If successful, this approach could lead to more accurate predictions of cancer aggressiveness, allowing for tailored treatment strategies for patients with NSCLC.
How similar studies have performed: While the use of deep learning in medical imaging is gaining traction, this specific application in predicting aggressive patterns in NSCLC is relatively novel and has not been extensively tested in prior studies.
Eligibility criteria
Show full inclusion / exclusion criteria
Inclusion Criteria: (1) Participants scheduled for surgery for radiological finding of pulmonary lesions from the preoperative thin-section CT scans; (2) Pathological confirmation of primary NSCLC; (3) Age ranging from 20-75 years; (4) Obtained written informed consent. Exclusion Criteria: (1) Multiple lung lesions; (2) Poor quality of PET-CT images; (3) Participants with incomplete clinical information; (4) Participants who have received neoadjuvant therapy.
Where this trial is running
Zunyi, Guizhou and 2 other locations
- Affiliated Hospital of Zunyi Medical University — Zunyi, Guizhou, China (Recruiting)
- The First Affiliated Hospital of Nanchang University — Nanchang, Jiangxi, China (Recruiting)
- Ningbo HwaMei Hospital — Ningbo, Zhejiang, China (Recruiting)
How to participate
- Review the eligibility criteria above with your treating physician.
- Visit the official trial page on ClinicalTrials.gov for the most current contact information and recruitment status.
- Contact the listed study coordinator or principal investigator to request pre-screening. Pre-screening is free and never obligates you to enroll.