Using deep learning to diagnose lung adenocarcinoma grading
Whole Slide Image Based Deep Learning for Diagnosing the International Association for the Study of Lung Cancer Proposed Grading System of Lung Adenocarcinoma
Shanghai Pulmonary Hospital, Shanghai, China · NCT05925764
This study is testing a new deep learning tool to see if it can help doctors better diagnose and grade lung adenocarcinoma in patients who have had surgery.
Quick facts
| Study type | Observational |
|---|---|
| Enrollment | 200 (estimated) |
| Ages | 18 Years to 85 Years |
| Sex | All |
| Sponsor | Shanghai Pulmonary Hospital, Shanghai, China (other) |
| Locations | 3 sites (Zunyi, Guizhou and 2 other locations) |
| Trial ID | NCT05925764 on ClinicalTrials.gov |
What this trial studies
This study evaluates a deep learning model that analyzes whole slide images to diagnose the IASLC grading system in patients with resected lung adenocarcinoma. It involves a multicenter prospective cohort approach, where data is collected from various hospitals to assess the model's performance. The goal is to improve diagnostic accuracy and efficiency in grading lung adenocarcinoma, which is crucial for treatment planning and prognosis.
Who should consider this trial
Good fit: Ideal candidates are adults aged 18-85 with a pathological confirmation of primary lung adenocarcinoma after surgery.
Not a fit: Patients with multiple lung lesions, poor quality whole slide images, mucinous adenocarcinomas, or those who have received neoadjuvant therapy may not benefit from this study.
Why it matters
Potential benefit: If successful, this could lead to more accurate and efficient diagnoses of lung adenocarcinoma, improving patient outcomes.
How similar studies have performed: Other studies utilizing deep learning for cancer diagnosis have shown promising results, indicating potential success for this approach.
Eligibility criteria
Show full inclusion / exclusion criteria
Inclusion Criteria: 1. Age ranging from 18-85 years old; 2. Pathological confirmation of primary lung adenocarcinoma after surgery; 3. Obtained written informed consent. Exclusion Criteria: 1. Multiple lung lesions; 2. Poor quality of whole slide images; 3. Mucinous adenocarcinomas and variants; 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.
Conditions: Lung Adenocarcinoma, Whole Slide Image, IASLC Grading System, Artificial Intelligence