Lung Cancer (Diagnosis)
Conditions
Keywords
Lung cancer, FLI
Brief summary
To investigate the diagnostic performance of Femtosecond Laser Label-Free Imaging combined with artificial intelligence for predicting high-risk pathological features in lung cancer
Interventions
DIAGNOSTIC_TESTFemtosecond Laser Label-Free Imaging Combined with Artificial Intelligence.
Femtosecond Laser Label-Free Imaging Combined with Artificial Intelligence. The diagnostic results will be blinded to both the physicians (including all clinicians involved in the patient's diagnostic and treatment process, such as surgeons and pathologists) and the patient, and the diagnostic results will NOT affect the original treatment plan.
Sponsors
Shanghai Zhongshan Hospital
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Inclusion criteria
1. Pulmonary nodules detected by clinical imaging with an indication for surgical resection. 2. Patient agrees to and is planned for pulmonary (partial) resection. 3. Resected specimens are suitable for FLI imaging. 4. Written informed consent obtained from the patient or legal representative. 5. Patients undergoing pulmonary (partial) resection at our hospital during this study.
Exclusion criteria
1. Insufficient sample. 2. Specimen with crushing, contamination, or improper preservation, precluding valid imaging as judged by the investigator. 3. Inability to obtain matched pathology results corresponding to FLI images. 4. Inability to obtain final pathological diagnosis. 5. Other conditions deemed by the investigator as unsuitable for study participation.
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| The primary endpoint is the accuracy of the FLI combined with the AI model in predicting regional lymph node metastasis status in lung cancer, evaluated using postoperative pathology results as the reference standard. | through study completion, an average of 2 year |
Outcome results
None listed