A Multicenter Prospective Study Evaluating the Diagnostic Performance of Femtosecond Laser Label-Free Imaging Combined With Artificial Intelligence for Predicting High-Risk Pathological Features in Lung Cancer

To investigate the diagnostic performance of Femtosecond Laser Label-Free Imaging combined with artificial intelligence for predicting high-risk pathological features in lung cancer

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Key information

Conditions

Lung Cancer (Diagnosis) Disease Lung Diseases Lung Neoplasms Neoplasms Neoplasms by Site Pathologic Processes Pathological Conditions, Signs and Symptoms Respiratory Tract Diseases Respiratory Tract Neoplasms Thoracic Neoplasms

Sex eligibility

All sexes

Study type

Observational

Who can participate

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

Pulmonary nodules detected by clinical imaging with an indication for surgical resection.
Patient agrees to and is planned for pulmonary (partial) resection.
Resected specimens are suitable for FLI imaging.
Written informed consent obtained from the patient or legal representative.
Patients undergoing pulmonary (partial) resection at our hospital during this study.

Exclusion criteria

Insufficient sample.
Specimen with crushing, contamination, or improper preservation, precluding valid imaging as judged by the investigator.
Inability to obtain matched pathology results corresponding to FLI images.
Inability to obtain final pathological diagnosis.
Other conditions deemed by the investigator as unsuitable for study participation.

Treatment and study plan

Femtosecond Laser Label-Free Imaging Combined with Artificial Intelligence.

Diagnostic Test

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.

Primary outcomes

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.

Time frame: through study completion, an average of 2 year

Sponsors and collaborators

Lead sponsor

Shanghai Zhongshan Hospital

Other

Registry information

Important dates

Study start: 2026
Primary completion: 2028
Study completion: 2028
First posted: Sep 2, 2026
Registry last updated: Sep 2, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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