NCT07794449

Brief Summary

To investigate the diagnostic efficacy of Femtosecond Laser Label-Free Imaging combined with artificial intelligence-assisted diagnosis for rapid diagnosis of lung cancer.

Trial Health

65
Monitor

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Enrollment
335

participants targeted

Target at P75+ for all trials

Timeline
23mo left

Started Sep 2026

Status
not yet recruiting

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

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Study Timeline

Key milestones and dates

Study Progress5%
Sep 2026Aug 2028

First Submitted

Initial submission to the registry

August 24, 2026

Completed
7 days until next milestone

First Posted

Study publicly available on registry

August 31, 2026

Completed
1 day until next milestone

Study Start

First participant enrolled

September 1, 2026

Completed
2 years until next milestone

Primary Completion

Last participant's last visit for primary outcome

August 31, 2028

Expected
Same day until next milestone

Study Completion

Last participant's last visit for all outcomes

August 31, 2028

Last Updated

August 31, 2026

Status Verified

August 1, 2026

Enrollment Period

2 years

First QC Date

August 24, 2026

Last Update Submit

August 28, 2026

Conditions

Lung Cancer (Diagnosis)

Keywords

Lung CancerFLI

Outcome Measures

Primary Outcomes (1)

  • The accuracy of FLI combined with AI-assisted diagnosis in differentiating benign from malignant lung surgical specimens.

    through study completion, an average of 2 year

Interventions

Femtosecond Laser Label-Free Imaging Combined with Artificial IntelligenceDIAGNOSTIC_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.

Eligibility Criteria

Sexall
Age GroupsChild (0-17), Adult (18-64), Older Adult (65+)
Sampling MethodNon-Probability Sample
Study Population

Patients undergoing pulmonary resection, with specimens suitable for FLI diagnosis.

You may qualify if:

  • 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 during this study.

You may not qualify if:

  • 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.

Contact the study team to confirm eligibility.

Sponsors & Collaborators

MeSH Terms

Conditions

Lung NeoplasmsDisease

Interventions

Artificial Intelligence

Condition Hierarchy (Ancestors)

Respiratory Tract NeoplasmsThoracic NeoplasmsNeoplasms by SiteNeoplasmsLung DiseasesRespiratory Tract DiseasesPathologic ProcessesPathological Conditions, Signs and Symptoms

Intervention Hierarchy (Ancestors)

AlgorithmsMathematical Concepts

Study Design

Study Type
observational
Observational Model
COHORT
Time Perspective
PROSPECTIVE
Sponsor Type
OTHER
Responsible Party
SPONSOR

Study Record Dates

First Submitted

August 24, 2026

First Posted

August 31, 2026

Study Start

September 1, 2026

Primary Completion (Estimated)

August 31, 2028

Study Completion (Estimated)

August 31, 2028

Last Updated

August 31, 2026

Record last verified: 2026-08